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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Semi-supervised 3D retinal fluid segmentation via correlation mutual learning with global reasoning attention.

Kaizhi Cao¹, Yi Liu², Xinhao Zeng¹

¹School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.

Biomedical Optics Express

|December 16, 2024

Summary

This study introduces a new semi-supervised method for segmenting 3D fluid lesions in optical coherence tomography (OCT) for diabetic macular edema (DME) diagnosis. The approach improves accuracy by leveraging correlation mutual learning and attention mechanisms, outperforming existing methods.

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Area of Science:

Ophthalmology
Medical Imaging
Artificial Intelligence

Background:

Accurate 3D segmentation of fluid lesions in optical coherence tomography (OCT) is vital for diagnosing diabetic macular edema (DME).
Challenges include high spatial complexity and limited annotated data for 3D segmentation.
Existing methods struggle with the intricacies of 3D retinal lesion segmentation.

Purpose of the Study:

To develop a novel semi-supervised strategy for 3D segmentation of diabetic macular edema lesions in OCT images.
To address the challenges of high-dimensional complexity and data scarcity in 3D segmentation.
To improve the accuracy and efficiency of DME lesion segmentation using advanced AI techniques.

Main Methods:

Proposed a semi-supervised strategy employing a correlation mutual learning framework.
Integrated a shared encoder with three parallel decoders to identify and represent uncertainty in unlabeled data.
Incorporated a global reasoning attention module for transferring label prior knowledge to unlabeled data.
Implemented a correlation mutual learning scheme for enforcing consistency between decoder outputs and pseudo-labels.

Main Results:

The proposed method demonstrated superior performance compared to state-of-the-art (SOTA) techniques.
Achieved accurate 3D segmentation of fluid lesions in OCT images.
Effectively handled higher-dimensional spatial complexity and limited annotated data.

Conclusions:

The novel semi-supervised framework shows significant potential for 3D retinal lesion segmentation.
The correlation mutual learning approach enhances segmentation accuracy for DME in OCT.
This method offers a promising solution for improving early diagnosis and management of DME.