This is the code for medmodeler, an end to end automatic segmentation tool for MRI/CAT files. This model identifies various tissues like lung, bladder, liver, kidney and bone, and then produces a medical image file for ease of visualization using torchio.
For use in your own project, simply download the project, install its dependencies and import "inference()" from inference.py file
- This project utilizes the famous PyTorch segmentation repo by qubvel that can be found here
- Specifically, this model uses the Feature Pyramid Network Architecture, with the Mix Vision Transformer as the encoder and pre-training performed on ImageNet
- This model was trained on the CT-ORG Dataset
- The model performs well on bone and liver segmentation, but often fails to fully capture the other classes, most likely due to insufficient training
- When inferencing, the tensor_to_label_map() function is a huge bottleneck, and could likely be implemented more efficiently with matrix operations
- No input validation, inference only accepts traditional medical image formats like *.nii.gz
This project was produced in collaboration with the University of Florida, who supplied compute resources in the form of 4 NVidia A100 GPUs.