Blood Vessels Detection by Regional-based CNN for CT Scan of Lower Extremities

Abstract

Blood vessels on computed tomography (CT) scan images are difficult to identify and discriminate between vessels and noise because blood vessels are not only small and shapeless, but its location can also be inconsistent. This is a challenge of object detection. We proposed an automatic blood vessel detection method based on YOLOv3 for object detection from CT scan of lower extremities. This work focused on detecting four main arteries: popliteal, anterior tibial, posterior tibial, and peroneal arteries. To obtain the best architecture for blood vessel detection, we evaluated and compared the performances of seven region-based CNN architectures: Faster R-CNN, Cascade R-CNN, Mask R-CNN, RetinaNet, YOLOv3, CornerNet, and Centernet. Experimental results show that the best architecture was YOLOv3 with precision, recall, and f1-score of 0.982, 0.954, and 0.968, respectively. Good accomplishment of YOLOv3 came from skip connections, multi-scale feature map, and anchor generated by k-means clustering.

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