title: Body Composition Assessment in Axial CT Images Using FEM-Based Automatic Segmentation of Skeletal Muscle.
abstract: The proportions of muscle and fat tissues in the human body, referred to as body composition is a vital measurement for cancer patients. Body composition has been recently linked to patient survival and the onset/recurrence of several types of cancers in numerous cancer research studies. This paper introduces a fully automatic framework for the segmentation of muscle and fat tissues from CT images to estimate body composition. We developed a novel finite element method (FEM) deformable model that incorporates a priori shape information via a statistical deformation model (SDM) within the template-based segmentation framework. The proposed method was validated on 1000 abdominal and 530 thoracic CT images and we obtained very good segmentation results with Jaccard scores in excess of 90% for both the muscle and fat regions.
authors.forename: Karteek
authors.initials: K
authors.lastname: Popuri
authors.forename: Dana
authors.initials: D
authors.lastname: Cobzas
authors.forename: Nina
authors.initials: N
authors.lastname: Esfandiari
authors.forename: Vickie
authors.initials: V
authors.lastname: Baracos
authors.forename: Martin
authors.initials: M
authors.lastname: Jägersand
mesh_terms: D001823:Body Composition / Q000502:physiology*; D020342:Finite Element Analysis; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D018482:Muscle, Skeletal / Q000000981:diagnostic imaging*; D011860:Radiography, Abdominal; D013902:Radiography, Thoracic / Q000379:methods; D015203:Reproducibility of Results; D014057:Tomography, X-Ray Computed / Q000379:methods*