Computational Methods and Clinical Applications for Spine Imaging: 5th International Workshop and Challenge, CSI 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Revised Selected Papers

by Guoyan Zheng (Editor), Daniel Belavy (Editor), Yunliang Cai (Editor), Shuo Li (Editor)

Overview

This book constitutes the refereed proceedings of the 5th International Workshop and Challenge on Computational Methods and Clinical Applications for Spine Imaging, CSI 2018, held in conjunction with MICCAI 2018, in Granada, Spain, in September 2018.
The 8 full papers presented together with 8 short papers and 1 keynote were carefully reviewed and selected for inclusion in this volume. Papers on novel methodology and clinical research, and also papers which demonstrate the performance of methods on the provided challenges, the aim is to cover both theoretical and very practical aspects of computerized spinal imaging.

Product Details

ISBN-13:	9783030137359
Publisher:	Springer International Publishing
Publication date:	03/14/2019
Series:	Lecture Notes in Computer Science , #11397
Edition description:	1st ed. 2019
Pages:	181
Product dimensions:	6.10(w) x 9.25(h) x (d)

Table of Contents

Spinal Cord Gray Matter-White Matter Segmentation on Magnetic Resonance AMIRA Images with MD-GRU.- Predicting Scoliosis in DXA Scans Using Intermediate Representations.- Fast Registration of CT with Intra-operative Ultrasound Images for Spine Surgery.- Automated Grading of Modic Changes Using CNNs – Improving the Performance with Mix-up.- Error Estimation for Appearance Model Segmentation of Musculoskeletal Structures Using Multiple, Independent Sub-models.- Automated Segmentation of Intervertebral Disc using Fully Dilated Separable Deep Neural Networks.- Intensity Standardization of Skeleton in Follow-up Whole-Body MRI.- Towards a Deformable Multi-Surface Approach to Ligamentous Spine Models for Predictive Simulation-Based Scoliosis Surgery Planning.- Intervertebral Disc Segmentation Using Mathematical Morphology—A CNN-Free Approach.