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

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。

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

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 APS 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.}