Search Results for author: Yuichiro Hayashi

Found 10 papers, 2 papers with code

COVID-19 Infection Segmentation from Chest CT Images Based on Scale Uncertainty

no code implementations9 Jan 2022 Masahiro Oda, Tong Zheng, Yuichiro Hayashi, Yoshito Otake, Masahiro Hashimoto, Toshiaki Akashi, Shigeki Aoki, Kensaku MORI

We utilize the scale uncertainty among various receptive field sizes of a segmentation FCN to obtain infection regions.

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

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.

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