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Keita Nakagomi

Showing results (1-10 of 10) with videos related to

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Computers in Biology and Medicine|April 28, 2020
Bone segmentation on whole-body CT using convolutional neural network with novel data augmentation techniquesShunjiro 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 volumeKeita Nakagomi, Akinobu Shimizu, Hidefumi Kobatake, et al.
European Radiology|April 8, 2022
Deep learning-based algorithm improved radiologists' performance in bone metastases detection on CTShunjiro Noguchi, Mizuho Nishio, Ryo Sakamoto, et al.
Journal of Digital Imaging|October 7, 2020
Adaptive Voxel Matching for Temporal CT SubtractionToru 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 imagesThai 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 MetastasesRyo Sakamoto, Masahiro Yakami, Koji Fujimoto, et al.
European Radiology|March 20, 2019
CT temporal subtraction improves early detection of bone metastases compared to SPECTKoji 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 residentsKoji 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 studyKoji 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 radiologistsMasami Kawagishi, Bin Chen, Daisuke Furukawa, et al.
Pageof 1

Showing results (1-10 of 10) with videos related to

Sort By:
Pageof 1
Computers in Biology and Medicine|April 28, 2020
Bone segmentation on whole-body CT using convolutional neural network with novel data augmentation techniquesShunjiro 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 volumeKeita Nakagomi, Akinobu Shimizu, Hidefumi Kobatake, et al.
European Radiology|April 8, 2022
Deep learning-based algorithm improved radiologists' performance in bone metastases detection on CTShunjiro Noguchi, Mizuho Nishio, Ryo Sakamoto, et al.
Journal of Digital Imaging|October 7, 2020
Adaptive Voxel Matching for Temporal CT SubtractionToru 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 imagesThai 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 MetastasesRyo Sakamoto, Masahiro Yakami, Koji Fujimoto, et al.
European Radiology|March 20, 2019
CT temporal subtraction improves early detection of bone metastases compared to SPECTKoji 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 residentsKoji 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 studyKoji 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 radiologistsMasami Kawagishi, Bin Chen, Daisuke Furukawa, et al.
Pageof 1