Learning‐based automatic segmentation of arteriovenous malformations on contrast CT images in brain stereotactic radiosurgery

• Published in: Medical physics (Lancaster) Vol. 46; no. 7; pp. 3133 - 3141
• Main Authors: Wang, Tonghe, Lei, Yang, Tian, Sibo, Jiang, Xiaojun, Zhou, Jun, Liu, Tian, Dresser, Sean, Curran, Walter J., Shu, Hui‐Kuo, Yang, Xiaofeng
• Format: Journal Article
• Language: English
• Published: United States 01.07.2019
• Subjects: Arteriovenous Malformations - diagnostic imaging; Automation; AVM; Brain - diagnostic imaging; Brain - radiation effects; Contrast Media; deep learning; Humans; Image Processing, Computer-Assisted - methods; Machine Learning; Radiosurgery; segmentation; Tomography, X-Ray Computed; AVM; CT; deep learning; segmentation

Purpose Stereotactic radiosurgery (SRS) is widely used to obliterate arteriovenous malformations (AVMs). Its performance relies on the accuracy of delineating the target AVM. Manual segmentation during a framed SRS procedure is time consuming and subject to inter‐ and intraobserver variation. To add...