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

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Weakly supervised anomaly segmentation in retinal OCT images using an adversarial learning approach.

Jing Wang^1,2, Wanyue Li^1,2, Yiwei Chen²

¹Jiangsu Key Laboratory of Medical Optics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Science, Suzhou 215163, China.

Biomedical Optics Express

|September 13, 2021

Summary

This study introduces a weakly supervised deep learning model for retinal lesion segmentation in optical coherence tomography (OCT) images. The novel approach accurately detects lesions in real-time without manual labeling, accelerating diagnosis.

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Area of Science:

Ophthalmology
Medical Imaging
Artificial Intelligence

Background:

Manual lesion segmentation in medical images is time-consuming and requires expertise.
Supervised deep learning models necessitate extensive manual labeling and may miss novel lesions.

Purpose of the Study:

To develop a weakly supervised learning network for accurate lesion segmentation in optical coherence tomography (OCT) images.
To address the limitations of manual segmentation and supervised deep learning in lesion detection.

Main Methods:

A CycleGAN-based weakly supervised learning network was proposed for lesion segmentation in full-width OCT images.
The model reconstructs normal anatomy from abnormal images; lesions are identified by image differences.
A customized architecture and multi-scale perceptual loss were employed to handle shape deformations.

Main Results:

The model achieved 96.94% AUC for image-level anomaly detection and a 0.8239 Dice similarity coefficient for pixel-level lesion detection.
Performance surpassed all comparative methods on an open-source retinal OCT dataset.
Average test time per image was 0.039 seconds, demonstrating real-time capability.

Conclusions:

The proposed weakly supervised model accurately detects and segments retinopathy lesions in real-time without manual labeling.
This method has the potential to expedite clinical diagnosis and reduce misdiagnosis rates in ophthalmology.