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Automated liver segmentation from a postmortem CT scan based on a statistical shape model.

Atsushi Saito¹, Seiji Yamamoto², Shigeru Nawano³

¹Tokyo University of Agriculture and Technology, Koganei, Tokyo, Japan. a-saito@go.tuat.ac.jp.

International Journal of Computer Assisted Radiology and Surgery

|September 24, 2016

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This study presents a new algorithm for automated liver segmentation in postmortem CT scans using a statistical shape model (SSM). The method accurately identifies liver location and shape, improving segmentation accuracy for challenging postmortem cases.

Area of Science:

Medical Imaging
Computational Anatomy
Radiology

Background:

Automated liver segmentation in postmortem CT (PMCT) is difficult due to significant tissue deformation and intensity variations.
Existing methods struggle with the unique challenges presented by postmortem changes and pathologies.

Purpose of the Study:

To develop a novel algorithm for automated liver segmentation in PMCT volumes.
To address the limitations of current methods by incorporating a statistical shape model (SSM).

Main Methods:

A novel SSM-guided expectation-maximization (EM) algorithm directly estimates liver location and shape parameters.
Graph cuts are used for fine segmentation, constrained by the estimated parameters, avoiding problematic spatial standardization.
The algorithm was trained on 144 in vivo and 32 postmortem livers and validated on 32 PMCT scans.

Keywords:

Autopsy imaging EM algorithm Liver segmentation Postmortem CT Statistical shape model Synthesized-based learning

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Main Results:

The best-performing SSM achieved an average Jaccard index (JI) of 0.8501.
This result significantly outperformed conventional SSMs and previous postmortem liver segmentation techniques.
The algorithm demonstrated accurate estimation of liver location and shape parameters.

Conclusions:

The proposed algorithm effectively performs automated liver segmentation on PMCT volumes.
The SSM-guided EM approach accurately estimates liver parameters, overcoming challenges of postmortem data.
The study validates the algorithm's effectiveness on real postmortem CT datasets.