title: Automated image segmentation of haematoxylin and eosin stained skeletal muscle cross-sections.
abstract: The ability to accurately and efficiently quantify muscle morphology is essential to determine the physiological relevance of a variety of muscle conditions including growth, atrophy and repair. There is agreement across the muscle biology community that important morphological characteristics of muscle fibres, such as cross-sectional area, are critical factors that determine the health and function (e.g. quality) of the muscle. However, at this time, quantification of muscle characteristics, especially from haematoxylin and eosin stained slides, is still a manual or semi-automatic process. This procedure is labour-intensive and time-consuming. In this paper, we have developed and validated an automatic image segmentation algorithm that is not only efficient but also accurate. Our proposed automatic segmentation algorithm for haematoxylin and eosin stained skeletal muscle cross-sections consists of two major steps: (1) A learning-based seed detection method to find the geometric centres of the muscle fibres, and (2) a colour gradient repulsive balloon snake deformable model that adopts colour gradient in Luv colour space. Automatic quantification of muscle fibre cross-sectional areas using the proposed method is accurate and efficient, providing a powerful automatic quantification tool that can increase sensitivity, objectivity and efficiency in measuring the morphometric features of the haematoxylin and eosin stained muscle cross-sections.
authors.affiliation: Division of Biomedical Informatics, Department of Biostatistics, University of Kentucky, Lexington, Kentucky, 40536, U.S.A.; Department of Computer Science, University of Kentucky, Lexington, Kentucky, 40536, U.S.A.
authors.forename: F
authors.initials: F
authors.lastname: Liu
authors.forename: A L
authors.initials: AL
authors.lastname: Mackey
authors.forename: R
authors.initials: R
authors.lastname: Srikuea
authors.forename: K A
authors.initials: KA
authors.lastname: Esser
authors.forename: L
authors.initials: L
authors.lastname: Yang
mesh_terms: D000886:Anthropometry / Q000379:methods; D057205:Automation, Laboratory / Q000379:methods*; D006651:Histocytochemistry / Q000379:methods*; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008853:Microscopy / Q000379:methods*; D018482:Muscle, Skeletal / Q000033:anatomy & histology* / Q000166:cytology*; D012680:Sensitivity and Specificity
keywords: Cross-sections; learning; muscle; segmentation