4.6k citations · 5.2k across the 12 of their papers we have counts for
13 papers
Semi-automated Virtual Unfolded View Generation Method of Stomach from CT Volumes
Masahiro Oda, Tomoaki Suito, Yuichiro Hayashi +9
CT image-based diagnosis of the stomach is developed as a new way of diagnostic method. A virtual unfolded (VU) view is suitable for displaying its wall. In this paper, we propose…
Automated Pancreas Segmentation Using Multi-institutional Collaborative Deep Learning
Pochuan Wang, Chen Shen, Holger R. Roth +9
The performance of deep learning-based methods strongly relies on the number of datasets used for training. Many efforts have been made to increase the data in the medical image an…
Regression Forest-Based Atlas Localization and Direction Specific Atlas Generation for Pancreas Segmentation
Masahiro Oda, Natsuki Shimizu, Ken'ichi Karasawa +6
This paper proposes a fully automated atlas-based pancreas segmentation method from CT volumes utilizing atlas localization by regression forest and atlas generation using blood ve…
Intelligent image synthesis to attack a segmentation CNN using adversarial learning
Liang Chen, Paul Bentley, Kensaku Mori +3
Deep learning approaches based on convolutional neural networks (CNNs) have been successful in solving a number of problems in medical imaging, including image segmentation. In rec…
3D FCN Feature Driven Regression Forest-Based Pancreas Localization and Segmentation
Masahiro Oda, Natsuki Shimizu, Holger R. Roth +6
This paper presents a fully automated atlas-based pancreas segmentation method from CT volumes utilizing 3D fully convolutional network (FCN) feature-based pancreas localization. S…
A multi-scale pyramid of 3D fully convolutional networks for abdominal multi-organ segmentation
Holger R. Roth, Chen Shen, Hirohisa Oda +5
Recent advances in deep learning, like 3D fully convolutional networks (FCNs), have improved the state-of-the-art in dense semantic segmentation of medical images. However, most ne…