Joint Learning of Brain Lesion and Anatomy Segmentation from Heterogeneous Datasets

Nicolas Roulet, Diego Fernandez Slezak, Enzo Ferrante

Keywords: Brain image segmentation, multi-task learning, heterogeneous datasets, convolutional neural networks

TL;DR: We propose the adaptive cross entropy loss to perform joint learning of brain lesion and anatomy segmentation from disjoint datasets, that were labeled independently according to only one of these tasks

Abstract:

Brain lesion and anatomy segmentation in magnetic resonance images are fundamental tasks in neuroimaging research and clinical practise. 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.

In 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 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 multi-network approach. Our experiments suggest 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.

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