Method for retinal vessel segmentation and coronary artery disease prediction using optical coherence tomography angiography

doi:10.11772/j.issn.1001-9081.2025020220

Journal of Computer Applications ›› 2026, Vol. 46 ›› Issue (2): 640-651.DOI: 10.11772/j.issn.1001-9081.2025020220

• Frontier and comprehensive applications • Previous Articles

Method for retinal vessel segmentation and coronary artery disease prediction using optical coherence tomography angiography

Kejian CUI, Zhiming WANG, Zhaowen QIU()

College of Computer and Control Engineering，Northeast Forestry University，Harbin Heilongjiang 150040，China

Received:2025-03-06 Revised:2025-05-18 Accepted:2025-05-27 Online:2025-08-08 Published:2026-02-10
Contact: Zhaowen QIU
About author:CUI Kejian， born in 2001， M. S. candidate. His research interests include computer vision， medical image processing， deep learning.
WANG Zhiming， born in 1998， M. S. His research interests include computer vision， medical image processing， deep learning.
QIU Zhaowen， born in 1974， Ph. D.， professor. His research interests include artificial intelligence， virtual reality. Email:qiuzw@nefu.edu.cn
Supported by:
Key Research and Development Program of Heilongjiang Province(2023ZX02C10)

基于光学相干断层扫描血管成像的视网膜血管分割与冠心病预测方法

崔克俭, 王志明, 邱兆文()

东北林业大学计算机与控制工程学院，哈尔滨 150040

通讯作者: 邱兆文
作者简介:崔克俭（2001—），男，辽宁盘锦人，硕士研究生，CCF会员，主要研究方向：计算机视觉、医疗影像处理、深度学习
王志明（1998—），男，内蒙古兴安盟人，硕士，主要研究方向：计算机视觉、医疗影像处理、深度学习
邱兆文（1974—），男，黑龙江哈尔滨人，教授，博士，CCF杰出会员，主要研究方向：人工智能、虚拟现实。 Email:qiuzw@nefu.edu.cn
基金资助:
黑龙江省重点研发项目(2023ZX02C10)

Abstract

Abstract:

Addressing the problem that the existing retinal vessel segmentation models lose topological information in 3D feature extraction， leading to branch breakage， poor continuity in 2D segmentation results， and missing cross-modal associations in vascular analysis and disease prediction， a collaborative framework， MA_DNet（Multi-scale topology-Aware Disease Network）， was proposed. The framework consists of an enhanced segmentation model， MA_Net+， and a disease prediction module. Based on MA_Net（Multi-scale topology-Aware Network）， an intermediate feature retraining module was introduced by MA_Net+ to refine vessel structures and reconnect broken branches. Firstly， the GMSF （Gated Multi-Scale Fusion） module was employed to extract multi-scale spatial convolutions and fuse complex branch features， and the ResMamba module was combined to model long-range topological dependencies within vessels， so as to enhance 3D feature representations， thereby suppressing topological breakage in segmentation results effectively. Then， the convolutional layers of 2D module MA_Net+ were used to further optimize the continuity of local vascular structure. Finally， a cascade prediction module was designed， combining morphological parameters with clinical indicators， so as to establish cross-modal associations between image features and Coronary Artery Disease （CAD） risk. Experimental results show that the MA_Net+ framework achieves a Dice score of 93.02% and a Jaccard index of 87.04% on one subset of the OCTA-500 public dataset， with improvements of 0.28 and 0.37 percentage points， respectively， compared to the IPN-V2+（Image Projection Network V2+） model； on another OCTA-500 subset， the MA_Net+ framework achieves the two indicators of 89.76% and 81.52%， respectively， with gains of 0.35 and 0.57 percentage points， respectively； the disease prediction module of the MA_Net+ framework achieves an AUC（Area Under Curve） of 86.23% on a private dataset. It can be seen that MA_DNet framework enhances the continuity of vascular segmentation effectively through 3D topological modeling and multi-scale fusion mechanism； meanwhile， the framework explores cross-modal correlation prediction between retinal images and CAD risks， offering a new solution for non-invasive cardiovascular diagnosis.

Key words: Coronary Artery Disease (CAD) prediction, retinal vessel segmentation, deep learning, non-invasive diagnosis, Optical Coherence Tomography Angiography (OCTA)

摘要：

针对现有的视网膜分割模型在三维特征提取时易丢失血管拓扑信息导致分支断裂、二维分割结果结构连续性不足以及血管分析与疾病预测跨模态关联缺失的三大问题，提出协同框架MA_DNet（Multi-scale topology-Aware Disease Network）。该框架由增强分割模型MA_Net+与疾病预测模块组成。MA_Net+是在所提模型MA_Net（Multi-scale topology-Aware Network）的基础上，通过一个中间特征再训练模块细化并连接断裂的血管分支，从而优化拓扑完整性。首先，通过三维模块GMSF（Gated Multi-Scale Fusion）模块提取多尺度空间卷积和融合复杂分支特征，并结合ResMamba模块建模血管的长程拓扑依赖关系以增强三维特征表征，进而抑制分割结果中拓扑断裂的发生；其次，利用二维模块MA_Net+的卷积层进一步优化局部血管结构的连续性；最后，设计级联预测模块，并将形态学参数与临床指标相结合，从而构建影像特征与冠心病（CAD）风险的跨模态关联。实验结果表明，MA_Net+框架在OCTA-500公开数据集的一个数据子集上的Dice系数和Jaccard指数分别达到了93.02%和87.04%，较IPN-V2+（Image Projection Network V2+）模型分别提升了0.28和0.37个百分点；在OCTA-500的另一子集上MA_Net+框架的两个指标分别为89.76%和81.52%，较IPN-V2+模型分别提升了0.35和0.57个百分点；MA_Net+框架的疾病预测模块在某个私有数据集上的AUC（Area Under Curve）达到了86.23%。可见，MA_DNet框架通过三维拓扑建模与多尺度融合机制，能够有效提升血管分割的连续性；同时，该框架还探索了视网膜影像与CAD风险之间的跨模态关联预测，为无创心血管诊断提供了新方案。

关键词: 冠心病预测, 视网膜血管分割, 深度学习, 非侵入性诊断, 光学相干断层扫描血管成像

CLC Number:

TP391.41

Kejian CUI, Zhiming WANG, Zhaowen QIU. Method for retinal vessel segmentation and coronary artery disease prediction using optical coherence tomography angiography[J]. Journal of Computer Applications, 2026, 46(2): 640-651.

崔克俭, 王志明, 邱兆文. 基于光学相干断层扫描血管成像的视网膜血管分割与冠心病预测方法[J]. 《计算机应用》唯一官方网站, 2026, 46(2): 640-651.

Figures/Tables 15

Fig. 1 Overall framework of MA_DNet

Fig. 2 ResMamba module structure

Fig. 3 GMSF module structure

Tab. 1 Network configuration of MA_Net and MA_Net+ on two subsets

子集	MA_Net					MA_Net+
子集	输入体积	分割块大小	批量大小	迭代次数	模型保存频率	输入图片大小	批量大小	迭代次数	模型保存频率
OCTA_3M	304×304×640	76×76×160	3	25 000	300∶1	304×304	2	3 000	50∶1
OCTA_6M	400×600×640	100×100×160	3	25 000	300∶1	400×400	2	3 000	50∶1

Tab. 2 Ablation experiment results on OCTA_3M subset

基线	GMSF	ResMamba	Dice	Jac	Bacc	Pre	Rec
✓			91.71 $±$ 2.63	84.80 $±$ 4.22	95.36 $±$ 2.01	92.29 $±$ 2.46	89.43 $±$ 4.43
✓		✓	90.55 $±$ 3.27	82.88 $±$ 4.95	94.72 $±$ 2.58	91.27 $±$ 3.12	90.07 $±$ 5.24
✓	✓		92.05 $±$ 2.63	85.38 $±$ 4.18	95.07 $±$ 2.15	93.70 $±$ 1.66	90.58 $±$ 4.33
✓	✓	✓	92.77 $±$ 2.43	86.60 $±$ 3.90	95.67 $±$ 2.07	93.89 $±$ 1.19	91.78 $±$ 4.18

Tab. 2 Ablation experiment results on OCTA_3M subset

基线	GMSF	ResMamba	Dice	Jac	Bacc	Pre	Rec
✓			91.71 $±$ 2.63	84.80 $±$ 4.22	95.36 $±$ 2.01	92.29 $±$ 2.46	89.43 $±$ 4.43
✓		✓	90.55 $±$ 3.27	82.88 $±$ 4.95	94.72 $±$ 2.58	91.27 $±$ 3.12	90.07 $±$ 5.24
✓	✓		92.05 $±$ 2.63	85.38 $±$ 4.18	95.07 $±$ 2.15	93.70 $±$ 1.66	90.58 $±$ 4.33
✓	✓	✓	92.77 $±$ 2.43	86.60 $±$ 3.90	95.67 $±$ 2.07	93.89 $±$ 1.19	91.78 $±$ 4.18

Tab. 3 Ablation experiment results on OCTA_6M subset

基线	GMSF	ResMamba	Dice	Jac	Bacc	Pre	Rec
✓			88.19 $±$ 2.77	78.98 $±$ 4.31	93.22 $±$ 2.18	88.99 $±$ 3.16	87.57 $±$ 4.46
✓		✓	88.82 $±$ 3.07	80.02 $±$ 4.77	92.09 $±$ 2.54	93.46 $±$ 2.51	84.80 $±$ 5.14
✓	✓		89.05 $±$ 2.92	80.38 $±$ 4.56	92.58 $±$ 2.42	92.67 $±$ 2.62	85.88 $±$ 4.90
✓	✓	✓	89.40 $±$ 2.73	80.94 $±$ 4.27	93.81 $±$ 2.09	90.40 $±$ 3.62	88.62 $±$ 4.25

Tab. 3 Ablation experiment results on OCTA_6M subset

基线	GMSF	ResMamba	Dice	Jac	Bacc	Pre	Rec
✓			88.19 $±$ 2.77	78.98 $±$ 4.31	93.22 $±$ 2.18	88.99 $±$ 3.16	87.57 $±$ 4.46
✓		✓	88.82 $±$ 3.07	80.02 $±$ 4.77	92.09 $±$ 2.54	93.46 $±$ 2.51	84.80 $±$ 5.14
✓	✓		89.05 $±$ 2.92	80.38 $±$ 4.56	92.58 $±$ 2.42	92.67 $±$ 2.62	85.88 $±$ 4.90
✓	✓	✓	89.40 $±$ 2.73	80.94 $±$ 4.27	93.81 $±$ 2.09	90.40 $±$ 3.62	88.62 $±$ 4.25

Tab. 4 Comparison results of proposed and other OCTA models on OCTA_3M subset

模型	Dice	Jac	Bacc
U-Net^［42］	88.55±3.43	79.62±5.08	93.31±1.68
U-Net++^［43］	88.61±3.07	79.67±4.59	91.93±1.74
Attention U-Net^［44］	88.71±3.19	79.84±4.74	93.14±1.49
Joint-seg^［45］	91.13±2.09	83.78±3.40	—
IPN^［30］	90.62±5.96	83.25±7.78	93.87±4.19
IPN-V2^［31］	92.46±3.93	86.19±5.83	95.34±3.16
IPN-V2+^［31］	92.74±3.95	86.67±5.88	95.22±3.12
FARGO^［47］	91.68±2.05	84.70±3.34	—
MA_Net	92.77±2.43	86.60±3.90	95.67±2.07
MA_Net+	93.02±2.40	87.04±3.89	95.80±2.00

Tab. 5 Comparison results of proposed and other OCTA models on OCTA_6M subset

模型	Dice	Jac	Bacc
U-Net^［42］	88.55±3.43	79.62±5.08	93.31±1.68
U-Net++^［43］	88.61±3.07	79.67±4.59	91.93±1.74
Attention U-Net^［44］	88.71±3.19	79.84±4.74	93.14±1.49
Joint-seg^［45］	89.72±3.21	81.17±3.11	—
IPN^［30］	88.64±3.21	79.73±4.92	93.07±2.42
IPN-V2^［31］	89.08±2.73	80.41±4.29	93.52±2.13
IPN-V2+^［31］	89.41±2.74	80.95±4.32	93.46±2.12
PAENet^［46］	89.36±2.70	80.43±4.15	94.05±1.95
PAENet+^［46］	89.69±2.77	81.42±4.39	93.68±2.08
FARGO^［47］	89.15±2.39	80.50±3.75	—
MA_Net	89.40±2.73	80.94±4.27	93.81±2.09
MA_Net+	89.76±2.64	81.52±4.16	94.09±1.77

Fig. 4 Visual comparison of segmentation results on OCTA_3M subset using different models

Fig. 5 Visual comparison of segmentation results on OCTA_6M subset using different models

Tab. 6 Model complexity comparison of existing networks on OCTA-500 dataset

模型	总参数量/10⁶	模型	总参数量/10⁶
IPN-V2^［31］	2.25	IPN-V2+ResMamba	2.33
IPN-V2+^［31］	3.81	MA_Net	3.36
IPN-V2+GMSF	2.34	MA_Net+	4.86

Fig. 6 Violin plots of Dice coefficient of various models

Tab. 7 Prediction results of CAD

指标	数值	指标	数值
准确率	71.93	Bacc	71.71
AUC	86.23	召回率	69.23

Fig. 7 Violin plot of coronary artery disease prediction

Fig. 8 ROC curves and AUC values for coronary artery disease

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Method for retinal vessel segmentation and coronary artery disease prediction using optical coherence tomography angiography

基于光学相干断层扫描血管成像的视网膜血管分割与冠心病预测方法

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