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

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Advanced 3D retinal lesion segmentation using channel-spatial attention-guided multi-scale feature aggregation.

Guangming Ni¹, Kaizhi Cao¹, Xiaoyang Qin¹

¹School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China.

Biomedical Optics Express

|March 2, 2026

Summary

This summary is machine-generated.

A new deep learning network accurately segments 3D lesions in optical coherence tomography (OCT) scans for diabetic macular edema (DME) and age-related macular degeneration (AMD), improving diagnosis.

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Area of Science:

Ophthalmology
Medical Imaging
Artificial Intelligence

Background:

Diabetic macular edema (DME) and age-related macular degeneration (AMD) are leading causes of vision loss.
Optical coherence tomography (OCT) is vital for diagnosing these conditions.
Current 2D OCT analysis limits 3D lesion information extraction.

Purpose of the Study:

To develop an advanced deep learning network for high-accuracy 3D lesion segmentation in OCT images.
To overcome limitations of 2D OCT analysis by utilizing 3D information.

Main Methods:

Proposed an innovative deep learning network.
Implemented multi-scale feature extraction and aggregation.
Incorporated channel-spatial joint attention mechanisms.

Main Results:

Achieved commendable 3D segmentation performance for DME and AMD lesions.
Demonstrated robust generalization capabilities of the proposed method.
Validated the effectiveness of the deep learning approach on OCT 3D images.

Conclusions:

The novel deep learning network enhances 3D lesion segmentation accuracy in OCT scans.
This method offers improved understanding and clinical convenience for DME and AMD diagnosis.
Facilitates better clinical diagnosis and treatment planning for retinal diseases.