31 citations · 65 across the 5 of their papers we have counts for
12 papers
Micro CT Image-Assisted Cross Modality Super-Resolution of Clinical CT Images Utilizing Synthesized Training Dataset
Tong Zheng, Hirohisa Oda, Masahiro Oda +5
This paper proposes a novel, unsupervised super-resolution (SR) approach for performing the SR of a clinical CT into the resolution level of a micro CT (CT). The precise non-inv…
Super-resolution of clinical CT volumes with modified CycleGAN using micro CT volumes
Tong ZHENG, Hirohisa ODA, Takayasu MORIYA +7
This paper presents a super-resolution (SR) method with unpaired training dataset of clinical CT and micro CT volumes. For obtaining very detailed information such as cancer invasi…
Visualizing intestines for diagnostic assistance of ileus based on intestinal region segmentation from 3D CT images
Hirohisa Oda, Kohei Nishio, Takayuki Kitasaka +7
This paper presents a visualization method of intestine (the small and large intestines) regions and their stenosed parts caused by ileus from CT volumes. Since it is difficult for…
Multi-modality super-resolution loss for GAN-based super-resolution of clinical CT images using micro CT image database
Tong Zheng, Hirohisa Oda, Takayasu Moriya +6
This paper newly introduces multi-modality loss function for GAN-based super-resolution that can maintain image structure and intensity on unpaired training dataset of clinical CT…
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…
Unsupervised Segmentation of 3D Medical Images Based on Clustering and Deep Representation Learning
Takayasu Moriya, Holger R. Roth, Shota Nakamura +4
This paper presents a novel unsupervised segmentation method for 3D medical images. Convolutional neural networks (CNNs) have brought significant advances in image segmentation. Ho…