  Liver segmentation from abdominal CT images is an essential step for liver
cancer computer-aided diagnosis and surgical planning. However, both the
accuracy and robustness of existing liver segmentation methods cannot meet the
requirements of clinical applications. In particular, for the common clinical
cases where the liver tissue contains major pathology, current segmentation
methods show poor performance. In this paper, we propose a novel low-rank
tensor decomposition (LRTD) based multi-atlas segmentation (MAS) framework that
achieves accurate and robust pathological liver segmentation of CT images.
Firstly, we propose a multi-slice LRTD scheme to recover the underlying
low-rank structure embedded in 3D medical images. It performs the LRTD on small
image segments consisting of multiple consecutive image slices. Then, we
present an LRTD-based atlas construction method to generate tumor-free liver
atlases that mitigates the performance degradation of liver segmentation due to
the presence of tumors. Finally, we introduce an LRTD-based MAS algorithm to
derive patient-specific liver atlases for each test image, and to achieve
accurate pairwise image registration and label propagation. Extensive
experiments on three public databases of pathological liver cases validate the
effectiveness of the proposed method. Both qualitative and quantitative results
demonstrate that, in the presence of major pathology, the proposed method is
more accurate and robust than state-of-the-art methods.
