Automatic segmentation of Organs at Risk in Head and Neck cancer patients from CT and MRI scans

no code implementations • 17 May 2024 • Sébastien Quetin, Andrew Heschl, Mauricio Murillo, Rohit Murali, Shirin A. Enger, Farhad Maleki

Materials and methods: Paired CT and MRI-T1 images from 42 H&N cancer patients alongside annotation for 30 OARs from the H&N OAR CT & MR segmentation challenge dataset were used to develop a segmentation pipeline.

Anatomy Segmentation

Bridging the Gap: Generalising State-of-the-Art U-Net Models to Sub-Saharan African Populations

no code implementations • 19 Dec 2023 • Alyssa R. Amod, Alexandra Smith, Pearly Joubert, Confidence Raymond, Dong Zhang, Udunna C. Anazodo, Dodzi Motchon, Tinashe E. M. Mutsvangwa, Sébastien Quetin

We replicated a framework that secured the 2nd position in the 2022 BraTS competition to investigate the impact of dataset composition on model performance and pursued four distinct approaches through training a model with: 1) BraTS-Africa data only (train_SSA, N=60), 2) BraTS-Adult Glioma data only (train_GLI, N=1251), 3) both datasets together (train_ALL, N=1311), and 4) through further training the train_GLI model with BraTS-Africa data (train_ftSSA).