How We Estimate 3D Volumes in the Viewer


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 ↓

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A Quick Look at Our Volume Measurement Tool

One of the tools we’ve been working on is a simple way to estimate 3D volumes directly inside the viewer.

You start by drawing a polygon on any slice, axial, sagittal, or coronal. Right-click to finalize it, then move to another slice and draw the same region again.

The viewer interpolates between the two slices and gives you an estimated 3D volume of the structure.

A clean and intuitive workflow, and one of the features we build into our clients’ web DICOM viewers.

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AI Algorithms Making a Difference in Healthcare

AI is being applied in many areas of healthcare, especially where pattern recognition and large datasets play a major role. Some notable examples include:

  • Cancer: algorithms that detect DNA mutations in tumors to guide personalized treatments.
  • Cardiology: models that analyze echocardiograms and help assess cardiac function with strong accuracy.
  • Risk prediction: tools that estimate the likelihood of heart attacks, strokes, or suicide risk using routine clinical data.
  • Medical imaging: systems that support early detection of conditions such as breast cancer and skin cancer.
  • ICU and emergency care: algorithms that monitor patients and flag early signs of sepsis or other critical events.
  • Clinical decision support: large language models designed to assist with medical questions and summarize clinical information.

These algorithms don’t replace clinicians; they add an extra layer of support by catching subtle patterns, speeding up assessments, and helping prioritize high-risk cases.


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We Can Help You with Your Next Medical Imaging Project

If your company or organization is looking to build a machine learning solution for a medical imaging problem, then feel free to reach out to us at:

​contact@pycad.co​

We can help you build a full ML solution from training to deployment with affordable rates!

You can check out some of the projects that we worked on here:

​https://pycad.co/portfolio.

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That's it for this week's edition, I hope you enjoyed it!

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Machine Learning for Medical Imaging

👉 Learn how to build AI systems for medical imaging domain by leveraging tools and techniques that I share with you! | 💡 The newsletter is read by people from: Nvidia, Baker Hughes, Harvard, NYU, Columbia University, University of Toronto and more!

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