Image detection algorithm of cerebral arterial stenosis by improved Libra region-convolutional neural network

doi:10.11772/j.issn.1001-9081.2021071206

Journal of Computer Applications ›› 2022, Vol. 42 ›› Issue (9): 2909-2916.DOI: 10.11772/j.issn.1001-9081.2021071206

• Multimedia computing and computer simulation • Previous Articles Next Articles

Image detection algorithm of cerebral arterial stenosis by improved Libra region-convolutional neural network

Hanqing LIU¹, Xiaodong KANG¹(), Fuqing ZHANG², Xiuyuan ZHAO², Jingyi YANG¹, Xiaotian WANG¹, Mengfan LI³

^1.School of Medical Imaging，Tianjin Medical University，Tianjin 300202，China
^2.The Second Affiliated Hospital，Tianjin Medical University，Tianjin 300211，China
^3.School of Advanced Technology，Xi’an Jiaotong?Liverpool University，Suzhou Jiangsu 215123，China

Received:2021-07-12 Revised:2021-09-15 Accepted:2021-09-22 Online:2022-09-19 Published:2022-09-10
Contact: Xiaodong KANG
About author:LIU Hanqing， born in 1997， M. S. candidate. His research interests include medical image processing.
ZHANG Fuqing， born in 1977， M. S.， associate professor of medicine. His research interests include prevention and treatment of cerebrovascular diseases.
YANG Jingyi， born in 1999. Her research interests include medical image processing.
WANG Xiaotian， born in 1998. His research interests include medical image processing.
LI Mengfan， born in 1996， M. S. candidate. His research interests include computer vision， computer-aided medical imaging.
Supported by:
Beijing-Tianjin-Hebei Collaborative Innovation Project(17YEXTZC00020)

改进的Libra区域卷积神经网络的脑动脉狭窄影像学检测算法

刘汉卿¹, 康晓东¹(), 张福青², 赵秀圆², 杨靖怡¹, 王笑天¹, 李梦凡³

^1.天津医科大学医学影像学院, 天津 300202
^2.天津医科大学第二附属医院, 天津 300211
^3.西交利物浦大学智能工程学院, 江苏苏州 215123

通讯作者: 康晓东
作者简介:刘汉卿（1997—），男，湖南衡阳人，硕士研究生，主要研究方向：医学图像处理；
张福青（1977—），男，天津人，副主任医师，硕士，主要研究方向：脑血管疾病的预防与治疗；
赵秀圆（1993—），女，天津人，医师，硕士，主要研究方向：脑血管疾病的预防与治疗；
杨靖怡（1999—），女，天津人，主要研究方向：医学图像处理；
王笑天（1998—），男，甘肃兰州人，主要研究方向：医学图像处理；
李梦凡（1996—），男，陕西西安人，硕士研究生，主要研究方向：计算机视觉、计算机辅助医学成像。
基金资助:
京津冀协同创新项目(17YEXTZC00020)

Abstract

Abstract:

In view of the problems of vascular pleomorphism on transverse sections and sampling imbalance in the process of detection， an improved Libra Region-Convolutional Neural Network （R-CNN） cerebral arterial stenosis detection algorithm was proposed to detect internal carotid artery and vertebral artery stenosis in Computed Tomography Angiography （CTA） images. Firstly， ResNet50 was used as the backbone network in Libra R-CNN， Deformable Convolutional Network （DCN） was introduced into the 3， 4， 5 stages of backbone network， and the offsets were learnt to extract the morphological features of blood vessels on different transverse sections. Secondly， the feature maps extracted from the backbone network were input into Balanced Feature Pyramid （BFP） with the Non-local Neural Network （Non-local NN） introduced for deeper feature fusion. Finally， the fused feature maps were input to the cascade detector， and the final detection result was optimized by increasing the Intersection-over-Union （IoU） threshold. Experimental results show that compared with Libra R-CNN algorithm， the improved Libra R-CNN detection algorithm increases 4.3， 1.3， 6.9 and 4.0 percentage points respectively in AP， AP₅₀， AP₇₅ and AP_S， respectively_{on the cerebral artery CTA dataset； on the public CT dataset of colon polyps， the improved Libra R-CNN detection algorithm has the AP， AP₅₀， AP₇₅ and AP_S increased by 6.6， 3.6， 13.0 and 6.4 percentage points， respectively. By adding DCN， Non-local NN and cascade detector to the backbone network of Libra R-CNN algorithm， the features are further fused to learn the semantic information of cerebral artery structure and make the results of narrow area detection more accurate， and the improved algorithm has the ability of generalization in different detection tasks.}

Key words: Libra Region-Convolutional Neural Network (R-CNN), Deformable Convolutional Network (DCN), Non-local Neural Network (Non-local NN), cascade detector, cerebral artery stenosis

摘要：

针对断层面上血管的多形性和检测过程中出现的采样不均衡的问题，提出一种改进的Libra区域卷积神经网络（R-CNN）的脑动脉狭窄影像学检测算法，用于检测计算机断层扫描血管造影（CTA）图像的颈内动脉和椎动脉狭窄。首先，在目标检测网络Libra R-CNN中以ResNet50为骨干网络，并分别在骨干网络的3、4、5阶段引入可变卷积网络（DCN），通过学习偏移量提取血管在不同断层面的形态特征；然后，将从骨干网络中提取的特征图输入至引入非局部神经网络（Non-local NN）的平衡特征金字塔（BFP）中进行更深度的特征融合；最后，将融合后的特征图输入至级联检测器，并通过提高交并比（IoU）阈值优化最终检测结果。实验结果表明，改进的Libra R-CNN检测算法相比Libra R-CNN算法，在脑动脉CTA数据集中平均准确率（AP）、AP₅₀、AP₇₅和AP_S分别提升了4.3、1.3、6.9和4.0个百分点；在公开的结肠息肉CT数据集中，AP、AP₅₀、AP₇₅和AP_S分别提升了6.6、3.6、13.0和6.4个百分点。通过在Libra R-CNN的骨干网络中加入DCN、Non-local NN和级联检测器，进一步融合特征从而学习脑动脉血管结构的语义信息，使得狭窄区域检测结果更精确，且改进算法在不同的检测任务中具有泛化能力。

关键词: Libra区域卷积神经网络, 可变卷积网络, 非局部神经网络, 级联检测器, 脑动脉狭窄

CLC Number:

TP391.41

Hanqing LIU, Xiaodong KANG, Fuqing ZHANG, Xiuyuan ZHAO, Jingyi YANG, Xiaotian WANG, Mengfan LI. Image detection algorithm of cerebral arterial stenosis by improved Libra region-convolutional neural network[J]. Journal of Computer Applications, 2022, 42(9): 2909-2916.

刘汉卿, 康晓东, 张福青, 赵秀圆, 杨靖怡, 王笑天, 李梦凡. 改进的Libra区域卷积神经网络的脑动脉狭窄影像学检测算法[J]. 《计算机应用》唯一官方网站, 2022, 42(9): 2909-2916.

Figures/Tables 13

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算法	Backbone	FPS	AP/%	AP₅₀/%	AP₇₅/%	AP_S/%
Faster R-CNN^［9］	ResNet50	37.7	47.3	96.2	31.3	47.3
YOLOv3^［25］	ResNet50	56.2	43.9	95.9	24.6	43.9
Libra R-CNN^［21］	ResNet50	33.7	47.6	96.0	35.8	47.6
Cascade R-CNN^［24］	ResNet50	26.1	49.8	94.8	41.8	49.8
本文算法	ResNet50	22.9	51.9	97.3	42.7	51.6

算法	Backbone	FPS	AP/%	AP₅₀/%	AP₇₅/%	AP_S/%
Faster R-CNN^［9］	ResNet50	37.7	47.3	96.2	31.3	47.3
YOLOv3^［25］	ResNet50	56.2	43.9	95.9	24.6	43.9
Libra R-CNN^［21］	ResNet50	33.7	47.6	96.0	35.8	47.6
Cascade R-CNN^［24］	ResNet50	26.1	49.8	94.8	41.8	49.8
本文算法	ResNet50	22.9	51.9	97.3	42.7	51.6

算法	FPS	AP/%	AP₅₀/%	AP₇₅/%	AP_S/%
Libra R-CNN	33.7	47.6	96.0	35.8	47.6
Libra R-CNN+DCN	32.0	47.8	95.2	36.8	47.9
Libra R-CNN+DCN+Cascade	22.7	50.5	95.9	42.8	50.8
Libra R-CNN+DCN+Cascade+Non-Local NN	22.9	51.9	97.3	42.7	51.6

算法	FPS	AP/%	AP₅₀/%	AP₇₅/%	AP_S/%
Libra R-CNN	33.7	47.6	96.0	35.8	47.6
Libra R-CNN+DCN	32.0	47.8	95.2	36.8	47.9
Libra R-CNN+DCN+Cascade	22.7	50.5	95.9	42.8	50.8
Libra R-CNN+DCN+Cascade+Non-Local NN	22.9	51.9	97.3	42.7	51.6

算法	Backbone	FPS	AP/%	AP₅₀/%	AP₇₅/%	AP_S/%
Faster R-CNN^［9］	ResNet50	36.7	52.0	94.1	54.0	53.8
YOLOv3^［25］	ResNet50	57.4	47.2	92.2	47.0	37.8
Libra R-CNN^［21］	ResNet50	33.2	53.2	95.7	53.9	53.4
Cascade R-CNN^［24］	ResNet50	24.8	56.1	91.0	63.4	56.1
本文算法	ResNet50	22.3	59.8	99.3	66.9	59.8

Image detection algorithm of cerebral arterial stenosis by improved Libra region-convolutional neural network

改进的Libra区域卷积神经网络的脑动脉狭窄影像学检测算法

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Figures/Tables 13

References 25

Related Articles 15

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Metrics

算法	FPS	AP/%	AP₅₀/%	AP₇₅/%	AP_S/%
Libra R-CNN	33.2	53.2	95.7	53.9	53.4
Libra R-CNN+DCN	32.1	53.9	91.4	55.0	53.3
Libra R-CNN+DCN+Cascade	23.0	55.6	92.5	58.2	55.6
Libra R-CNN+DCN+ Cascade+Non-Local	22.3	59.8	99.3	66.9	59.8

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