Hello Reader,

Welcome to another edition of PYCAD newsletter where we cover interesting topics in Machine Learning and Computer Vision applied to Medical Imaging. The goal of this newsletter is to help you stay up-to-date and learn important concepts in this amazing field! I've got some cool insights for you below ↓

Trame for Building Medical Imaging Web Apps

This week I've been doing some experimentation with Trame. Here's a quick explanation of what you're seeing below ↓

This is a web application built purely in Python. It's using a library called Trame.

That thing that looks like an alien is the Aorta. Which is the largest blood artery in the human body!

This is actually the output of a deep learning model that we trained to segment the aorta automatically.

The output of this model is a 3D array that we saved as Nrrd file for easy manipulation.

We then reconstructed an STL object using VTK library.

Then we are rendering the STL object directly on the browser using Trame.

All of this process is automated!

The output looks very slick if you ask me 😂

This is the first time I try Trame, and I have to say that I'm impressed so far!

I will probably document more about my experiments with it, so stay tuned!

At PYCAD we have built several models like this for our clients, for automatic segmentation of different organs and anatomical structures.

We have also built web apps for them that allow them to easily run the AI models in the backend and do visualizations like this in the frontend (using other tools, not Trame).

After my experimentations, if I see that Trame is truly as powerful as its creators make it seem, then I will definitely use it when we build medical imaging web apps (or desktop apps) for our clients in the future!

Free Tools to Use for Medical Data Annotation

Some people ask me what we use to annotate medical imaging data. Here's my answer.

Usually, we don't annotate medical data by ourselves. We work with certified 3rd party companies to do this.

But what if I want to do some minor modifications on some annotation?

In this case, we use 2 freely available software programs: 3D Slicer and ITK Snap.

They both make it easy to visualize all sorts of medical data formats including: DICOM, NIFTI and NRRD.

Btw, we don't just use these tools to make modifications to medical annotations.

We also use them to do data verification and validation, which is a crucial step in every project we take at PYCAD.

When do we use which?

Generally speaking, ITK Snap is my go to for a quick data visualization and checking. Mostly because it opens up quicker than 3D Slicer 😅

But if I want to do a thorough check of the data and have more control, then I go with 3D Slicer.

However, when I want to be very precised when doing data verification and validation, I actually open the file I'm inspecting in both software.

This helps in some edge cases where the person who did the annotation used 3D Slicer and saved the file in some generic way.

When an annotator does this, it could hide some information that are crucial for any machine learning project.

For example, we noticed that sometimes when we open an annotation file in 3D Slicer, we see: segment_1, segment_2, ...

These would be the names of the annotated classes.

If you just look at this, you'll think that segment_1 has label 1, segment_2 has label 2 so on..

But in reality, it could be that segment_1 has label 2, segment_2 has label 5, ...etc.

This could happen when the annotator creates a segment (annotation), then deletes it and creates a new one. 3D Slicer keeps the information of the deleted segment but hides it away.

When we open the same patient file in ITK Snap, we immediately notice the discrepancy in the labels.

This is just one of the possible scenarios.

Keep this in mind next time you're working on a medical imaging project 😉.