332 citations · 597 across the 13 of their papers we have counts for
14 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…
Unsupervised Pathology Image Segmentation Using Representation Learning with Spherical K-means
Takayasu Moriya, Holger R. Roth, Shota Nakamura +4
This paper presents a novel method for unsupervised segmentation of pathology images. Staging of lung cancer is a major factor of prognosis. Measuring the maximum dimensions of the…
Deep learning and its application to medical image segmentation
Holger R. Roth, Chen Shen, Hirohisa Oda +4
One of the most common tasks in medical imaging is semantic segmentation. Achieving this segmentation automatically has been an active area of research, but the task has been prove…
An application of cascaded 3D fully convolutional networks for medical image segmentation
Holger R. Roth, Hirohisa Oda, Xiangrong Zhou +7
Recent advances in 3D fully convolutional networks (FCN) have made it feasible to produce dense voxel-wise predictions of volumetric images. In this work, we show that a multi-clas…
On the influence of Dice loss function in multi-class organ segmentation of abdominal CT using 3D fully convolutional networks
Chen Shen, Holger R. Roth, Hirohisa Oda +4
Deep learning-based methods achieved impressive results for the segmentation of medical images. With the development of 3D fully convolutional networks (FCNs), it has become feasib…
Towards dense volumetric pancreas segmentation in CT using 3D fully convolutional networks
Holger Roth, Masahiro Oda, Natsuki Shimizu +6
Pancreas segmentation in computed tomography imaging has been historically difficult for automated methods because of the large shape and size variations between patients. In this…
Towards Automatic Abdominal Multi-Organ Segmentation in Dual Energy CT using Cascaded 3D Fully Convolutional Network
Shuqing Chen, Holger Roth, Sabrina Dorn +7
Automatic multi-organ segmentation of the dual energy computed tomography (DECT) data can be beneficial for biomedical research and clinical applications. However, it is a challeng…
Hierarchical 3D fully convolutional networks for multi-organ segmentation
Holger R. Roth, Hirohisa Oda, Yuichiro Hayashi +5
Recent advances in 3D fully convolutional networks (FCN) have made it feasible to produce dense voxel-wise predictions of full volumetric images. In this work, we show that a multi…
Multi-scale Image Fusion Between Pre-operative Clinical CT and X-ray Microtomography of Lung Pathology
Holger R. Roth, Kai Nagara, Hirohisa Oda +4
Computational anatomy allows the quantitative analysis of organs in medical images. However, most analysis is constrained to the millimeter scale because of the limited resolution…