Deep Segmentation of Abdominal Organs from MRI: off-the-shelf architectures and improvements

Deep Learning outperforms prior art in medical imaging tasks. It has been applied to segmentation of Magnetic Resonance Imaging (MRI) scans, where consecutive slices capture relevant body structures for visualization and diagnosis of medical condition. In this work we investigate experimentally the factors that improve segmentation performance of MRI sequences of abdominal organs, including network architecture, pre-training, data augmentation and improvements to loss function. After comparing segmentation network architectures, we choose the best performing one and experimented improvements (data augmentation, training choices). Finally, metrics are fundamental and IoU of each organ in particular, therefore we change loss function to IoU and evaluate the resulting quality. We show that DeepLabV3 is better than competitors by 20 percentage points (pp) or more (depending on the competitor), data augmentation and further enhancements improve performance of DeepLabV3 by 12 percentage points (pp) in average, and that loss function improves performance by up to 13pp as well. Finally, we discuss challenges and further work.