31 citations · 65 across the 5 of their papers we have counts for
9 papers · 1 filter
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…
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…