Retraining a U-Net on masks derived from its own segmentation errors improves liver and lesion dice by up to 2 points on the LiTS dataset.
Liver Lesion Segmentation with slice-wise 2D Tiramisu and Tversky loss function
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abstract
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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Mask Mining for Improved Liver Lesion Segmentation
Retraining a U-Net on masks derived from its own segmentation errors improves liver and lesion dice by up to 2 points on the LiTS dataset.