MultitaskLearning 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 pretrain the encoder and shift the objective towards a second necrosis segmentation task in a reconstructiondependent 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 branchxe2x80x99s output. The evaluation of the segmentation performance of our architecture on coronal T1weighted MRI volumes shows promising improvements compared to a standard UNet implementation.

Joint Learning of Brain Lesion and Anatomy Segmentation from Heterogeneous Datasets Brain lesion and anatomy segmentation in magnetic resonance images are fundamental tasks in neuroimaging research and clinical practice. Given enough training data, convolutional neuronal networks (CNN) proved to outperform all existent techniques in both tasks independently. However, to date, little work has been done regarding simultaneous learning of brain lesion and anatomy segmentation from disjoint datasets.  :56,this work we focus on training a single CNN model to predict brain tissue and lesion segmentations using heterogeneous datasets labeled independently, according to only one of these tasks (a common scenario when using publicly available datasets). We show that label contradiction issues can arise in :56,this case, and propose a novel adaptive cross entropy (ACE) loss function that makes such training possible. We provide quantitative evaluation in two different scenarios, benchmarking the proposed method in comparison with a multinetwork approach. Our experiments suggest that ACE loss enables training of single models when standard cross entropy and Dice loss functions tend to fail. Moreover, we show that it is possible to achieve competitive results when comparing with multiple networks trained for independent tasks.