Medical Image Analysis: LV Segmentation
Workshop on Cardiac MRI segmentation using U-Net architecture. Covering DICOM processing and Dice-based loss functions.
This specialized workshop focuses on Medical Image Analysis, specifically the segmentation of the Left Ventricle (LV) from Cardiac MRI scans. Using the U-Net architecture, we explore how deep learning can assist in automated cardiac diagnostics.
Workshop Instructor: Zahra Amini
The repository includes the complete pipeline: from handling medical imaging formats to implementing custom loss functions and evaluating the model’s performance on clinical datasets.
🚀 ACCESS WORKSHOP MATERIALS ON GITHUB
Workshop Highlights
The curriculum is designed to cover the end-to-end medical segmentation workflow:
- Medical Data Handling: Processing DICOM/NIfTI files and data augmentation techniques specifically for MRI.
- Architecture Deep-Dive: Understanding the symmetric encoder-decoder structure of U-Net and skip connections.
- Loss Functions for Imbalance: Implementing Dice Loss and Intersection over Union (IoU) to handle class imbalance in medical masks.
- Evaluation: Analyzing segmentation masks and measuring pixel-wise accuracy in a clinical context.
Technologies used: PyTorch/TensorFlow, OpenCV, Nibabel, Scikit-Image.