Computational Methods and Clinical Applications for Spine Imaging
6th International Workshop and Challenge, CSI 2019, Shenzhen, China, October 17, 2019, Proceedings
Published on 31. January 2020
XII, 120 pages
978-3-030-39752-4 (ISBN)
Description
This book constitutes the proceedings of the 7th International Workshop and Challenge on Computational Methods and Clinical Applications for Spine Imaging, CSI 2019, which was held in conjunction with MICCAI on October 17, 2019, in Shenzhen, China. All submissions were accepted for publication; the book contains 5 peer-reviewed regular papers, covering topics of vertrebra detection, spine segmentation and image-based diagnosis, and 9 challenge papers, investigating (semi-)automatic spinal curvature estimation algorithms and providing a standard evaluation framework with a set of x-ray images.
More details
Series
Language
English
Place of publication
Cham
Switzerland
Publishing group
Springer International Publishing
Product notice
Reflowable
Illustrations
XII, 120 p. 63 illus., 50 illus. in color.
File size
22,86 MB
ISBN-13
978-3-030-39752-4 (9783030397524)
DOI
10.1007/978-3-030-39752-4
Schweitzer Classification
Other editions
Additional editions
Yunliang Cai | Liansheng Wang | Michel Audette
Computational Methods and Clinical Applications for Spine Imaging
6th International Workshop and Challenge, CSI 2019, Shenzhen, China, October 17, 2019, Proceedings
Book
02/2020
Springer
€53.49
Shipment within 7-9 days
Content
Regular Papers.-
Detection of vertebral fractures in CT using 3D Convolutional Neural Networks.- Metastatic Vertebrae Segmentation for Use in a Clinical Pipeline.- Conditioned Variational Auto-Encoder for Detecting Osteoporotic Vertebral Fractures.- Vertebral Labelling in Radiographs: Learning a Coordinate Corrector to Enforce Spinal Shape.- Semi-supervised semantic segmentation of multiple lumbosacral structures on CT.-
AASCE Challenge.-
Accurate Automated Keypoint Detections for Spinal Curvature Estimation.- Seg4Reg Networks for Automated Spinal Curvature Estimation.- Automatic Spine Curvature Estimation by a Top-down Approach.- Automatic Cobb Angle Detection using Vertebra Detector and Vertebra Corners Regression.- Automated Estimation of the Spinal Curvature via Spine Centerline Extraction with Ensembles of Cascaded Neural Networks.- Automated Spinal Curvature Assessment from X-Ray Images using Landmarks Estimation Network via Rotation Proposals.- A coarse-to-fine deep heatmap regression method for Adolescent Idiopathic Scoliosis Assessment.- Spinal Curve Guide Network(SCG-Net) for Accurate Automated Spinal Curvature Estimation.- A Multi-Task Learning Method for Direct Estimation of Spinal Curvature.
