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Computational Methods and Clinical Applications in Musculoskeletal Imaging

Overview of attention for book
Cover of 'Computational Methods and Clinical Applications in Musculoskeletal Imaging'

Table of Contents

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    Book Overview
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    Chapter 1 Localization of Bone Surfaces from Ultrasound Data Using Local Phase Information and Signal Transmission Maps
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    Chapter 2 Shape-Aware Deep Convolutional Neural Network for Vertebrae Segmentation
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    Chapter 3 Automated Characterization of Body Composition and Frailty with Clinically Acquired CT
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    Chapter 4 Unfolded Cylindrical Projection for Rib Fracture Diagnosis
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    Chapter 5 3D Cobb Angle Measurements from Scoliotic Mesh Models with Varying Face-Vertex Density
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    Chapter 6 Automatic Localization of the Lumbar Vertebral Landmarks in CT Images with Context Features
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    Chapter 7 Joint Multimodal Segmentation of Clinical CT and MR from Hip Arthroplasty Patients
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    Chapter 8 Reconstruction of 3D Muscle Fiber Structure Using High Resolution Cryosectioned Volume
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    Chapter 9 Segmentation of Pathological Spines in CT Images Using a Two-Way CNN and a Collision-Based Model
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    Chapter 10 Attention-Driven Deep Learning for Pathological Spine Segmentation
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    Chapter 11 Automatic Full Femur Segmentation from Computed Tomography Datasets Using an Atlas-Based Approach
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    Chapter 12 Classification of Osteoporotic Vertebral Fractures Using Shape and Appearance Modelling
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    Chapter 13 DSMS-FCN: A Deeply Supervised Multi-scale Fully Convolutional Network for Automatic Segmentation of Intervertebral Disc in 3D MR Images
Attention for Chapter 7: Joint Multimodal Segmentation of Clinical CT and MR from Hip Arthroplasty Patients
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Chapter title
Joint Multimodal Segmentation of Clinical CT and MR from Hip Arthroplasty Patients
Chapter number 7
Book title
Computational Methods and Clinical Applications in Musculoskeletal Imaging
Published by
Springer, Cham, September 2017
DOI 10.1007/978-3-319-74113-0_7
Book ISBNs
978-3-31-974112-3, 978-3-31-974113-0
Authors

Marta Bianca Maria Ranzini, Michael Ebner, M. Jorge Cardoso, Anastasia Fotiadou, Tom Vercauteren, Johann Henckel, Alister Hart, Sébastien Ourselin, Marc Modat, Ranzini, MBM, Ebner, M, Cardoso, MJ, Fotiadou, A, Vercauteren, T, Henckel, J, Hart, A, Ourselin, S, Modat, M, Ranzini, Marta Bianca Maria, Ebner, Michael, Cardoso, M. Jorge, Fotiadou, Anastasia, Vercauteren, Tom, Henckel, Johann, Hart, Alister, Ourselin, Sébastien, Modat, Marc

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X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
 Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 24 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 24 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 9 38%
Lecturer > Senior Lecturer 2 8%
Other 2 8%
Student > Doctoral Student 2 8%
Student > Master 2 8%
Other 2 8%
Unknown 5 21%
Readers by discipline Count As %
Computer Science 8 33%
Engineering 6 25%
Mathematics 2 8%
Physics and Astronomy 1 4%
Unknown 7 29%

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