Anisotropic Hybrid Networks for liver tumor segmentation with
  uncertainty quantification
Benjamin Lambert Pauline Roca Florence Forbes Senan Doyle Michel Dojat
The burden of liver tumors is important, ranking as the fourth leading cause
of cancer mortality. In case of hepatocellular carcinoma (HCC), the delineation
of liver and tumor on contrast-enhanced magnetic resonance imaging (CE-MRI) is
performed to guide the treatment strategy. As this task is time-consuming,
needs high expertise and could be subject to inter-observer variability there
is a strong need for automatic tools. However, challenges arise from the lack
of available training data, as well as the high variability in terms of image
resolution and MRI sequence. In this work we propose to compare two different
pipelines based on anisotropic models to obtain the segmentation of the liver
and tumors. The first pipeline corresponds to a baseline multi-class model that
performs the simultaneous segmentation of the liver and tumor classes. In the
second approach, we train two distinct binary models, one segmenting the liver
only and the other the tumors. Our results show that both pipelines exhibit
different strengths and weaknesses. Moreover we propose an uncertainty
quantification strategy allowing the identification of potential false positive
tumor lesions. Both solutions were submitted to the MICCAI 2023 Atlas challenge
regarding liver and tumor segmentation.