Automatic detection and quantification of brain midline shift using anatomical marker model

• Main Authors: LIU, Ruizhe, LI, Shimiao, SU, Bolan, TAN, Chew Lim, Tze-Yun LEONG, PANG, Boon Chuan, LIM, C.C. Tchoyoson, LEE, Cheng Kiang
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2014
• Subjects: Anatomatic marker model; Brain CT diagnosis; Brain midline shift; Midline shift detection and quantification; Artificial Intelligence and Robotics; Computer Sciences; Medicine and Health Sciences
• Online Access: https://ink.library.smu.edu.sg/sis_research/2922; http://dx.doi.org/10.1016/j.compmedimag.2013.11.001
• Institution: Singapore Management University

Brain midline shift (MLS) is a significant factor in brain CT diagnosis. In this paper, we present a new method of automatically detecting and quantifying brain midline shift in traumatic injury brain CT images. The proposed method automatically picks out the CT slice on which midline shift can be o...