Deep Learning Based Automated Multi-Organ Segmentation in Lymphoma Patients using Whole Body Multiparametric MRI Images

Widespread of lymphoma cancer makes manual segmentation of metastatic lymph nodes a tedious task. Lymphoma cancer is assigned an anatomic stage using the Ann Arbor system which relies on the segmentation and localization of affected lymph nodes with respect to anatomical stations. We present a framework for multi-organ segmentation for multiparametric MRI images. Our modified nnUnet using a transfer learning approach achieved 0.8313 mean DSC and 0.659 IoU in lymphoma cancer dataset.