Multitask-Learning for the Extraction of Avascular Necrosis of the Femoral Head in MRI

In this paper, we present a 2D deep multitask learning approach for the segmentation of small structures on the example of avascular necrosis of the femoral head (AVNFH) in MRI. It consists of one joint encoder and three separate decoder branches, each assigned to its own objective. We propose using a reconstruction task to initially pre-train the encoder and shift the objective towards a second necrosis segmentation task in a reconstruction-dependent loss adaptation manner. The third branch deals with the rough localization of the topographical neighborhood of possible femoral necrosis areas. Its output is used to emphasize the roughly approximated location of the segmentation branch’s output. The evaluation of the segmentation performance of our architecture on coronal T1-weighted MRI volumes shows promising improvements compared to a standard U-Net implementation.