Liver Lesion Segmentation with slice-wise 2D Tiramisu and Tversky loss function

9 May 2019  ·  Karsten Roth, Tomasz Konopczyński, Jürgen Hesser ·

At present, lesion segmentation is still performed manually (or semi-automatically) by medical experts. To facilitate this process, we contribute a fully-automatic lesion segmentation pipeline. This work proposes a method as a part of the LiTS (Liver Tumor Segmentation Challenge) competition for ISBI 17 and MICCAI 17 comparing methods for automatics egmentation of liver lesions in CT scans. By utilizing cascaded, densely connected 2D U-Nets and a Tversky-coefficient based loss function, our framework achieves very good shape extractions with high detection sensitivity, with competitive scores at time of publication. In addition, adjusting hyperparameters in our Tversky-loss allows to tune the network towards higher sensitivity or robustness.

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Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Liver Segmentation LiTS2017 U-Net LiS (MICCAI 17) Dice 94 # 3


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