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arXiv · 1810.04017

Comparison of U-net-based Convolutional Neural Networks for Liver Segmentation in CT

Hans Meine·Grzegorz Chlebus·Mohsen Ghafoorian·Itaru Endo·Andrea Schenk
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Abstract

Various approaches for liver segmentation in CT have been proposed: Besides statistical shape models, which played a major role in this research area, novel approaches on the basis of convolutional neural networks have been introduced recently. Using a set of 219 liver CT datasets with reference segmentations from liver surgery planning, we evaluate the performance of several neural network classifiers based on 2D and 3D U-net architectures. An interesting observation is that slice-wise approaches perform surprisingly well, with mean and median Dice coefficients above 0.97, and may be preferable over 3D approaches given current hardware and software limitations.

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Hans Meine, Grzegorz Chlebus, Mohsen Ghafoorian, Itaru Endo, Andrea Schenk. 2018-10-09. Comparison of U-net-based Convolutional Neural Networks for Liver Segmentation in CT. https://arxiv.org/abs/1810.04017

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