Curved planar reformatting and convolutional neural network‐based segmentation of the small bowel for visualization and quantitative assessment of pediatric Crohn's disease from MRI-Reference-Cited by-同舟云学术

Curved planar reformatting and convolutional neural network‐based segmentation of the small bowel for visualization and quantitative assessment of pediatric Crohn's disease from MRI

Published:2018-10-24 Issue:6 Volume:49 Page:1565-1576
ISSN:1053-1807
Container-title:Journal of Magnetic Resonance Imaging
language:en
Short-container-title:J. Magn. Reson. Imaging

Author:

Lamash Yechiel¹^ORCID,Kurugol Sila¹,Freiman Moti¹,Perez‐Rossello Jeannette M.²,Callahan Michael J.²,Bousvaros Athos³,Warfield Simon K.¹

Affiliation:

1. Computational Radiology Laboratory, Department of Radiology, Children's Hospital Boston Harvard Medical School Boston Massachusetts USA

2. Department of Radiology Children's Hospital Boston, Harvard Medical School Boston Massachusetts USA

3. Center for Inflammatory Bowel Disease Treatment and Research Children's Hospital Boston, Harvard Medical School Boston Massachusetts USA

Funder

National Institute of Diabetes and Digestive and Kidney Diseases

Publisher

Wiley

Subject

Radiology Nuclear Medicine and imaging

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/jmri.26330

Reference24 articles.

1. Consensus Recommendations for Evaluation, Interpretation, and Utilization of Computed Tomography and Magnetic Resonance Enterography in Patients With Small Bowel Crohn’s Disease

2. Magnetic resonance for assessment of disease activity and severity in ileocolonic Crohn's disease

3. A Supervised Learning Based Approach to Detect Crohn’s Disease in Abdominal MR Volumes

4. Automatic Detection and Segmentation of Crohn's Disease Tissues From Abdominal MRI

5. MahapatraD SchüfflerPJ TielbeekJA et al. eds.Active learning based segmentation of Crohn's disease using principles of visual saliency. Biomedical Imaging (ISBI) 2014 I.E. 11th International Symposium on;2014: IEEE.

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