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TorchXRayVision Meets 3D Slicer: Bridging Deep Learning and Medical Imaging

Key Investigators

• Constantin Constantinescu (Lucian Blaga University of Sibiu, Romania)
• Juan Ruiz-Alzola (University of Las Palmas de Gran Canaria, Spain)
• Csaba Pintér (EBATINCA, Spain)

Project Description

This project focuses on developing a 3D Slicer module for the automatic processing of chest X-rays, integrating powerful deep learning capabilities provided by TorchXRayVision. The module streamlines radiological analysis by offering the following features:

• Segmentation: Automatically identify and outline anatomical regions in chest X-rays, such as lungs or other structures.
• Anomaly Detection: Detect abnormalities and highlight regions of interest for further investigation.
• Pathology Classification: Classify pathologies such as pneumonia, atelectasis, or other common conditions.

By combining the advanced machine learning models from TorchXRayVision with the versatile 3D Slicer platform, this module aims to provide a robust tool for clinicians and researchers to enhance diagnostic workflows, reduce manual workload, and improve consistency in radiological interpretation.

Objective

1. A 3D Slicer Module
2. TorchXRayVision models included in the module
3. Torch XRays automatic segmentation, anomaly detection and pathology classification
4. Heatmaps

Approach and Plan

1. Create a Slicer Module
2. Create an interface to upload X-Rays and perform automatic analysis
3. Use TorchXRayVision framework to perform automatic analysis
4. Compute heatmaps

Progress and Next Steps

1. Creating a 3D Slicer Module
2. Building the interface
3. Including the TorchXRayVision models (work in progress)

Illustrations

No response

Background and References

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