Search Results for author: Hirohisa ODA

Found 14 papers, 2 papers with code

Visualizing intestines for diagnostic assistance of ileus based on intestinal region segmentation from 3D CT images

no code implementations3 Mar 2020 Hirohisa Oda, Kohei Nishio, Takayuki Kitasaka, Hizuru Amano, Aitaro Takimoto, Hiroo Uchida, Kojiro Suzuki, Hayato Itoh, Masahiro Oda, Kensaku MORI

This paper presents a visualization method of intestine (the small and large intestines) regions and their stenosed parts caused by ileus from CT volumes.

Multi-modality super-resolution loss for GAN-based super-resolution of clinical CT images using micro CT image database

no code implementations30 Dec 2019 Tong Zheng, Hirohisa ODA, Takayasu MORIYA, Shota NAKAMURA, Masahiro Oda, Masaki MORI, Horitsugu Takabatake, Hiroshi NATORI, Kensaku MORI

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 and micro CT volumes.

Computed Tomography (CT) Super-Resolution +1

A multi-scale pyramid of 3D fully convolutional networks for abdominal multi-organ segmentation

no code implementations6 Jun 2018 Holger R. Roth, Chen Shen, Hirohisa ODA, Takaaki Sugino, Masahiro Oda, Yuichiro Hayashi, Kazunari Misawa, Kensaku MORI

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.

Semantic Segmentation

Unsupervised Pathology Image Segmentation Using Representation Learning with Spherical K-means

no code implementations11 Apr 2018 Takayasu Moriya, Holger R. Roth, Shota NAKAMURA, Hirohisa ODA, Kai Nagara, Masahiro Oda, Kensaku MORI

In this paper, we propose a unified approach to unsupervised representation learning and clustering for pathology image segmentation.

Representation Learning Semantic Segmentation

Deep learning and its application to medical image segmentation

no code implementations23 Mar 2018 Holger R. Roth, Chen Shen, Hirohisa ODA, Masahiro Oda, Yuichiro Hayashi, Kazunari Misawa, Kensaku MORI

However, recent advances in deep learning have made it possible to significantly improve the performance of image recognition and semantic segmentation methods in the field of computer vision.

Computed Tomography (CT) Medical Image Segmentation +1

An application of cascaded 3D fully convolutional networks for medical image segmentation

1 code implementation14 Mar 2018 Holger R. Roth, Hirohisa ODA, Xiangrong Zhou, Natsuki Shimizu, Ying Yang, Yuichiro Hayashi, Masahiro Oda, Michitaka Fujiwara, Kazunari Misawa, Kensaku MORI

In this work, we show that a multi-class 3D FCN trained on manually labeled CT scans of several anatomical structures (ranging from the large organs to thin vessels) can achieve competitive segmentation results, while avoiding the need for handcrafting features or training class-specific models.

3D Medical Imaging Segmentation Semantic Segmentation

Towards dense volumetric pancreas segmentation in CT using 3D fully convolutional networks

no code implementations17 Nov 2017 Holger Roth, Masahiro Oda, Natsuki Shimizu, Hirohisa ODA, Yuichiro Hayashi, Takayuki Kitasaka, Michitaka Fujiwara, Kazunari Misawa, Kensaku MORI

Pancreas segmentation in computed tomography imaging has been historically difficult for automated methods because of the large shape and size variations between patients.

Pancreas Segmentation

Hierarchical 3D fully convolutional networks for multi-organ segmentation

1 code implementation21 Apr 2017 Holger R. Roth, Hirohisa ODA, Yuichiro Hayashi, Masahiro Oda, Natsuki Shimizu, Michitaka Fujiwara, Kazunari Misawa, Kensaku MORI

In this work, we show that a multi-class 3D FCN trained on manually labeled CT scans of seven abdominal structures (artery, vein, liver, spleen, stomach, gallbladder, and pancreas) can achieve competitive segmentation results, while avoiding the need for handcrafting features or training organ-specific models.

Multi-scale Image Fusion Between Pre-operative Clinical CT and X-ray Microtomography of Lung Pathology

no code implementations27 Feb 2017 Holger R. Roth, Kai Nagara, Hirohisa ODA, Masahiro Oda, Tomoshi Sugiyama, Shota NAKAMURA, Kensaku MORI

The alignment of clinical CT with $\mu$CT will allow further registration with even finer resolutions of $\mu$CT (up to 10 $\mu$m resolution) and ultimately with histopathological microscopy images for further macro to micro image fusion that can aid medical image analysis.

Computed Tomography (CT)

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