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Multi-slice low-rank tensor decomposition based multi-atlas segmentation: Application to automatic pathological liver

Changfa Shi¹, Min Xian², Xiancheng Zhou³

¹Mobile E-business Collaborative Innovation Center of Hunan Province, Hunan University of Technology and Business, Changsha 410205, China; Department of Computer Science, Utah State University, Logan, UT 84322, USA.

Medical Image Analysis

|July 19, 2021

Summary

This summary is machine-generated.

This study introduces a new method using low-rank tensor decomposition (LRTD) for accurate pathological liver segmentation in CT scans, improving computer-aided diagnosis and surgical planning.

Keywords:

-product Low-rank tensor decomposition Multi-atlas segmentation Pathological liver segmentation Tensor robust PCA

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Area of Science:

Medical Imaging
Computer-Aided Diagnosis
Image Segmentation

Background:

Accurate liver segmentation in abdominal CT images is crucial for clinical applications like cancer diagnosis and surgical planning.
Existing segmentation methods struggle with accuracy and robustness, especially when liver pathology is present.
Pathological conditions significantly degrade the performance of current liver segmentation techniques.

Purpose of the Study:

To develop a novel framework for accurate and robust pathological liver segmentation in CT images.
To address the limitations of existing methods in handling complex clinical cases with liver pathology.
To enhance computer-aided diagnosis and surgical planning through improved liver segmentation.

Main Methods:

Proposed a multi-slice low-rank tensor decomposition (LRTD) scheme to recover underlying structures in 3D medical images.
Developed an LRTD-based atlas construction method to generate tumor-free liver atlases, mitigating tumor-related segmentation degradation.
Introduced an LRTD-based multi-atlas segmentation (MAS) algorithm for patient-specific atlas generation, accurate registration, and label propagation.

Main Results:

The proposed LRTD-based MAS framework achieved accurate and robust pathological liver segmentation.
Experiments on public databases demonstrated superior performance compared to state-of-the-art methods, particularly in cases with major pathology.
Both qualitative and quantitative evaluations confirmed the effectiveness of the novel approach.

Conclusions:

The proposed LRTD-based MAS framework offers a significant advancement in pathological liver segmentation from CT images.
This method shows promise for improving the accuracy and robustness required for clinical applications.
The findings suggest a potential for enhanced liver cancer computer-aided diagnosis and surgical planning.