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Computers in Biology and Medicine
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April 28, 2020
Bone segmentation on whole-body CT using convolutional neural network with novel data augmentation techniques
Shunjiro Noguchi, Mizuho Nishio, Masahiro Yakami, et al.
Medical Image Analysis
|
October 16, 2012
Multi-shape graph cuts with neighbor prior constraints and its application to lung segmentation from a chest CT volume
Keita Nakagomi, Akinobu Shimizu, Hidefumi Kobatake, et al.
European Radiology
|
April 8, 2022
Deep learning-based algorithm improved radiologists' performance in bone metastases detection on CT
Shunjiro Noguchi, Mizuho Nishio, Ryo Sakamoto, et al.
Journal of Digital Imaging
|
October 7, 2020
Adaptive Voxel Matching for Temporal CT Subtraction
Toru Tanaka, Ryo Ishikawa, Keita Nakagomi, et al.
European Radiology
|
August 1, 2018
Detection of suspected brain infarctions on CT can be significantly improved with temporal subtraction images
Thai Akasaka, Masahiro Yakami, Mizuho Nishio, et al.
Radiology
|
July 6, 2017
Temporal Subtraction of Serial CT Images with Large Deformation Diffeomorphic Metric Mapping in the Identification of Bone Metastases
Ryo Sakamoto, Masahiro Yakami, Koji Fujimoto, et al.
European Radiology
|
March 20, 2019
CT temporal subtraction improves early detection of bone metastases compared to SPECT
Koji Onoue, Mizuho Nishio, Masahiro Yakami, et al.
European Radiology
|
July 6, 2019
Temporal subtraction of computed tomography images improves detectability of bone metastases by radiology residents
Koji Onoue, Mizuho Nishio, Masahiro Yakami, et al.
Scientific Reports
|
September 17, 2021
Temporal subtraction CT with nonrigid image registration improves detection of bone metastases by radiologists: results of a large-scale observer study
Koji Onoue, Masahiro Yakami, Mizuho Nishio, et al.
International Journal of Computer Assisted Radiology and Surgery
|
March 13, 2017
A study of computer-aided diagnosis for pulmonary nodule: comparison between classification accuracies using calculated image features and imaging findings annotated by radiologists
Masami Kawagishi, Bin Chen, Daisuke Furukawa, et al.
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of 1
Search research articles
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Showing results (1-10 of 10) with videos related to
Sort By:
Page
of 1
Computers in Biology and Medicine
|
April 28, 2020
Bone segmentation on whole-body CT using convolutional neural network with novel data augmentation techniques
Shunjiro Noguchi, Mizuho Nishio, Masahiro Yakami, et al.
Medical Image Analysis
|
October 16, 2012
Multi-shape graph cuts with neighbor prior constraints and its application to lung segmentation from a chest CT volume
Keita Nakagomi, Akinobu Shimizu, Hidefumi Kobatake, et al.
European Radiology
|
April 8, 2022
Deep learning-based algorithm improved radiologists' performance in bone metastases detection on CT
Shunjiro Noguchi, Mizuho Nishio, Ryo Sakamoto, et al.
Journal of Digital Imaging
|
October 7, 2020
Adaptive Voxel Matching for Temporal CT Subtraction
Toru Tanaka, Ryo Ishikawa, Keita Nakagomi, et al.
European Radiology
|
August 1, 2018
Detection of suspected brain infarctions on CT can be significantly improved with temporal subtraction images
Thai Akasaka, Masahiro Yakami, Mizuho Nishio, et al.
Radiology
|
July 6, 2017
Temporal Subtraction of Serial CT Images with Large Deformation Diffeomorphic Metric Mapping in the Identification of Bone Metastases
Ryo Sakamoto, Masahiro Yakami, Koji Fujimoto, et al.
European Radiology
|
March 20, 2019
CT temporal subtraction improves early detection of bone metastases compared to SPECT
Koji Onoue, Mizuho Nishio, Masahiro Yakami, et al.
European Radiology
|
July 6, 2019
Temporal subtraction of computed tomography images improves detectability of bone metastases by radiology residents
Koji Onoue, Mizuho Nishio, Masahiro Yakami, et al.
Scientific Reports
|
September 17, 2021
Temporal subtraction CT with nonrigid image registration improves detection of bone metastases by radiologists: results of a large-scale observer study
Koji Onoue, Masahiro Yakami, Mizuho Nishio, et al.
International Journal of Computer Assisted Radiology and Surgery
|
March 13, 2017
A study of computer-aided diagnosis for pulmonary nodule: comparison between classification accuracies using calculated image features and imaging findings annotated by radiologists
Masami Kawagishi, Bin Chen, Daisuke Furukawa, et al.
Page
of 1