Annotations et al

On-slide annotations

Do you recognize the above image? It’s a rendering of the mitotic figures and algorithmic classifications  as determined by Bertram at al in their 2019 paper.

Digital pathology and virtual microscopy are concerned with slides (duh), but those are only part of the equation. External data as well as on-slide annotations are an integral part of the package, and any self-respecting software in this space supports various flavors of annotations. The various components in the Pathomation platform are no exception.

First some terminology: we generally refer to on-slide annotations that are geometric shapes and figures presented on top of virtual slide pixels in order to distinguish from (text-based) slide meta-data. The latter is presented as data attached to a slide, but not particularly or directly associated with a specific region on the slide. We discussed how Pathomation handles various flavors of slide metadata in an earlier blog post.

Creating on-slide annotations in

With the forthcoming release of 2.0, you can create on-slide annotations interactively. offers a ribbon tab for this purpose. The first group of buttons lets you control what exactly is visible: the annotations themselves can be toggled, and you can choose whether to include the annotations labels or not.

The Style group in the ribbon is used to change the presentation of annotations. Annotations can have an edge color, fill color, which can be set independently from each other. Filled shapes can have a transparency attribute, too.

Each annotation can be associated with a class and a description attribute. An example would be a set of polygon shapes that each indicate necrosis and therefore have the “necrosis” class attribute, while individual shapes would be referred as a “necrosis-region-1”, “necrosis-region-2” etcetera.

Once you’ve made the annotations, you can follow-up on them through the annotations panel. Here, annotations are grouped per class. You can also use this panel to filter annotations per user.’s ribbon can be customized. This means that custom annotations are possible, too. You can use this feature to implement protocols.

In the example below, we’ve used XML to define pre-sets for pathologists to indicate various types of TLS regions:

Behind the scenes sits PMA.core

Let’s spend some time on how it all works behind the scenes.

When you make annotations in, you interact with a PMA.UI viewport. Once you decide to save you annotations, it’s the PMA.UI viewport that sends your annotations to PMA.core, where they are saved in the back-end database.

The format in which annotations are saved is Well-Known Text (WKT).

PMA.core has API calls to work with annotations. PMA.UI makes heavy use of these.

External annotations

Because we totally understand that may not be your first environment of choice to make your on-slide annotations with (though we think it’s a really good one!), PMA.core supports various other types of on-slide annotations, too.

We distinguish between “native” and “third-party” annotations.

Native annotations are shapes and forms included in the original vendor’s file format. Several vendors provide the option of making on-slide annotations in their own native viewers. Examples include 3DHistech and Aperio. If you have a 3Dhistech MRXS file that has overlaying annotations created by Case Center or the Pannoramic viewer, PMA.core will render them accordingly. The same goes for Aperio SVS files: just make sure you put the .xml file from ImageScope next to the corresponding .svs file.

Other vendors support annotations, too. If you find yourself with a vendor-specific annotation file format, do tell us, and we’ll add it to our next version of PMA.core.

Third-party annotations are another kind than “native” annotations. Like PMA.core’s own (WKT-encoded) annotations, these are created in software that is not coming from the vendor. Typically we’re talking about image analysis software. The three big names are out there are Definiens, Visiopharm, and Indica Labs HALO. Each of these environments is supported.

We’re not technically hindered to only support these abovementioned flavors of image analysis. So if you have a different environment from the one listed, let us know.

Transient annotations through

Two products of Pathomation support real-time conferencing: and PMA.control. While in a conference, it is possible for participants to make live annotations that only exist for the duration of the conference.

Transient annotations in and PMA.control (through a third component referred to as can perhaps best be compared to the annotation toolbar that appears while giving a Powerpoint presentation: several tools are made available to temporarily highlight specific features, but these don’t become part of the original presentation.

Bringing it all together

As the “middleware for digital pathology and virtual microscopy” company, we take our job seriously. Therefore, apart from managing slides, we also allow our tile server PMA.core to be used to organize graphical annotations. The Pathomation platform offers many ways to organize

Pathomation can ingest both native and third-party annotations. The difference is that we consider native annotations to be annotations made with the original manufacturer’s (viewer) software, while third-party annotations are annotations created by independent vendors like Indica Labs or Visiopharm.

We also allow people to make annotations on top of slides using only components within the Pathomation platform. You don’t need external software to get started with annotating your slides; you can use our own, or even couple OpenCV output back to PMA.core through our API.

There’s more to say about annotations. In upcoming articles, we plan to show you how you can manage heterogenous annotation data from many sources, as well as how to use the back-end API directly to feed annotations back to PMA.core.

Want to see us work out a specific use case for your annotation workflows? Let us know.

Research publications in digital pathology

At Pathomation, we’re big fans of XKCD. And Randall Munroe seems to have hit a particular sore point with scientists recently, if the follow-up article in the Atlantic is to be believed.

We’re not endorsing the Atlantic here. It’s easy to critique the team if you’re a bystander and not actually playing the game.

All the same, we conducted our own small study and found that nobody had created an adaptation for the field of digital pathology yet. So we made our own.

The following data is therefore completely made up. We post it in the public domain, free for all to share, and hope that it will benefit humanity as a whole:

Read the original version of the cartoon at

Fingerprinting applications

A naïve way to detect duplicate data

We often copy data for a variety of reasons. While testing a new program, we can copy data any number of times so the software is able to work on a dataset instead of a single datapoint. Temporary data unfortunately is all too often forgotten about, lingers around, and unnecessarily clutters our hard disks.

There are a number of ways to solve this problem. With Python, we can create a script that retrieves all slides, inspects the file size of each slide, and reports which two slides have the same size.

Using our PMA.python SDK, the code looks like this:

from pma_python import core
all_slides = core.get_slides("C:/", recursive = True)
def get_slide_size(slide):
    info = core.get_slide_info(slide)
    return info["PhysicalSize"]

all_sizes = {}
for slide in all_slides:
    fp = get_slide_size(slide)
    if "d" in fp.keys():
        fp = fp["d"]
        # print (slide, fp)
        if not fp in all_sizes.keys():
            all_sizes[fp] = []
for (k, v) in all_sizes.items():
    if len(v) > 1:
        fn = v[0]
        if not (".png" in fn.lower() or ".jpg" in fn.lower()):
            print(k, len(v))

But wait, what if two slides are the same size? Whole slide images are typically 100s of megabytes in size. Based on this characteristic, you could assume that it’s unlikely two slides result in identical file sizes. But macroscopic images are important in pathology, too, and then we’re talking about file sizes that are only a couple of megabytes in size at most. Now think of a scenario where tens of 1000s of cases are treated annually, with multiple macroscopic photos being taken of each resection piece… Suddenly the chances of two files having the same size becomes plausible.

There’s a second, albeit somewhat more hypothetical reason, why slide size is a poor indicator here. Wsi data could be stored in a container format, and the container format can have certain limitations. We observed e.g. that our PMA.start installation package has now not changed in size for the last 7 releases or so. But of course our code did change. So, empirically, file size is not a good discriminant for executable files. We feel therefore that we cannot assume that this would be the case for image file formats. Since re-scans are a specific concern with microscopy and WSI data, something better is needed than just the filesize.

Introducing fingerprinting

We can think of a way to unambiguously distinguish slides from one another by combining a number of characteristics into a digital fingerprint. These would include:

  1. Filesize (we didn’t say this was a bad one; just an insufficient one)
  2. Pixel size
  3. Pixels per micron
  4. Number of channels
  5. Number of z-stack layers

If we had infinite real-time computing power, we can think of more:

For practically, we define a slide’s fingerprint in the Pathomation platform as a combined hash of physical file size, as well as most of the parameters returned through the GetImageInfo method.

We also consider this fingerprint method to be essential, and so for stability, it is incorporated at the level of PMA.core (PMA.start) itself rather than at SDK level, so it can be transferred across programming boundaries. A fingerprint for slide [foo] requested through yields the same results as when requested through PMA.php or PMA.python.

Slide integrity

The fingerprint method is a good way to confirm the integrity of a slide itself. When a file is not what it pretends to be, the fingerprint cannot be calculated, and an error follows.

Note that the above would not be possible if we stuck to conventional CRC-like checks, since those don’t take into account the nature of a slide. Of course, you can do a CRC check on any file regardless of whether it actually is a slide or not.


We recently introduced PMA.transfer. Have you ever been frustrated by people sending you individual VSI or MRXS slides without anything else? Did you ever feel uneasy about just having transferred a gazillion number of bytes half across the world, without any reassurance of whether it actually worked? Then you should definitely have a look at PMA.transfer. It’s like FileZilla, but for slides. SlideZilla.

PMA.transfer uses fingerprinting to ensure data integrity in between transfers. Whether you’re moving slides from and to PMA.start, PMA.core, or My Pathomation, the same fingerprint calculation algorithm is used to compute a slide’s unique signature. This means that PMA.transfer can obtain the fingerprint of a source and a target instance and simply compare one with another to see that they’re identical.

Another application is found in our upcoming product. Of course, the actual fingerprint of a slide is shown in the slide info panel. But a string like “” is not saying a whole lot and is for purely informative purposes at best. can also be used to create annotations on slides. When you make an annotation, it is stored in our back end with a reference to the original slide, as well as the slide’s fingerprint.

This serves two purposes:

  • After moving a slide to a new folder path or even physical location, you can still retrieve its annotations.
  • When you have two identical copies of slides, each annotated separately, you can use fingerprinting to combine the annotations from both in a single view.

The possibility to combine annotations from identical slides stored in different locations in a single view offers opportunities for blinded studies and validation exercises. Inter- and even intra-observer variability can be measured this way, too.

Retrieving annotations by fingerprint is not available by default; you need to invoke this with an explicit button in the ribbon. It’s a performance thing.

Last but not least, fingerprinted annotations can be used to keep track of annotations during migration processes. As your applications for digital pathology increase, you will occasionally restructure your folder structures, or perhaps move to an entire new storage device altogether.

Finding duplicates

Back to our original question: imagine that you’ve been managing a whole slide repository for a while, and as careful as you’ve been, you suspect that you now have ended up with a number of copies of a variety of slides in different locations. You know: you copy a slide to test something, pinky-promise yourself that you’ll remove the slide again afterwards, that for real you’re really not going to forget this time… and then… you forget about it.

Thanks to the fingerprinting method and a few lines of Python, it is easy to trace duplicates however.

Here’s the basic code to build a dictionary that has all the possible fingerprints as keys. Each entry then contains a list that specifies where the exact copies that share a particular fingerprint:

slides_by_fingerprint = {}
for slide in slides:
    fp = core.get_fingerprint(slide)
    if not fp in slides_by_fingerprint:
        slides_by_fingerprint[fp] = [slide]

When a slide is unique, then the list of the dictionary will only have one entry. Alternatively, it can have two or more entries. So the duplicated slides are detected and flagged as follows:

for fprint in slides_by_fingerprint:
    if len(slides_by_fingerprint[fprint]) > 1:
        print(slides_by_fingerprint[fprint][0], "is copied", len(slides_by_fingerprint[fprint]), "times")

If you want, you can further automate the pruning and the deletion of these duplicates. Sometimes it’s easy; sometimes it’s not. You need to make sure that you have the original copy in its intended place. And in some cases, you may actually want to keep at least a second copy of a slide around, as one may be transient in a clinical setting, whereas its copy may have just been added to a reference repository to teach students and staff.

Coming full circle

Fingerprinting serves a triple function:

  • Detect whether a dataset is a real slide or not
  • Guard data integrity when transferring from one medium to another
  • Trace slides and associated content through a complex storage hierarchy

Fingerprinting applies the concept of hash-functions to slides. Like everything in the Pathomation platform, the slide itself is the key unit to interact with. There is only one fingerprint for a slide, whether it consists of a single, or multiple files. Consequently, you can only obtain a fingerprint for a slide. If the file is somehow corrupt or the file format isn’t recognized by PMA.core, you’re not going to get a fingerprint from it. Last but not least, fingerprints are invariant across storage media and instances of PMA.core (PMA.start), making it a useful feature for slide tracking.

Random sampling and ground truth annotations

Challenges and opportunities

It’s been well established that whole slide images are big. We wrote a tutorial on this ourselves.

This poses challenges for both computers and analysts alike:

Consider the pathologist that must identify x number of cells of a certain classification. How many should he aim for? How big should his field of view be to select from?…

Automation seems to be a solution, but here too limits crop up. Professional image analysis software is expensive, people need to be trained, and there are only so many pixels any GPU can process in any given time.

The solution comes in treating the image analysis process as a multi-phased project.

In the first phase, select fields of view and regions of interest can be prepared as annotations on top of a whole slide image. This pre-selection can be as simple as an automated algo that identifies entropy in a pixel environment, or a pathologist that carefully picks and curates regions of interest.

In other words: statistical (random) sampling is the name of the game. And our very own is a great solution to make these ground-truth annotations in.

Annotations in PMA.core

Whether via scripting or manual curation, annotations end up stored in PMA.core.

Internally, we store the annotations as Well-Known Text (WKT) strings, but they can be converted to several other file formats, too, including Excel CSV, Visiopharm MLD, Leica/Aperio XML, or Halo Annotation XML.

We provide several other resources regarding annotations that can provide more background:

When your annotations are part of a random sampling exercise, chances are that you’re going to want to do more downstream operations with them.

In this article we will therefore:

  • Use Jupyter and pma_python to interact with PMA.core
  • Identify geometric (polygon) annotations and examine their properties
  • Convert annotations to rectangular snapshots at high-resolution
  • Save these extracted annotations as new separate high-resolution tiled TIFF slides


The Core module of our SDK contains a get_annotations() function already. Let’s start by examining what we get back when we invoke it on our sample slide:

from pma_python import core
core.connect("https://srv/pma.core/", "usr", "***")
slideref = "/rootdir/slide.mrxs"
annotations = core::get_annotations(slideref) 

Now we can print the first element and see what it contains. We use the pprint library to make our output look pretty:

We can immediately see the audit trail, and beyond that the most obvious element is the Geometry. As you be deduced: the geometry defines all points that make up an annotation. In our case our polygon is merely a rectangle, so we find 5 (x, y) coordinates, with the fifth one being the same as the origin. The format can be generalized and written out in a symbolic annotation that looks like this:

POLYGON((x1 y1,x2 y2, x3 y3, x4 y4,…, xn yn,…, x1 y1))

If we want to convert these annotations to snapshots, we need to determine the x y coordinates of the points that define a rectangle that contains all points of our original polygon.

In other words, for each of the x y pairs of coordinates given, we find the minimum and maximum x and y values. We can then use these to compute the width and height of the resulting (high resolution) snapshot.

Luckily this is easier to do than finding the largest rectangle within a polygon!

def annotation_to_rect(ann):
    points = ann.split(",")
    min_x = sys.maxsize
    max_x = sys.maxsize * -1
    min_y = sys.maxsize
    max_y = sys.maxsize * -1
    for point in points:
        (x, y) = point.split(" ")
        x = float(x)
        y = float(y)
        if x > max_x:
            max_x = x
        if x < min_x:
            min_x = x
        if y > max_y:
            max_y = y
        if y < min_y:
            min_y = y
    w = max_x - min_x
    h = max_y - min_y
    return (min_x, min_y, max_x, max_y, w, h)

And we can use this method to get the coordinates of the first annotation.


In earlier tutorials, we mostly stuck with extracting tiles from PMA.core. But if you want to extract arbitrary regions, you can use core::get_region() instead. The call uses the same coordinate system as used to store annotations.

Our next step then is to use these coordinates and parameters as arguments for the get_region() call.

region = core.get_region(slideref, min_x, min_y, w, h)

Without any additional parameters, get_region() automatically retrieves pixels at the deepest zoomlevel. While this is what you want, it is quite possible that your environment may be protected against such (perceived) over-zealous behavior and responds with an error:

DOS attacks are a reasonable concern of course.

The solution then is to split up the coordinates in 4 quadrants. Like this:

region11 = core.get_region("slide.mrxs", min_x, min_y, w / 2, h / 2)
region12 = core.get_region("slide.mrxs", min_x + w/2, min_y, w / 2, h / 2)
region21 = core.get_region("slide.mrxs", min_x, min_y + h/2, w / 2, h / 2)
region22 = core.get_region("slide.mrxs", min_x + w/2, min_y + h/2, w / 2, h / 2)

Once the four quadrants are loaded, a new PIL image can be constructed, and the 4 quadrants can be pasted into the respective corners.

region_combo ='RGB', (int(math.ceil(w)), int(math.ceil(h))))
region_combo.paste(region12, (int(math.floor(w/2)), 0))
region_combo.paste(region21, (0, int(math.floor(h/2))))
region_combo.paste(region22, (int(math.floor(w/2)), int(math.floor(h/2))))"region.jpg", "JPEG", quality = 95, optimize = True, progressive = True)

By working with quadrants, you’re effectively creating a de facto 2 x 2 grid. If this still doesn’t work for you, you can create 3 x 3 grids, or go even more refined.

Pyramidal TIFF

What’s missing? Say that your resulting extracted high-resolution snapshot is 8K x 5K pixels in size. You can work with that kind of image in some programs, but it’s not ideal. And your resulting snapshot can be even larger than that.

The solution is to not save your PIL image in a JPEG format. Instead, to save it as a pyramidal (tiled) TIFF. Some environments, like ASAP, even require this kind of input format.

After installing the gdal library, you can use the following method to convert any PIL image object into a pyramidal (tiled) TIFF:

def PILToTiff(pilref, output_file= "pil.tif", target_quality = 80, downscale_factor = 1):
    tileSize = 512    
    tiff_drv = gdal.GetDriverByName("GTiff")
    output_filename =  output_file
    (w, h) = pilref.size
    ds = tiff_drv.Create(
        output_filename,  w,  h,  3,
            'COMPRESS=JPEG', 'TILED=YES', 'BLOCKXSIZE=' + str(tileSize), 'BLOCKYSIZE=' + str(tileSize),

    tilesX = int(math.ceil(w / 512))
    tilesY = int(math.ceil(h / 512))
    totalTiles = tilesX * tilesY
    pbar = tqdm(total=totalTiles)
    for x in range(tilesX):
        for y in range(tilesY):

            x1 = x * 512
            y1 = y * 512
            x2 = min((x+1)*512, w)
            y2 = min((y+1)*512, h)
            tile = pilref.crop((x1, y1, x2, y2))
            arr = np.array(tile, np.uint8)

            # calculate startx starty pixel coordinates based on tile indexes (x,y)
            sx = x * tileSize
            sy = y * tileSize

            ds.GetRasterBand(1).WriteArray(arr[..., 0], sx, sy)
            ds.GetRasterBand(2).WriteArray(arr[..., 1], sx, sy)
            ds.GetRasterBand(3).WriteArray(arr[..., 2], sx, sy)

    ds.BuildOverviews('average', [pow(2, l) for l in range(1, 5)])
    ds = None
    print("Done; see result in ", output_filename)

When we now systematically want to convert all annotations from a set of slides into separate high-resolution pyramidal TIFF files, it’s just a matter of putting together the functions we’ve developed in this tutorial:

for slide in core.get_slides("/root_dir/path/…"):
    annotations = core.get_annotations(slide)
    ann_idx = 0
    for annotation in annotations:
        ann_img = AnnotationToPIL(slide, ann_idx)
        tif_file = "c:/output/" + os.path.basename(slide).replace(".", "_") + "_" + str(ann_idx) + ".tif"
        PILToTiff(ann_img, tif_file)
        ann_idx = ann_idx + 1

The result can be seen in PMA.start in the c:\output folder afterwards:

Ground truth

Image Analysis (IA) comes in many shapes: Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL)… What they all need: curated data to train on. Sometimes it’s as simple as feeding them all the tiles contained in a whole slide image one by one, and we’ve done a couple of examples of this already on our blog.

At times however, supervised machine learning is the name of the game. For that, a pathologist may pre-select particularly interesting looking areas of interest. In other cases, statistical random sampling may help to make an existing algorithm more robust or fine-tune it.

The resulting regions of interest (whether manually annotated through or automated via an environment like OpenCV) can in turn be exported again in individual high-resolution images that represent true subsets of slides.

In this article, we showed how such a complete workflow can be facilitated by our very own PMA.python SDK. The resulting dataset is at the same high-resolution as the original, and the performance of the images is just as good as what you started with.

Of course you may not be using Python, or you may just be looking for something just a bit different. Need help? Do drop us a note. We love hearing about your use case, and think about how we can help solve problems.

Angular.JS and PMA.UI

PMA.UI is a Javascript library that provides UI and programmatic components to interact with Pathomation ecosystem. I’s extended interoperability allows it to display WSI slides from PMA.core, PMA.start or Pathomation’s cloud service My Pathomation. PMA.UI can be used in any web app that uses Javascript frameworks (Angular.JS, React.JS etc) or plain HTML webpages.

As an example we will use Angular.JS and build an application based on PMA.UI. You can use an existing Angular.JS application or go on and use Angular’s demo application. Our application will have a tree that allows as to view and select directories and slides, a gallery that shows thumbnails of all slides in selected directory, and viewer for displaying selected slide.

First we have to install PMA.UI library using npm by running

npm i @pathomation/pma.ui

inside application’s directory.

Next step is to add JQuery in our app. Open index.html file inside src directory and add

<script src=""

after closing body tag. Also we have to add PMA.UI‘s css for components to display correctly. We can do that by opening angular.json file and replace

"styles": [


"styles": [

Now we can start modify PMA.UI component. Delete everything from components html file and add

    <!-- the element that will host the tree -->
    <div id="tree"></div>

    <!-- the element that will host the gallery -->
    <div id="gallery"></div>

    <!-- the element that will host the viewport -->
    <div id="viewer"></div>

These are the 3 main components we need. After that add

#tree {
    float: left;
    height: 100%;
    width: 20%;
    max-height: 95vh;
    overflow-y: scroll;

#viewer {
    float: left;
    height: 75vh;
    width: 80%;

#gallery {
    float: left;
    height: 20%;
    width: 80%;

in components css file and change if needed.

It’s time to implement component’s main functionality so open component’s ts file. First of all, we have to import OnInit function from @angular/core and PMA.UI

import { Component, OnInit } from '@angular/core';
import * as PMA from "@pathomation/pma.ui";

and change class to implement OnInit

export class MyComponent implements OnInit {

Lastly we have to create PMA.UI‘s context, slideloader, gallery, and treeview functions and their eventListeners.

ngOnInit() {
    console.log("PMA.UI version: " + PMA.getVersion());

    // create a context
    var context = new PMA.Context({ caller: caller });

    // add a prompt authentication provider
    new PMA.AutoLogin(context, [{ serverUrl: url, username: username, password: password }]);

    // create an image loader component that will allow us to load images easily
    var slideLoader = new PMA.SlideLoader(context, {
        element: "#viewer",
        overview: {
            collapsed: true
        // the channel selector is only displayed for images that have multiple channels
        channels: {
            collapsed: false
        // the barcode is only displayed if the image actually contains one
        barcode: {
            collapsed: true,
            rotation: 180
        loadingBar: true,
        snapshot: true,
        digitalZoomLevels: 2,
        scaleLine: true,
        filename: true

    // create a tree view that will display the contents of PMA.core servers
    var tree = new PMA.Tree(context, {
        servers: [
                name: "PMA.core",
                url: url,
                showFiles: true
        element: "#tree",
        preview: true

    // create a gallery that will display the contents of a directory
    var gallery = new PMA.Gallery(context, {
        element: "#gallery",
        thumbnailWidth: 200,
        thumbnailHeight: 140,
        mode: "horizontal",
        showFileName: true

    // listen for the directory selected event by the tree view
    tree.listen(PMA.ComponentEvents.DirectorySelected, function (args) {
        // load the contents of the selected directory
        gallery.loadDirectory(args.serverUrl, args.path);

    // listen for the slide selected event by the tree view
    tree.listen(PMA.ComponentEvents.SlideSelected, function (args) {
        // load the image
        slideLoader.load(args.serverUrl, args.path);

    // listen for the slide selected event to load the selected image when clicked
    gallery.listen(PMA.ComponentEvents.SlideSelected, function (args) {
        // load the image with the context
        slideLoader.load(args.serverUrl, args.path);

Build and run your application to check that everything works correctly. That’s it!

You can download complete demo here.

Additional developer resources are provided here.

Customizing the next generation of slide viewer software

It’s coming, it’s coming, it’s coming, it’s coming…

And we’re really excited about it!

We’re in the process of wrapping up This is going to be our flagship product for the next three years. It’s got everything a microscopy enthusiast needs, ranging from powerful viewport manipulation options, over annotations, to live conferencing. Remember our earlier article about how PMA.core handles (external) slide meta-data? All of that and more is in there, too.

In this blog post, we want to give developers as well as customers in our OEM and reseller program a sneak peek at and specifically focus on the opportunity for custom add-on development and white labeling. is currently in testing phase for various use cases already, ranging from comparative validation studies to enterprise-wide deployment as a central histological information cockpit. If you are interested in joining the effort and helping us track down last minute bugs, do shoot us an email and we can see if you’re a good fit for our beta-program.

You can find more information about itself in the landing page that we’re building for the software at

Administrative console has a separate interface apart for administrative tasks. admin console

In the administrative console, you can register any number of PMA.core instances to be used by to retrieve slides from. Once in use, for each registered PMA.core instance, you can see what users have sought access to it.

There’s a general dialog where you can set system-wide settings, like the address to contact in case of trouble, or the company / institute logo to use. This is where white labeling starts. global settings

Like for PMA.view 1.x, the administrative console in can be used to customize the ribbon interface at top of the interface. The syntax is XML. We don’t have any formal definition of the format, and typically work with customers to get the result they want.

You can play around with the XML code yourself if you want. The default toolbar provides enough example code to allow simple re-arrangements of buttons and move features between different tabs. ribbon editor

If you run into trouble, there’s always the option to restore the code back to a default toolbar.

Custom annotations supports annotations. We have a separate tutorial on the subject as part of our beta-program.

There are situations where you don’t want users to go off and make their own annotations at will.

You may want your pathologists to annotate invasive tumor margins or necrotic areas for a study protocol. It’s useful then that people align, and everybody uses the same color scheme. So in addition to providing standard annotation tools, it’s possible to define buttons on your toolbars w/ pre-set attributes and parameters.

Here’s a great example of a project we participated in recently, for which participants had to indicate six different types of tissues / cells: custom ribbon

The underlaying XML for this extra ribbon tab is as follows:

<Tab label="NKI" hint="custom ribbon tab" name="nki_annotations" enabled="true" visible="true">
    <Ribbon label="Total TLS" width="30%">
        <Tool type="buttons" size="large">
            <Command name="preset-peri_mat" icon="an_m_peri_tls.png" hint="Total # mature TLS (peritumoral)" label="Peritumoral, mature"
                pma-classification="peri mature"
            <Command name="preset-intra_mat" icon="an_m_intra_tls.png" hint="Total # mature TLS (intratumoral)" label="Intratumoral, mature"
                pma-classification="Intra mature"
           <Command name="preset-peri_immat" icon="an_im_peri_tls.png" hint="Total # immature TLS (peritumoral)" label="Peritumoral, immature"
                pma-classification="Peri immature"
            <Command name="preset-intra_immat" icon="an_im_intra_tls.png" hint="Total # immature TLS (intratumoral)" label="Intratumoral, immature"
                pma-classification="Intra immature"
        <Ribbon label="Pre-sets" width="30%">
            <Tool type="buttons" size="large">
                <Command name="preset-necrosis" icon="an_necrosis.png" hint="Tumor" label="% necrosis"
                <Command name="preset-fibrosis" icon="an_macro.png" hint="Tissue" label="% fibrosis"
            <Command name="preset-via_tumor" icon="an_tumorcells.png" hint="Tissue" label="% viable tumor cells"
                pma-classification="Viable tumor cells"

The pay-off of this kind of configuration is two-fold: the protocols are consistently executed as intended. In addition, the training needed for novel users to interact with the software is reduced because they only have a few options to choose from. Less can definitely be more in these scenarios.

Configuring the panel layout organizes its content across different panels. The layout of is a lot more powerful and flexible than in PMA.view 1.x.

All panels can be turned on or off via the Configuration tab on the ribbon. In addition, the admin console can be used to pre-determine the layout and panel organization when people first log in.

In its simplest configuration, you’re navigating slides and looking at them one by one:

You can add various information panels as you see fit to get to something that looks more like this:

Like with the ribbon customization and pre-defined annotation controls, you can pre-set’s panel layout. Again, you’re looking at XML snippets.

The layout from the latter screenshot is then defined as follows:

Iframe panels

A special type of panel is the iframe-panel. With these panels, you can virtually load any website or page that you want within a pre-defined panel. You can define your own panel through the Layout configuration button on the ribbon: ribbon

A pop-up dialog appears and let’s you specify the title of the panel, and the URL you want it to load. custom panel

In our example we took  a promotional video for our own PMA.slidebox product (more on that later), but you can put just about anything in there.

The result looks like this: custom panel

You can pre-define these custom iframe-panels any way you want through the admin console.

<Component name="IFrame" label="Pathomation video" url=""/>

Respective parameters that indicate the current state of the active viewport in are passed along automatically, like this:

Last but not least: a product pitch

We wrote earlier about PMA.slidebox.

This week, we’ve officially started promoting this product. We updated the landing page for PMA.slidebox, and the first demonstration videos are available through our YouTube channel.


Like with everything we do, we’re very excited about being able to package a ton of useful functionality in nevertheless compact product offering. Do have a look at our demo portal for PMA.slidebox and let us know if this something you, too, are interested in, too!

How to handle slide meta-data?

What is slide meta-data?

In order to know to interact with something, we first need to know what it is. Or, at least, we need you to know what it means for us. Just so we’re clear what we’re talking about.

For the sake of this article, we distinguish three different kinds:

  • Intrinsic meta-data: information stored within a slide’s file format. A trivial example is the slide’s pixel size, and a more advanced feature is the time it took the scanner to produce the slide.
  • User-captured meta-data (forms): Pathomation’s central tile server component, PMA.core, allows for the definition of table structures. Forms are a basic data structure in PMA.core that’s picked up again and used to capture and present user-attributed data in other several Pathomation platform components, including, PMA.slidebox, and PMA.control.
  • External data: if you have tons of external data in a separate repository and want access to it through the Pathomation software platform for digital pathology and virtual microscopy, you can link to it in real-time.

Below you can find an overview of what kind of meta-data is supported by what component and in what capacity

  Slide info Forms External data
PMA.start Read Not supported Not supported
PMA.core Read Define / read Read
PMA.view Read Not supported Not supported Read Read / write Read
PMA.slidebox Read Read / write Read
PMA.control Read Read / write Read

Slide info

Intrinsic slide meta-data can be shown in PMA.start by clicking on the filename in the viewport.

In the upcoming versions of both PMA.view and, we provide a separate info-panel for this.

There can be more slide info available than is actually shown in our various front-end interfaces. Some information is really specific, too specialized, or irrelevant even to show to most users. Other fields are scanner-specific and don’t make sense to include on a systematic basis.

At the API and SDK level, we provide dedicated SlideInfo calls that return full hierarchical dictionary structures that do give exhaustive slide information and can be consumed as you see fit for your specific application or workflow.

Data with a twist

Forms are defined in PMA.core through the form editor.

PMA.core’s forms support trivial datatypes like text or numbers (of course). We also offer scientifically relevant twists on traditional data capture. An example is that numerical data fields allow the option to be recorded as “below detectable limit”, since it’s very hard to prove that something is not present in a sample. Oftentimes, a numerical zero just means that our detection apparatus just lacks the sensitivity the unmask the presence of a specific phenomenon.

What can you do in forms? Pretty advanced things. We can model the CAP-recommended cancer protocols as PMA.core forms.

PMA.core offers different ways to interact with forms, but it doesn’t provide a data entry module. That’s because it doesn’t really make sense to enter data at this level. Data entry is done in the context of an application or workflow. Our upcoming will support data entry, and PMA.control offers several interaction modes. Underneath, PMA.control interaction modes rely on data stored as PMA.core forms.

You can ask PMA.core to generate a spreadsheet template based on a select folder of slides and a particular already defined form.

At Pathomation, we pride ourselves to be a truly open platform, so PMA.core offers several data formats to export captured form data to, including CSV, XML, and ARFF.

Use cases for external data

Imagine that you organize a toxicology experiment with a rodent population. In a separate database, you’ve been keeping data about each specimen’s vital statistics, dietary and behavior observations, as well as their phenotypic expression and genotypic make-up. Observations happen daily, so that’s 1750 new records per week.

You take weekly biopsies of the animals to monitor their response to a new drug you’re testing. The biopsies are prepared and stained in triplicate (good for a total of 750 new slides per week), and each slide has a barcode that can be used to trace back to the original individual animal. Your slide scanner takes care that the barcode is encoded in the slide’s filename.

Both the slide population and the separate database keep evolving. Replication between your database and a PMA.core form is considered at some point, but deemed inefficient and error-prone, because we are talking about experimental and evolving data structures here.

The solution is to define an external data source in PMA.core. This goes in two steps: first a connection string is defined to connect to the database server.

Next, the external connection is used to formulate any number of queries against.

Data can be previewed within PMA.core, and subsequently is automatically propagated to other environments like

External data are everywhere. They can be in proprietary databases as in our example above, but they can also be in a (AP)LI(M)S, VNA, PACS, or EHR system. In all those cases, replication is hard and impractical at best, and can even lead to data inconsistencies and errors at worst.

The Pathomation platform for digital pathology and virtual microscopy allows for a more elegant solution.

What WSI data are REALLY made of

Image data types

Pathomation is concerned with any (imaging) data that is microscopy- or pathology-related. Much of the data is large: we talk about gigapixel data, or (more apt for microscopy) whole slide images (WSI).

Not all image data accessed via Pathomation need be large though:

  • Microscopic images can represent individual fields of view, specific areas of interest captured by a mounted camera and for discussion or other ad hoc purposes
  • Pathology starts from physical tissue. Therefore, it is often useful to photograph the obtained tissue. These are typically referred to as “macroscopic images”.

Oftentimes the above results can be stored in common (image) file formats: JPEG and TIFF are most often encountered. You can distribute these images via any medium. But if you have them side by side with your high-resolution images representing prepared slides, it’s reassuring to know that with Pathomation software you can organize these different slides under one umbrella.

Back to the really big data. Elsewhere on this blog, we have an article about the technical challenges when wanting to store an image that contains 100,000 x 200,000 pixels.

We send said article to (potential) customers regularly. Some are helped by it, some not. Because, understandably, many times you just want to know about your slides. When you book a flight, you don’t want to get an explanation about Newton’s or Bernouilli’s laws either… Just get me the tickets please.

So why did we write the article in the first place? Because at Pathomation, we pride ourselves at being format-agnostic. We don’t pick hardware vendors. Each scanner has their pros and cons. Each comes out to server a specific market segment and works better with some tissues than others. It’s not our place to subjectively decide who’s better or worse (although we would appreciate better feedback from some with respect to a vendor’s specific file format).

So please do understand that mentioning specific vendors has nothing to do with any positive or negative endorsements for said vendor. We’re merely stating facts with respect to the way how their techies have long time ago decided to organize their (giga)pixels.

Single or many files

As you already know from the previous article, virtual slides can consist of many different files. Within the files that represent MRXS slides, you find plenty of .dat files, for VSI slides you find .ets files (amongst others) etc.

Several vendors have adopted a single file format for their WSI data. These include Hamamatsu (NDPI), Aperio (SVS), Leica (SCN), and Zeiss (CZI and ZVI).

Other vendors that have adopted a multi-file approach include 3DHistech (MRXS), Olympus (VSI), and Motic (MDS).

The organization of data across multiple files for different vendors is not standardized. In the case of 3DHistech, individual files more or less represent different magnifications. Olympus’ file structure seems more organized around scanned regions of interest.

The single file formats can also be upgraded to multi-file formats. Hamamatsu scanners can create .ndpis files for fluorescent or z-stacked content. The “s” stands for “set”: the .ndpis file merely contains pointers to individual .ndpi files which then each contain the image for the particular layer or channel. You can open these .ndpi files by themselves by the way, but they’re only useful when you also correctly interpret their context from the .ndpis file.

Aperio SVS files can be accompanied by .xml files, which contain annotations. Ventana BIF files can be accompanied by .tifp and .bmp files.

As soon as you have more than 1 file involved, it becomes a multi-file file-format. The strict distinction is important, because some storage systems don’t support multi-file containers.


Let’s look at the 3DHistech line of scanners: In the screenshot below we scanned two slides: HE and PDL1. The software ends created “HE.mrxs” and “PDL1.mrxs” files, along with “HE” and “PDL1” subfolders.

Within the subfolders, a standard naming convention it used, so you won’t find any more “HE*” files in there.

The .mrxs files themselves are typically just there to allow third-parties to identify these different file-types. Case in point: the .mrxs file can be renamed into a .jpeg file, and then you can just view it as any other image (it’s a quick way for us, too, to display slide thumbnails).

Another example? You got it!

This is what the structure of a .VSI slide looks like:

Each scanned region translates into a “stack”; each stack can contain one or multiple frames.

A different approach, a more hierarchical structure.

For DICOM slides, all related files are grouped together. No subdirectory required:

The details of these for the typical end-user don’t matter, except that they do matter when you want to copy / move/ transfer slides to others. The basic principle here to always make sure you not copy the index .vsi / .mrxs / .whatever file, but also all the accompanying data-files. Zipping them may help both you and your receiving party.

How Pathomation can help

Have a look at our [slides] folder in the Windows Explorer:

Confusing, right?

Now let’s have a look at the same folder through PMA.start eyes:

Since Pathomation knows slides, we systematically hide the intricacies of respective formats that you shouldn’t to worry about. In PMA.start it becomes obvious which subfolders are true subfolders, and which ones are merely there to support vendors’ data structures.

This doesn’t help you yet to transfer slides of course, but if you are one of our commercial users, you typically want to transfer slides from your local system to PMA.core and back. If that is you, then PMA.transfer is a great free tool (it’s part of your license package) for you to look at.

This is how our [slides] folder shows in PMA.transfer:

PMA.transfer seamlessly interfaces with PMA.start, and uses it as a jumping board to transfer slides between different endpoints. The biggest benefit of PMA.transfer therefore is that it encapsulates format-specific complexities and hides them from the end-user. Through PMA.transfer, you’re truly manipulating slides instead of files.

In addition, PMA.transfer also makes sure that only correct slides are transferred (nothing more frustrating than transferring 2 GB of data over Wifi, only to discover that the source was corrupt), as well as confirming that the transfer was completed successfully (probably the second most common source of frustration in the endeavors).

Why it matters

At Pathomation, we take much care designing our components in such a way that data duplication can be avoided at all costs. In PMA.control e.g., you can create as many cases and case collections as you want, but the data always remain in the same location. In PMA.core, you can create nested root-directories, each with different ACL properties. To your end-users these end up looking like different filesystems, but they’re really not. At the API level, we provide the possibility to fingerprint a slide, so you can scan for duplicate files and possibly eliminate them. All of these measures matter when you’re talking about Terabytes of data.

But there comes a time when you do need to copy slides. Perhaps you’re moving them from one installation to another, or there’s a network upgrade, or you just want to ship off some slides to a colleague (My Pathomation can now do this for you, too).

Whatever the case, (virtual) slides will need to be moved around. By their nature, they are big. It helps to have some understanding about how they are structured at that point.

And now you know…

… everything about whole slide images, or, at least, almost everything.

Amongst other things, .mrxs-, .vsi-, and other files help companies like ourselves decide what kind of file format is being used. The alternative would be that we find a large number of subfolders, and we have to parse each and everyone of those folders in a variety of ways to try to “guess” what file format it belongs to (if this is even the case at all; a subfolder can still be a regular subfolder, containing no slides at all). This would be a tremendous drag on system performance.

Whether you deal with Pathomation or another vendor, we hope this article has helped you take a peek behind the curtain of what whole slide images are made of, and how to best work with them.

Of course, if you are using the Pathomation platform, you should have a look at PMA.transfer, a no-hassle tool we developed to facilitate slide transfers.

If you’re not yet a Pathomation customer (whaaaaaaaaaat??), you can contact us for a free no-obligation demonstration.

I “just” want a slide catalog

The case for simplicity

Sometimes user requests are simple. In this particular instance, we had a customer that “just” wanted a list of all of their slides (within a particular root-directory).

We pointed them to the repository of PMA.control:

We pointed them to the tree interface that combines folder and slides in PMA.view:

But as it turned out, these were too complicated. The customer already had built a nested hierarchy of folders and subfolders, but now wanted a linear list of all slides across all folders. A thumbnail next to each slide reference would also be useful, thank you very much.

A linear list of slides

Here’s how we can create a linear list of slides:

from pma_python import core
from datetime import date
from os import mkdir
from os.path import exists


slides = core.get_slides("C:/slides", recursive=True)
print(len(slides), " slides found")     # sanity check

f = open("c:/wsi_report/cat1.html", "w+")
f.write("Market slide catalog created on " + str( + "")
for slide in slides:
    f.write(slide + "
") f.write("") f.write("") f.close()

Want to include the thumbnail? Look no further than the get_thumbnail_url method:

f = open("c:/wsi_report/cat2.html", "w+")
f.write("Market slide catalog created on " + str( + "")
for slide in slides:
    thumb = core.get_thumbnail_url(slide)
    f.write("" + slide + "
") f.write("") f.write("") f.close()

Ah crap, that looks horrible!

No worries; just add some formatting to the ole’ <img> tag:

f = open("c:/wsi_report/cat3.html", "w+")
f.write("Market slide catalog created on " + str( + "")
for slide in slides:
    thumb = core.get_thumbnail_url(slide)
    f.write("" + slide + "
") f.write("") f.write("") f.close()

Yes, something like this:

Much better!

But there’s a catch here: careful observers notice that the thumbnail URLs used by the above code have a PMA.core Session ID embedded. This means that those URLs are only valid as long the respective Session IDs remain valid.

This is fine for ad-hoc reporting, but if we want a list that we can post somewhere on a central server as a reference source for others, we need something just a little more sophisticated. Yes, the keyword in this post is “just”, just in case you’re wondering.

Creating a persistent slide catalog

We want to create a list of all slides in our repository, and we want to list to be persistent. In other words, it is not ok to walk away from our browser for a couple of hours, refresh our list, and see the following:

The solution is to not use the thumbnail URL, but retrieve each thumbnail as a binary object and save it to a subfolder. Then, we let our <img> tag point to the downloaded (or cached, if you will) files.

dir = "c:/wsi_report/cat4/"
if not exists(dir): 
f = open("c:/wsi_report/cat4.html", "w+")
f.write("Market slide catalog created on " + str( + "")
for slide in slides:
    thumb = core.get_thumbnail_image(slide)
    fn = core.get_slide_file_name(slide) + ".jpg"
    thumb_fn = dir + fn
    print("Saving thumbnail as ", thumb_fn)
    f.write("" + slide + "
") f.write("") f.write("") f.close()

This catalog fits our needs. We can post it anywhere, and it only relies on local data. You can store it on your hard disk. You don’t need PHP, you don’t need Python, you don’t need the underlying PMA.core (PMA.start in our example code) to be up and running.

There are cons to our approach, too:

  • The catalog takes longer to generate, as we need to download a large number of thumbnails one by one
  • The catalog takes up more space. In our case, for 265 slides, we went from 197 KB to 20+ MB. That’s still manageable to send in a zipped file package, but for larger repositories may become inconvenient.
  • The catalog is a snapshot of the repository. If you use as a reference today and add slides to the underlying root-directory tomorrow, the catalog will not pick up the newly added (or removed, for that matter) slides, unless you re-generate the catalog and re-distribute or publish it.

And finally

Every solution has trade-offs, and even for simple problems, you have think through things to come to the right solution. Problems that have the word “just” somewhere in their formulation can be particularly trickly.

The sourcecode for this post is available through our repository as realdata 038 – simple slide catalog.ipynb. Feel free to download it and adjust it for your own needs.

As always: we encourage interaction. Do let us know what digital pathology problem or scenario you want us to work out in one of our next posts!

My first 6 months @Pathomation

Or the story of me, being thrown to the lions several times.

Digital pathology, a topic I had never heard of six months ago (can you imagine?!) is now one of the most important subjects in my life.

To be honest, I never thought I would have gotten the job at Pathomation, but even though I did not have any experience they still believed in me (or they just gave me the benefit of doubt of course). And for sure I’m glad they did.

I still go to school and before Pathomation, I worked at a local supermarket, which is nice but not very challenging. Now I have to step out of my comfort-zone everyday, doing things I’ve never done and trying to be the best and smartest version of myself, since I’m surrounded by pathologists, scientists etc… Believe me they are a challenge in themselves, but a challenge I’ve accepted happily, because working with people that are so skilled and have such great knowledge is enlightening and motivating.

Step by step and day by day, I’m learning more about medical imaging and the whole world around it. Not only by working from the office, but also abroad. During the first week of January I went to Morocco to work side by side with our Moroccan developers. This was my first work trip ever!

Unfortunately, it was (probably) also the last one of this year, due to unseen circumstances (covid-19, anybody?). I’m waiting for the next trip, even though I’m secretly happy with that little bit of extra time to mentally prepare myself for a medical conference… (which will also be my first one of course)

You might be wondering what I actually do in this company, since I’ve been bragging so much about it in this post. Explaining that in only one paragraph is quite hard, but I’ll try to keep it short.

First of all, I want you to understand, that there’s a variety of things that I actually do, and an array of things that I try to do. All paperwork related things, like bookkeeping, invoicing and preparing estimates, that’s on me! I also try to help Yves by testing our software and giving him my opinion from a user perspective (and hopefully inspire him for new ideas). In actual fact, I try to ease the work of all my colleagues where I can. Because of this, my job contains a wide selection of different tasks, and I’m still exploring what I like to do most.

As you can see, I get lots of chances where I can grow my courage and improve my social skills. Therefore, I’m glad I can be part of this small but growing team!