title: Automatic segmentation of the glenohumeral cartilages from magnetic resonance images.
abstract: OBJECTIVE
  Magnetic resonance (MR) imaging plays a key role in investigating early degenerative disorders and traumatic injuries of the glenohumeral cartilages. Subtle morphometric and biochemical changes of potential relevance to clinical diagnosis, treatment planning, and evaluation can be assessed from measurements derived from in vivo MR segmentation of the cartilages. However, segmentation of the glenohumeral cartilages, using approaches spanning manual to automated methods, is technically challenging, due to their thin, curved structure and overlapping intensities of surrounding tissues. Automatic segmentation of the glenohumeral cartilages from MR imaging is not at the same level compared to the weight-bearing knee and hip joint cartilages despite the potential applications with respect to clinical investigation of shoulder disorders. In this work, the authors present a fully automated segmentation method for the glenohumeral cartilages using MR images of healthy shoulders.
  
  
  METHODS
  The method involves automated segmentation of the humerus and scapula bones using 3D active shape models, the extraction of the expected bone-cartilage interface, and cartilage segmentation using a graph-based method. The cartilage segmentation uses localization, patient specific tissue estimation, and a model of the cartilage thickness variation. The accuracy of this method was experimentally validated using a leave-one-out scheme on a database of MR images acquired from 44 asymptomatic subjects with a true fast imaging with steady state precession sequence on a 3 T scanner (Siemens Trio) using a dedicated shoulder coil. The automated results were compared to manual segmentations from two experts (an experienced radiographer and an experienced musculoskeletal anatomist) using the Dice similarity coefficient (DSC) and mean absolute surface distance (MASD) metrics.
  
  
  RESULTS
  Accurate and precise bone segmentations were achieved with mean DSC of 0.98 and 0.93 for the humeral head and glenoid fossa, respectively. Mean DSC scores of 0.74 and 0.72 were obtained for the humeral and glenoid cartilage volumes, respectively. The manual interobserver reliability evaluated by DSC was 0.80 ± 0.03 and 0.76 ± 0.04 for the two cartilages, implying that the automated results were within an acceptable 10% difference. The MASD between the automatic and the corresponding manual cartilage segmentations was less than 0.4 mm (previous studies reported mean cartilage thickness of 1.3 mm).
  
  
  CONCLUSIONS
  This work shows the feasibility of volumetric segmentation and separation of the glenohumeral cartilages from MR images. To their knowledge, this is the first fully automated algorithm for volumetric segmentation of the individual glenohumeral cartilages from MR images. The approach was validated against manual segmentations from experienced analysts. In future work, the approach will be validated on imaging datasets acquired with various MR contrasts in patients.
authors: School of Information Technology and Electrical Engineering, University of Queensland, Brisbane 4072, Australia and The Australian E-Health Research Centre, CSIRO Health and Biosecurity, Brisbane 4029, Australia.
authors: A
authors: A
authors: Neubert
authors: School of Information Technology and Electrical Engineering, University of Queensland, Brisbane 4072, Australia and Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
authors: Z
authors: Z
authors: Yang
authors: School of Human Movement Studies, University of Queensland, Brisbane 4072, Australia.
authors: C
authors: C
authors: Engstrom
authors: School of Information Technology and Electrical Engineering, University of Queensland, Brisbane 4072, Australia.
authors: Y
authors: Y
authors: Xia
authors: School of Information Technology and Electrical Engineering, University of Queensland, Brisbane 4072, Australia.
authors: M W
authors: MW
authors: Strudwick
authors: School of Information Technology and Electrical Engineering, University of Queensland, Brisbane 4072, Australia.
authors: S S
authors: SS
authors: Chandra
authors: The Australian E-Health Research Centre, CSIRO Health and Biosecurity, Brisbane, 4029, Australia.
authors: J
authors: J
authors: Fripp
authors: School of Information Technology and Electrical Engineering, University of Queensland, Brisbane 4072, Australia.
authors: S
authors: S
authors: Crozier
mesh_terms: D000328:Adult; D000465:Algorithms; D001331:Automation; D002358:Cartilage, Articular / Q000000981:diagnostic imaging*; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging; D008297:Male; D012785:Shoulder Joint / Q000000981:diagnostic imaging*; D013997:Time Factors