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Semi-supervised Liver Segmentation Based On Local Regions Self-supervision.

Home
Semi-supervised Liver Segmentation Based On Local Regions Self-supervision.

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Semi-supervised liver segmentation based on local regions self-supervision.

Qiong Lou¹, Tingyi Lin¹, Yaguan Qian¹

¹School of Science, Zhejiang University of Science and Technology, Hangzhou, China.

Medical Physics

|December 18, 2023

View abstract on PubMed

Summary

This summary is machine-generated.

This study introduces a novel semi-supervised liver segmentation method using contrastive learning and local region self-supervision (LRS²). The approach effectively utilizes unreliable predictions, improving Dice coefficients by up to 6.11% compared to supervised methods.

Keywords:

contrastive learning liver segmentation morphological operations semi‐supervised learning

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Area of Science:

Medical Imaging
Artificial Intelligence
Computer Vision

Background:

Semi-supervised learning reduces annotation needs in medical image segmentation.
Contrastive learning aids in utilizing unreliable predictions but can neglect anatomical structures.
Integrating anatomical context is crucial for optimizing medical image segmentation.

Purpose of the Study:

To propose a novel semi-supervised approach for liver segmentation using contrastive learning.
To enhance the effectiveness of contrastive learning for medical image segmentation tasks.
To leverage unlabeled data for improved liver segmentation accuracy.

Main Methods:

Proposed a semi-supervised contrastive learning method with local regions self-supervision (LRS²).

Utilized Shannon entropy to differentiate reliable and unreliable predictions, reducing representational dissimilarity within regional units.

Introduced a dynamic reliability threshold and applied morphological operations (erosion, dilation) for refined sample selection.

Main Results:

The LRS² method demonstrated satisfactory performance by effectively exploiting unreliable predictions.
Achieved significant improvements in Dice coefficients compared to supervised VNet, with gains up to +6.11%.
Reported Dice coefficients ranging from 93.31% to 95.12% across different labeled data proportions.

Conclusions:

The proposed method successfully selects positive and negative samples from reliable regions, assigning anchor pixels in unreliable regions correctly.
Incorporating anatomical structure through regional partitioning enhances the precision of sample information capture.
Extensive experiments validate the effectiveness of the LRS² method for semi-supervised liver segmentation.