Yao / Zheng / Pozo | Computational Methods and Clinical Applications in Musculoskeletal Imaging | Buch | sack.de

Yao / Zheng / Pozo Computational Methods and Clinical Applications in Musculoskeletal Imaging

6th International Workshop, MSKI 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Revised Selected Papers

1. Auflage 2019, Band: 11404, 153 Seiten, Kartoniert, Book, Format (B × H): 156 mm x 237 mm, Gewicht: 266 g Reihe: Lecture Notes in Computer Science
ISBN: 978-3-030-11165-6
Verlag: Springer, Berlin

Yao / Zheng / Pozo Computational Methods and Clinical Applications in Musculoskeletal Imaging

This book constitutes the refereed proceedings of the 6th International Workshop on Computational Methods and Clinical Applications for Musculoskeletal Imaging, MSKI 2018, held in conjunction with MICCAI 2018, in Granada, Spain, in September 2018.

The 13 workshop papers were carefully reviewed and selected for inclusion in this volume. Topics of interest include all major aspects of musculoskeletal imaging, for example: clinical applications of musculoskeletal computational imaging; computer-aided detection and diagnosis of conditions of the bones, muscles and joints; image-guided musculoskeletal surgery and interventions; image-based assessment and monitoring of surgical and pharmacological treatment; segmentation, registration, detection, localization and visualization of the musculoskeletal anatomy; statistical and geometrical modeling of the musculoskeletal shape and appearance; image-based microstructural characterization of musculoskeletal tissue; novel techniques for musculoskeletal imaging.



Weitere Infos & Material

Automated Recognition of Erector Spinae Muscles and Their Skeletal Attachment Region via Deep Learning in Torso CT Images.- Fully automatic teeth segmentation in adult OPG images.- Fully Automatic Planning of Total Shoulder Arthroplasty without Segmentation: A Deep Learning Based Approach.- Deep Volumetric Shape Learning for Semantic Segmentation of the Hip Joint from 3D MR Images.- Pelvis segmentation using multi-pass U-net and iterative shape estimation.- Bone Adaptation as Level Set Motion.- Landmark Localisation in Radiographs Using Weighted Heatmap Displacement Voting.- Perthes Disease Classification Using Shape and Appearance Modelling.- Deep Learning Based Rib Centerline Extraction and Labeling.- Automatic Wrist Fracture Detection From Posteroanterior and Lateral Radiographs: A Deep Learning-Based Approach.- Bone Reconstruction and Depth Control During Laser Ablation.- Automated Dynamic 3D Ultrasound Assessment of Developmental Dysplasia of the Infant Hip.- Automated Measurement of Pelvic Incidence from X-Ray Images.


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