Computational Methods and Clinical Applications in Musculoskeletal Imaging
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Computational Methods and Clinical Applications in Musculoskeletal Imaging
5th International Workshop, MSKI 2017, Held in Conjunction with MICCAI 2017, Quebec City, QC, Canada, September 10, 2017, Revised Selected Papers
Vrtovec, Tomaz; Glocker, Ben; Yao, Jianhua; Frangi, Alejandro; Zheng, Guoyan
Springer International Publishing AG
01/2018
161
Mole
Inglês
9783319741123
15 a 20 dias
454
Descrição não disponível.
Localization of Bone Surfaces from Ultrasound Data Using Local Phase Information and Signal Transmission Maps.- Shape-aware Deep Convolutional Neural Network for Vertebrae Segmentation.- Automated Characterization of Body Composition and Frailty with Clinically Acquired CT.- Unfolded cylindrical projection for rib fracture diagnosis.- 3D Cobb Angle Measurements from Scoliotic Mesh Models with Varying Face-Vertex Density.- Automatic Localization of the Lumbar Vertebral Landmarks in CT Images with Context Features.- Joint Multimodal Segmentation of Clinical CT and MR from Hip Arthroplasty Patients.- Reconstruction of 3D muscle fiber structure using high resolution cryosectioned volume.- Segmentation of Pathological Spines in CT Images Using a Two-Way CNN and a Collision-Based Model.- Attention-driven deep learning for pathological spine segmentation.- Automatic Full Femur Segmentation from Computed Tomography Datasets using an Atlas-Based Approach.- Classification of Osteoporotic Vertebral Fractures using Shape and Appearance Modelling.- DSMS-FCN: A Deeply Supervised Multi-Scale Fully Convolutional Network for Automatic Segmentation of Intervertebral Disc in 3D MR Images.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Biomedical engineering;Imaging / Radiology;Computer Imaging, Vision, Pattern Recognition and Graphics;Image Processing and Computer Vision;Computational Methods;Clinical Applications;Orthopedic Applications;Musculoskeletal imaging;Spine imaging;image analysis;image segmentation;artificial intelligence;image reconstruction;computer vision;computer networks;machine learning;support vector machines
Localization of Bone Surfaces from Ultrasound Data Using Local Phase Information and Signal Transmission Maps.- Shape-aware Deep Convolutional Neural Network for Vertebrae Segmentation.- Automated Characterization of Body Composition and Frailty with Clinically Acquired CT.- Unfolded cylindrical projection for rib fracture diagnosis.- 3D Cobb Angle Measurements from Scoliotic Mesh Models with Varying Face-Vertex Density.- Automatic Localization of the Lumbar Vertebral Landmarks in CT Images with Context Features.- Joint Multimodal Segmentation of Clinical CT and MR from Hip Arthroplasty Patients.- Reconstruction of 3D muscle fiber structure using high resolution cryosectioned volume.- Segmentation of Pathological Spines in CT Images Using a Two-Way CNN and a Collision-Based Model.- Attention-driven deep learning for pathological spine segmentation.- Automatic Full Femur Segmentation from Computed Tomography Datasets using an Atlas-Based Approach.- Classification of Osteoporotic Vertebral Fractures using Shape and Appearance Modelling.- DSMS-FCN: A Deeply Supervised Multi-Scale Fully Convolutional Network for Automatic Segmentation of Intervertebral Disc in 3D MR Images.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Biomedical engineering;Imaging / Radiology;Computer Imaging, Vision, Pattern Recognition and Graphics;Image Processing and Computer Vision;Computational Methods;Clinical Applications;Orthopedic Applications;Musculoskeletal imaging;Spine imaging;image analysis;image segmentation;artificial intelligence;image reconstruction;computer vision;computer networks;machine learning;support vector machines
