title: A computational approach to detect and segment cytoplasm in muscle fiber images.
abstract: We developed a computational approach to detect and segment cytoplasm in microscopic images of skeletal muscle fibers. The computational approach provides computer-aided analysis of cytoplasm objects in muscle fiber images to facilitate biomedical research. Cytoplasm in muscle fibers plays an important role in maintaining the functioning and health of muscular tissues. Therefore, cytoplasm is often used as a marker in broad applications of musculoskeletal research, including our search on treatment of muscular disorders such as Duchenne muscular dystrophy, a disease that has no available treatment. However, it is often challenging to analyze cytoplasm and quantify it given the large number of images typically generated in experiments and the large number of muscle fibers contained in each image. Manual analysis is not only time consuming but also prone to human errors. In this work we developed a computational approach to detect and segment the longitudinal sections of cytoplasm based on a modified graph cuts technique and iterative splitting method to extract cytoplasm objects from the background. First, cytoplasm objects are extracted from the background using the modified graph cuts technique which is designed to optimize an energy function. Second, an iterative splitting method is designed to separate the touching or adjacent cytoplasm objects from the results of graph cuts. We tested the computational approach on real data from in vitro experiments and found that it can achieve satisfactory performance in terms of precision and recall rates.
authors.affiliation: Key Laboratory of Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, China.|Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Zhejiang University, Hangzhou, China.
authors.forename: Yanen
authors.initials: Y
authors.lastname: Guo
authors.affiliation: Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts.
authors.forename: Xiaoyin
authors.initials: X
authors.lastname: Xu
authors.affiliation: Key Laboratory of Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, China.|Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Zhejiang University, Hangzhou, China.
authors.forename: Yuanyuan
authors.initials: Y
authors.lastname: Wang
authors.affiliation: Department of Clinical Hematology, Southwestern Hospital, Third Military Medical University, Chongqing, China.
authors.forename: Zhong
authors.initials: Z
authors.lastname: Yang
authors.affiliation: Department of Anesthesia, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts.
authors.forename: Yaming
authors.initials: Y
authors.lastname: Wang
authors.affiliation: Key Laboratory of Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, China.|Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Zhejiang University, Hangzhou, China.
authors.forename: Shunren
authors.initials: S
authors.lastname: Xia
mesh_terms: D000818:Animals; D003593:Cytoplasm / Q000648:ultrastructure*; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008297:Male; D008810:Mice, Inbred C57BL; D018485:Muscle Fibers, Skeletal / Q000648:ultrastructure*
keywords: cytoplasm; graph cuts; image segmentation; iterative splitting; muscle fiber images