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

arXiv: https://arxiv.org/abs/2102.12056 · HTML: https://mlsanity.com/paper/2102.12056/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 92 references that are in the map.
- First posted on arXiv 24 Feb 2021.

## Builds on (most cited first, 20 of 20)

- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [VoxelMorph: A Learning Framework for Deformable Medical Image Registration](https://mlsanity.com/paper/1809.05231/) (2018)
- [The Liver Tumor Segmentation Benchmark (LiTS)](https://mlsanity.com/paper/1901.04056/) (2019)
- [Tensor Decomposition for Signal Processing and Machine Learning](https://mlsanity.com/paper/1607.01668/) (2016)
- [Tensor Robust Principal Component Analysis with A New Tensor Nuclear Norm](https://mlsanity.com/paper/1804.03728/) (2018)
- [Multi-Atlas Segmentation of Biomedical Images: A Survey](https://mlsanity.com/paper/1412.3421/) (2014)
- [3D Anisotropic Hybrid Network: Transferring Convolutional Features from 2D Images to 3D Anisotropic Volumes](https://mlsanity.com/paper/1711.08580/) (2017)
- [Tensor Robust Principal Component Analysis: Exact Recovery of Corrupted Low-Rank Tensors via Convex Optimization](https://mlsanity.com/paper/1708.04181/) (2017)
- [Automatic Liver and Tumor Segmentation of CT and MRI Volumes using Cascaded Fully Convolutional Neural Networks](https://mlsanity.com/paper/1702.05970/) (2017)
- [Low-Rank Modeling and Its Applications in Image Analysis](https://mlsanity.com/paper/1401.3409/) (2014)
- [Automatic 3D liver location and segmentation via convolutional neural networks and graph cut](https://mlsanity.com/paper/1605.03012/) (2016)
- [Automatic Breast Ultrasound Image Segmentation: A Survey](https://mlsanity.com/paper/1704.01472/) (2017)
- [Low-dose spectral CT reconstruction using L0 image gradient and tensor dictionary](https://mlsanity.com/paper/1801.01452/) (2017)
- [Hierarchical Convolutional-Deconvolutional Neural Networks for Automatic Liver and Tumor Segmentation](https://mlsanity.com/paper/1710.04540/) (2017)
- [User-Guided Domain Adaptation for Rapid Annotation from User Interactions: A Study on Pathological Liver Segmentation](https://mlsanity.com/paper/2009.02455/) (2020)
- [Basic Ensembles of Vanilla-Style Deep Learning Models Improve Liver Segmentation From CT Images](https://mlsanity.com/paper/2001.09647/) (2020)
- [Neural Network-Based Automatic Liver Tumor Segmentation With Random Forest-Based Candidate Filtering](https://mlsanity.com/paper/1706.00842/) (2017)
- [A Tensor Factorization Method for 3D Super-Resolution with Application to Dental CT](https://mlsanity.com/paper/1807.10027/) (2018)
- [Exact Recovery of Tensor Robust Principal Component Analysis under Linear Transforms](https://mlsanity.com/paper/1907.08288/) (2019)
- [Tensor Low Rank Modeling and Its Applications in Signal Processing](https://mlsanity.com/paper/1912.03435/) (2019)

## Built on by (most cited first, 4 of 4)

- [Towards Quantum Tensor Decomposition in Biomedical Applications](https://mlsanity.com/paper/2502.13140/) (2025)
- [M-GenSeg: Domain Adaptation For Target Modality Tumor Segmentation With Annotation-Efficient Supervision](https://mlsanity.com/paper/2212.07276/) (2022)
- [LiMT: A Multi-task Liver Image Benchmark Dataset](https://mlsanity.com/paper/2511.19889/) (2025)
- [Image-level supervision and self-training for transformer-based cross-modality tumor segmentation](https://mlsanity.com/paper/2309.09246/) (2023)

## Abstract (arXiv)

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.