Abstract #0897

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

Anum Masood^1,2, Sølvi Knapstad², Håkon Johansen³, Trine Husby³, Live Eikenes ¹, Pål Erik Goa^2,3, and Mattijs Elschot ^1,3

¹Department of Circulation and Medical Imaging, NTNU, Trondheim, Norway, ²Department of Physics, NTNU, Trondheim, Norway, ³Department of Radiology and Nuclear Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway

Synopsis

Keywords: Segmentation, Data Processing, Automated Segmentation, Deep Learning, nnUnetWidespread 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.

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