Segmentation of choroidal area in optical coherence tomography images using a transfer learning-based conventional neural network: a focus on diabetic retinopathy and a literature review.

IF 2.9 3区 医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING BMC Medical Imaging Pub Date : 2024-10-18 DOI:10.1186/s12880-024-01459-2
Jamshid Saeidian, Hossein Azimi, Zohre Azimi, Parnia Pouya, Hassan Asadigandomani, Hamid Riazi-Esfahani, Alireza Hayati, Kimia Daneshvar, Elias Khalili Pour
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Abstract

Background: This study aimed to evaluate the effectiveness of DeepLabv3+with Squeeze-and-Excitation (DeepLabv3+SE) architectures for segmenting the choroid in optical coherence tomography (OCT) images of patients with diabetic retinopathy.

Methods: A total of 300 B-scans were selected from 21 patients with mild to moderate diabetic retinopathy. Six DeepLabv3+SE variants, each utilizing a different pre-trained convolutional neural network (CNN) for feature extraction, were compared. Segmentation performance was assessed using the Jaccard index, Dice score (DSC), precision, recall, and F1-score. Binarization and Bland-Altman analysis were employed to evaluate the agreement between automated and manual measurements of choroidal area, luminal area (LA), and Choroidal Vascularity Index (CVI).

Results: DeepLabv3+SE with EfficientNetB0 achieved the highest segmentation performance, with a Jaccard index of 95.47, DSC of 98.29, precision of 98.80, recall of 97.41, and F1-score of 98.10 on the validation set. Bland-Altman analysis indicated good agreement between automated and manual measurements of LA and CVI.

Conclusions: DeepLabv3+SE with EfficientNetB0 demonstrates promise for accurate choroid segmentation in OCT images. This approach offers a potential solution for automated CVI calculation in diabetic retinopathy patients. Further evaluation of the proposed method on a larger and more diverse dataset can strengthen its generalizability and clinical applicability.

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使用基于迁移学习的传统神经网络分割光学相干断层扫描图像中的脉络膜区域:聚焦糖尿病视网膜病变及文献综述。
研究背景本研究旨在评估 DeepLabv3+ with Squeeze-and-Excitation (DeepLabv3+SE) 架构在糖尿病视网膜病变患者的光学相干断层扫描(OCT)图像中分割脉络膜的效果:从 21 名轻度至中度糖尿病视网膜病变患者中选取了共计 300 张 B 扫描图像。对六种 DeepLabv3+SE 变体进行了比较,每种变体都使用不同的预训练卷积神经网络(CNN)进行特征提取。分段性能使用 Jaccard 指数、Dice 分数 (DSC)、精确度、召回率和 F1 分数进行评估。采用二值化和Bland-Altman分析来评估脉络膜面积、管腔面积(LA)和脉络膜血管指数(CVI)的自动测量与人工测量之间的一致性:在验证集上,DeepLabv3+SE 与 EfficientNetB0 的分割性能最高,Jaccard 指数为 95.47,DSC 为 98.29,精确度为 98.80,召回率为 97.41,F1 分数为 98.10。Bland-Altman分析表明,LA和CVI的自动测量与手动测量之间具有良好的一致性:DeepLabv3+SE和EfficientNetB0有望在OCT图像中实现准确的脉络膜分割。这种方法为自动计算糖尿病视网膜病变患者的 CVI 提供了一种潜在的解决方案。在更大和更多样化的数据集上对所提出的方法进行进一步评估,可以增强其通用性和临床适用性。
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来源期刊
BMC Medical Imaging
BMC Medical Imaging RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-
CiteScore
4.60
自引率
3.70%
发文量
198
审稿时长
27 weeks
期刊介绍: BMC Medical Imaging is an open access journal publishing original peer-reviewed research articles in the development, evaluation, and use of imaging techniques and image processing tools to diagnose and manage disease.
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