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

Simple item page
dc.contributor.authorSakunpaisanwari, Littikrai
dc.contributor.authorYodrabum, Nutcha
dc.contributor.authorSirirapisit, Tanongchai
dc.contributor.authorTitijaroonroj, Taravichet
dc.date.accessioned2026-08-06T10:34:21Z
dc.date.available2026-08-06T10:34:21Z
dc.date.issued2022-01-01
dc.description.abstractBlood 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.
dc.identifier.citationIcsec 2022 International Computer Science and Engineering Conference 2022, 14-19, 2022
dc.identifier.doi10.1109/ICSEC56337.2022.10049364
dc.identifier.other2-s2.0-85149620159
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/12501
dc.sourceIcsec 2022 International Computer Science and Engineering Conference 2022
dc.subjectBlood vessels detection
dc.subjectCNN
dc.subjectCT scan
dc.subjectObject detection
dc.subjectYOLO
dc.titleBlood Vessels Detection by Regional-based CNN for CT Scan of Lower Extremities
dc.typeConference Paper

Files

Collections

Research