Blood Vessels Detection by Regional-based CNN for CT Scan of Lower Extremities
Issued Date
2022-01-01
Resource Type
Scopus ID
2-s2.0-85149620159
Journal Title
ICSEC 2022 - International Computer Science and Engineering Conference 2022
Start Page
14
End Page
19
Rights Holder(s)
SCOPUS
Bibliographic Citation
ICSEC 2022 - International Computer Science and Engineering Conference 2022 (2022) , 14-19
Suggested Citation
Sakunpaisanwari L., Yodrabum N., Sirirapisit T., Titijaroonroj T. Blood Vessels Detection by Regional-based CNN for CT Scan of Lower Extremities. ICSEC 2022 - International Computer Science and Engineering Conference 2022 (2022) , 14-19. 19. doi:10.1109/ICSEC56337.2022.10049364 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/84307
Title
Blood Vessels Detection by Regional-based CNN for CT Scan of Lower Extremities
Other Contributor(s)
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.
