This repository contains the code of SAM-UNETR, a model that combines the Segment Anything Model (SAM) image encoder from Meta AI with a convolutional-decoder based on UNETR. SAM-UNETR was specifically trained and evaluated for the challenging task of Clinically Significant Prostate Cancer Segmentation.
SAM-UNETR is designed to leverage the strengths of both SAM and UNETR to achieve accurate prostate cancer segmentation. The model incorporates the powerful image encoding capabilities of SAM with the effective convolutional-decoder structure of UNETR, resulting in improved segmentation performance.
To train and evaluate SAM-UNETR, a dataset of clinically significant prostate cancer images was used. The dataset consists of high-resolution MRI scans along with corresponding ground truth segmentation masks from PI-CAI Challenge and Prostate158.
SAM-UNETR achieved good results on the Clinically Significant Prostate Cancer Segmentation task. The model's performance was evaluated against other models apliying the same technique.
To use SAM-UNETR first run Extract_SAM_encoder_weigths.ipynb to save the weights of the image encoder, then run requires Train_model.py file that you want to use.
Finally Analize_Results_model.py contains the codes for predictions on each model