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Liver segmentation from abdominal CT images is an essential step for livercancer computer-aided diagnosis and surgical planning. However, both theaccuracy and robustness of existing liver segmentation methods cannot meet therequirements of clinical applications. In particular, for the common clinicalcases where the liver tissue contains major pathology, current segmentationmethods show poor performance. In this paper, we propose a novel low-ranktensor decomposition (LRTD) based multi-atlas segmentation (MAS) framework thatachieves accurate and robust pathological liver segmentation of CT images.Firstly, we propose a multi-slice LRTD scheme to recover the underlyinglow-rank structure embedded in 3D medical images. It performs the LRTD on smallimage segments consisting of multiple consecutive image slices. Then, wepresent an LRTD-based atlas construction method to generate tumor-free liveratlases that mitigates the performance degradation of liver segmentation due tothe presence of tumors. Finally, we introduce an LRTD-based MAS algorithm toderive patient-specific liver atlases for each test image, and to achieveaccurate pairwise image registration and label propagation. Extensiveexperiments on three public databases of pathological liver cases validate theeffectiveness of the proposed method. Both qualitative and quantitative resultsdemonstrate that, in the presence of major pathology, the proposed method ismore accurate and robust than state-of-the-art methods.

Multi-Slice Low-Rank Tensor Decomposition Based Multi-Atlas Segmentation: Application to Automatic Pathological Liver CT Segmentation