MRI Manufacturer Shift and Adaptation: Increasing the Generalizability of Deep Learning Segmentation for MR Images Acquired with Different Scanners-Reference-Cited by-同舟云学术

MRI Manufacturer Shift and Adaptation: Increasing the Generalizability of Deep Learning Segmentation for MR Images Acquired with Different Scanners

Published:2020-07-01 Issue:4 Volume:2 Page:e190195
ISSN:2638-6100
Container-title:Radiology: Artificial Intelligence
language:en
Short-container-title:Radiology: Artificial Intelligence

Author:

Yan Wenjun¹,Huang Lu¹^ORCID,Xia Liming¹,Gu Shengjia¹^ORCID,Yan Fuhua¹^ORCID,Wang Yuanyuan¹,Tao Qian¹

Affiliation:

1. From the Biomedical Engineering Center, Fudan University, Shanghai, China (W.Y., Y.W.); Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China (L.H., L.X.); Department of Radiology, Ruijin Hospital, Shanghai Jiaotong University, Shanghai, China (S.G., F.Y.); and Division of Image Processing, Department of Radiology, Leiden University Medical Center, Albinusdreef 2, 2333 ZA Leiden, the Netherlands (Q.T.).

Publisher

Radiological Society of North America (RSNA)

Subject

General Medicine

Link

http://pubs.rsna.org/doi/pdf/10.1148/ryai.2020190195

Reference24 articles.

1. Deep Learning in Medical Image Analysis

2. Machine Learning in Radiology: Applications Beyond Image Interpretation

3. Deep Learning–based Method for Fully Automatic Quantification of Left Ventricle Function from Cine MR Images: A Multivendor, Multicenter Study

4. Automatic segmentation of the uterus on MRI using a convolutional neural network

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