Rapid Organ Localization in 3D Torso CT Images Based on Ensemble Learning
This paper describes a new approach to automatically find out the location of a target solid organ in 3D CT scans. Specifically,our goal is to detect a 3D rectangle for the target organ in a way that this rectangle bounds the organ region tightly and accurately. The proposed approach combines the ensemble learning and the majority voting techniques to achieve a robust detection by using a small number of CT scans for training. A database including 3,329 torso CT scans is used in experiments. Among them,we manually label the heart and the left/right kidneys from nearly 100 3D CT scans as training samples,and use the proposed approach to localize those organs in the other CT scans. Experimental results show that detection rates are 99％ for the heart,8S％-87％ for the right and left kidney,with a computation time less than 15 seconds per CT scan on a general PC.
CT images solid organ localization ensemble learning,majority voting
Xiangrong Zhou Song Wang Huayue Chen Xuejun Zhang Takeshi Hara Ryujiro Yokoyama Masayuki Kanematsu Hiroaki Hoshi Hiroshi Fujita
Graduate School of Medicine,Gifu University,Gifu 501-1194,Japan Department of Computer Science and Engineering,University of South Carolina,USA Radiology Service,Gifu University Hospital,Gifu 501-1194,Japan Department of Radiology,Gifu University Hospital,Gifu 501-1194,Japan
国际会议
南宁
英文
312-315
2010-12-10（万方平台首次上网日期，不代表论文的发表时间）