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A Boundary-Enhanced Liver Segmentation Network for Multi-Phase CT Images with Unsupervised Domain Adaptation.

Swathi Ananda¹, Rahul Kumar Jain¹, Yinhao Li¹

¹Graduate School of Information Science and Engineering, Ritsumeikan University, Kusatsu-shi 525-0058, Japan.

Bioengineering (Basel, Switzerland)

|August 26, 2023

Summary

This summary is machine-generated.

This study introduces a novel dual discriminator-based unsupervised domain adaptation (DD-UDA) method for accurate liver segmentation in multi-phase CT images. The approach overcomes annotation challenges and poor contrast, significantly improving segmentation accuracy without requiring multi-phase annotations.

Keywords:

boundary enhancement deep learning liver segmentation multi-phase CT image unsupervised domain adaptation

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Area of Science:

Medical Imaging
Computer Vision
Artificial Intelligence

Background:

Multi-phase computed tomography (CT) is crucial for diagnosing hepatic diseases.
Liver segmentation in multi-phase CT faces challenges including extensive annotation requirements and poor contrast in certain phases.
Existing methods struggle with domain shift across different CT phases, necessitating multi-phase annotations.

Purpose of the Study:

To develop an effective liver segmentation method for multi-phase CT images that addresses annotation burden and contrast issues.
To propose a dual discriminator-based unsupervised domain adaptation (DD-UDA) framework to enable segmentation without multi-phase annotations.
To enhance segmentation accuracy by improving boundary recognition in low-contrast CT images.

Main Methods:

Proposed a dual discriminator-based unsupervised domain adaptation (DD-UDA) network for liver segmentation.
Implemented feature-level and output-level discriminators to reduce domain distribution differences.
Introduced a boundary-enhanced decoder to improve recognition of liver boundaries in challenging contrast phases.

Main Results:

The DD-UDA method achieved superior liver segmentation performance on the target MPCT-FLLs dataset compared to baseline UDA and other state-of-the-art methods.
Demonstrated significant improvements in Intersection over Union (IoU) scores across different phases (PV, ART, NC) without multi-phase annotations.
Achieved IoU scores of 0.823 (PV), 0.811 (ART), and 0.800 (NC), outperforming the baseline's 0.785, 0.796, and 0.772 respectively.

Conclusions:

The proposed DD-UDA method effectively addresses the challenges of liver segmentation in multi-phase CT images.
Unsupervised domain adaptation combined with boundary enhancement significantly improves segmentation accuracy and reduces annotation labor.
The method shows strong potential for clinical application in hepatic disease diagnosis using multi-phase CT scans.