一． 主题：Two Dimensional Paraspinal Muscle Segmentation in CT Images
二． 主讲人：应梦欹
三． 时间：2019年1月14号，上午：10：00—11：00
四． 地点：A4-305
摘要：Abstract—Paraspinal muscles support the spine and are the source of movement force. The size, shape, density and volume of the paraspinal muscles cross section area (CSA) are affected by many factors, such as age, health condition, exercise, and low back pain. It is invaluable to segment the paraspinal muscle regions in images in order to measure and study them. Manual segmentation of the paraspinal muscle CSA is time consuming and inaccurate. In this work, an atlas-based image segmentation algorithm is proposed to segment the paraspinal muscles in CT images. To address the challenges of variations of muscle shape and its relative spatial relationship to other organs, mutual information is utilized to register the atlas and target images, followed by gradient vector flow contour deformation. Experimental results show that the proposed method can successfully segment paraspinal muscle regions in CT images in both intrapatient and interpatient cases. Furthermore, using mutual information to register atlas and targets images outperforms the method using spine-spine registration. It segments the muscle regions accurately without the need of computationally expensive local contour optimization. The results can be used to evaluate paraspinal muscle tissue injury and postoperative back muscle atrophy of spine surgery patients.
个人简介：应梦欹博士现任美国北乔治亚大学数学系副教授，2009年毕业于湖南大学数学与计量经济学院，2014年博士毕业于美国阿拉巴马大学数学系。主要研究约束优化、线性与非线性规划、数学建模与仿真、数学金融、精算科学、统计学等一些与优化相关的方向。
欢迎广大师生参加（联系人：王伟）
