Search Results for author: Kensaku MORI

Found 35 papers, 3 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.

Multi-task Federated Learning for Heterogeneous Pancreas Segmentation

no code implementations19 Aug 2021 Chen Shen, Pochuan Wang, Holger R. Roth, Dong Yang, Daguang Xu, Masahiro Oda, Weichung Wang, Chiou-Shann Fuh, Po-Ting Chen, Kao-Lang Liu, Wei-Chih Liao, Kensaku MORI

Federated learning (FL) for medical image segmentation becomes more challenging in multi-task settings where clients might have different categories of labels represented in their data.

Federated Learning Pancreas Segmentation +1

Regression Forest-Based Atlas Localization and Direction Specific Atlas Generation for Pancreas Segmentation

no code implementations7 May 2020 Masahiro Oda, Natsuki Shimizu, Ken'ichi Karasawa, Yukitaka Nimura, Takayuki Kitasaka, Kazunari Misawa, Michitaka Fujiwara, Daniel Rueckert, Kensaku MORI

This paper proposes a fully automated atlas-based pancreas segmentation method from CT volumes utilizing atlas localization by regression forest and atlas generation using blood vessel information.

Automated Pancreas Segmentation Pancreas Segmentation

Colon Shape Estimation Method for Colonoscope Tracking using Recurrent Neural Networks

no code implementations20 Apr 2020 Masahiro Oda, Holger R. Roth, Takayuki Kitasaka, Kazuhiro Furukawa, Ryoji Miyahara, Yoshiki Hirooka, Hidemi Goto, Nassir Navab, Kensaku MORI

We propose a colon deformation estimation method using RNN and obtain the colonoscope shape from electromagnetic sensors during its insertion into the colon.

Colonoscope tracking method based on shape estimation network

no code implementations20 Apr 2020 Masahiro Oda, Holger R. Roth, Takayuki Kitasaka, Kazuhiro Furukawa, Ryoji Miyahara, Yoshiki Hirooka, Nassir Navab, Kensaku MORI

We utilize the shape estimation network (SEN), which estimates deformed colon shape during colonoscope insertions.

Organ Segmentation From Full-size CT Images Using Memory-Efficient FCN

no code implementations24 Mar 2020 Chenglong Wang, Masahiro Oda, Kensaku MORI

In this paper, we present a memory-efficient FCN to tackle the high GPU memory demand challenge in organ segmentation problem from clinical CT images.

Computed Tomography (CT) Medical Image Segmentation +1

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

Intelligent image synthesis to attack a segmentation CNN using adversarial learning

no code implementations24 Sep 2019 Liang Chen, Paul Bentley, Kensaku MORI, Kazunari Misawa, Michitaka Fujiwara, Daniel Rueckert

Our approach has three key features: 1) The generated adversarial examples exhibit anatomical variations (in form of deformations) as well as appearance perturbations; 2) The adversarial examples attack segmentation models so that the Dice scores decrease by a pre-specified amount; 3) The attack is not required to be specified beforehand.

Image Generation Semantic Segmentation

Machine learning-based colon deformation estimation method for colonoscope tracking

no code implementations8 Jun 2018 Masahiro Oda, Takayuki Kitasaka, Kazuhiro Furukawa, Ryoji Miyahara, Yoshiki Hirooka, Hidemi Goto, Nassir Navab, Kensaku MORI

An estimation method of colon deformations occur during colonoscope insertions is necessary to reduce tracking errors.

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

Attention U-Net: Learning Where to Look for the Pancreas

27 code implementations11 Apr 2018 Ozan Oktay, Jo Schlemper, Loic Le Folgoc, Matthew Lee, Mattias Heinrich, Kazunari Misawa, Kensaku Mori, Steven McDonagh, Nils Y. Hammerla, Bernhard Kainz, Ben Glocker, Daniel Rueckert

We propose a novel attention gate (AG) model for medical imaging that automatically learns to focus on target structures of varying shapes and sizes.

Brain Tumor Segmentation Pancreas Segmentation +1

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

Employing Weak Annotations for Medical Image Analysis Problems

no code implementations21 Aug 2017 Martin Rajchl, Lisa M. Koch, Christian Ledig, Jonathan Passerat-Palmbach, Kazunari Misawa, Kensaku MORI, Daniel Rueckert

To efficiently establish training databases for machine learning methods, collaborative and crowdsourcing platforms have been investigated to collectively tackle the annotation effort.

Computed Tomography (CT) Liver Segmentation

Airway segmentation from 3D chest CT volumes based on volume of interest using gradient vector flow

no code implementations26 Apr 2017 Qier Meng, Takayuki Kitasaka, Masahiro Oda, Junji Ueno, Kensaku MORI

In this paper, we propose a new airway segmentation method from 3D chest CT volumes based on volume of interests (VOI) using gradient vector flow (GVF).

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.

Comparison of the Deep-Learning-Based Automated Segmentation Methods for the Head Sectioned Images of the Virtual Korean Human Project

no code implementations15 Mar 2017 Mohammad Eshghi, Holger R. Roth, Masahiro Oda, Min Suk Chung, Kensaku MORI

This paper presents an end-to-end pixelwise fully automated segmentation of the head sectioned images of the Visible Korean Human (VKH) project based on Deep Convolutional Neural Networks (DCNNs).

Semantic Segmentation

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)

Diversity-Enhanced Condensation Algorithm and Its Application for Robust and Accurate Endoscope Three-Dimensional Motion Tracking

no code implementations CVPR 2014 Xiongbiao Luo, Ying Wan, Xiangjian He, Jie Yang, Kensaku MORI

The paper proposes a diversity-enhanced condensation algorithm to address the particle impoverishment problem which stochastic filtering usually suffers from.

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