PURPOSE: To compare automated segmentation of the quadratus lumborum (QL) based on statistical shape modeling (SSM) with conventional manual processing of magnetic resonance (MR) images for segmentation of this paraspinal muscle. MATERIALS AND METHODS: The automated SSM scheme for QL segmentation was developed using an MR database of 7 mm axial images of the lumbar region from 20 subjects (cricket fast bowlers and athletic controls). Specifically, a hierarchical 3D-SSM scheme for segmentation of the QL, and surrounding psoas major (PS) and erector spinae+multifidus (ES+MT) musculature, was implemented after image preprocessing (bias field correction, partial volume interpolation) followed by image registration procedures to develop average and probabilistic MR atlases for initializing and constraining the SSM segmentation of the QL. The automated and manual QL segmentations were compared using spatial overlap and average surface distance metrics. RESULTS: The spatial overlap between the automated SSM and manual segmentations had a median Dice similarity metric of 0.87 (mean = 0.86, SD = 0.08) and mean average surface distance of 1.26 mm (SD = 0.61) and 1.32 mm (SD = 0.60) for the right and left QL muscles, respectively. CONCLUSION: The current SSM scheme represents a promising approach for future automated morphometric analyses of the QL and other paraspinal muscles from MR images.
PURPOSE: To compare automated segmentation of the quadratus lumborum (QL) based on statistical shape modeling (SSM) with conventional manual processing of magnetic resonance (MR) images for segmentation of this paraspinal muscle. MATERIALS AND METHODS: The automated SSM scheme for QL segmentation was developed using an MR database of 7 mm axial images of the lumbar region from 20 subjects (cricket fast bowlers and athletic controls). Specifically, a hierarchical 3D-SSM scheme for segmentation of the QL, and surrounding psoas major (PS) and erector spinae+multifidus (ES+MT) musculature, was implemented after image preprocessing (bias field correction, partial volume interpolation) followed by image registration procedures to develop average and probabilistic MR atlases for initializing and constraining the SSM segmentation of the QL. The automated and manual QL segmentations were compared using spatial overlap and average surface distance metrics. RESULTS: The spatial overlap between the automated SSM and manual segmentations had a median Dice similarity metric of 0.87 (mean = 0.86, SD = 0.08) and mean average surface distance of 1.26 mm (SD = 0.61) and 1.32 mm (SD = 0.60) for the right and left QL muscles, respectively. CONCLUSION: The current SSM scheme represents a promising approach for future automated morphometric analyses of the QL and other paraspinal muscles from MR images.
Authors: Marianna S Thomas; David Newman; Olof Dahlqvist Leinhard; Bahman Kasmai; Richard Greenwood; Paul N Malcolm; Anette Karlsson; Johannes Rosander; Magnus Borga; Andoni P Toms Journal: Eur Radiol Date: 2014-05-29 Impact factor: 5.315
Authors: Thomas Baum; Cristian Lorenz; Christian Buerger; Friedemann Freitag; Michael Dieckmeyer; Holger Eggers; Claus Zimmer; Dimitrios C Karampinos; Jan S Kirschke Journal: Eur Radiol Exp Date: 2018-11-07
Authors: Fanny Buckinx; Francesco Landi; Matteo Cesari; Roger A Fieding; Marjolein Visser; Klaus Engelke; Stefania Maggi; Elaine Dennison; Nasser M Al-Daghri; Sophie Allepaerts; Jurgen Bauer; Ivan Bautmans; Maria-Luisa Brandi; Olivier Bruyère; Tommy Cederholm; Francesca Cerreta; Antonio Cherubini; Cyrus Cooper; Alphonso Cruz-Jentoft; Eugene McCloskey; Bess Dawson-Hughes; Jean-Marc Kaufman; Andrea Laslop; Jean Petermans; Jean-Yves Reginster; René Rizzoli; Sian Robinson; Yves Rolland; Ricardo Rueda; Bruno Vellas; John A Kanis Journal: J Cachexia Sarcopenia Muscle Date: 2018-12 Impact factor: 12.910