332 citations · 554 across the 12 of their papers we have counts for
13 papers
Classification of COVID-19 cases from chest CT volumes using hybrid model of 3D CNN and 3D MLP-Mixer
Masahiro Oda, Tong Zheng, Yuichiro Hayashi +5
This paper proposes an automated classification method of COVID-19 chest CT volumes using improved 3D MLP-Mixer. Novel coronavirus disease 2019 (COVID-19) spreads over the world, c…
Automated classification method of COVID-19 cases from chest CT volumes using 2D and 3D hybrid CNN for anisotropic volumes
Masahiro Oda, Tong Zheng, Yuichiro Hayashi +5
This paper proposes an automated classification method of chest CT volumes based on likelihood of COVID-19 cases. Novel coronavirus disease 2019 (COVID-19) spreads over the world,…
Identifying Suspicious Regions of Covid-19 by Abnormality-Sensitive Activation Mapping
Ryo Toda, Hayato Itoh, Masahiro Oda +6
This paper presents a fully-automated method for the identification of suspicious regions of a coronavirus disease (COVID-19) on chest CT volumes. One major role of chest CT scanni…
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…
COVID-19 Infection Segmentation from Chest CT Images Based on Scale Uncertainty
Masahiro Oda, Tong Zheng, Yuichiro Hayashi +5
This paper proposes a segmentation method of infection regions in the lung from CT volumes of COVID-19 patients. COVID-19 spread worldwide, causing many infected patients and death…
Lung infection and normal region segmentation from CT volumes of COVID-19 cases
Masahiro Oda, Yuichiro Hayashi, Yoshito Otake +3
This paper proposes an automated segmentation method of infection and normal regions in the lung from CT volumes of COVID-19 patients. From December 2019, novel coronavirus disease…
Precise Estimation of Renal Vascular Dominant Regions Using Spatially Aware Fully Convolutional Networks, Tensor-Cut and Voronoi Diagrams
Chenglong Wang, Holger R. Roth, Takayuki Kitasaka +7
This paper presents a new approach for precisely estimating the renal vascular dominant region using a Voronoi diagram. To provide computer-assisted diagnostics for the pre-surgica…
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…
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…
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…