1 code implementation • 11 Oct 2024 • Zhou Zheng, Yuichiro Hayashi, Masahiro Oda, Takayuki Kitasaka, Kensaku MORI
In this paper, we study weakly-supervised laparoscopic image segmentation with sparse annotations.
1 code implementation • 8 Aug 2023 • Pochuan Wang, Chen Shen, Weichung Wang, Masahiro Oda, Chiou-Shann Fuh, Kensaku MORI, Holger R. Roth
Federated learning (FL) is a key technology enabling the collaborative development of a model without exchanging training data.
no code implementations • 27 Mar 2023 • Ryo Toda, Hayato Itoh, Masahiro Oda, Yuichiro Hayashi, Yoshito Otake, Masahiro Hashimoto, Toshiaki Akashi, Shigeki Aoki, Kensaku MORI
This paper presents a fully-automated method for the identification of suspicious regions of a coronavirus disease (COVID-19) on chest CT volumes.
1 code implementation • 2 Feb 2023 • Masahiro Oda, Kazuhiro Furukawa, Nassir Navab, Kensaku MORI
Kinematic data of a colonoscope and the colon, including positions and directions of their centerlines, are obtained using electromagnetic and depth sensors.
no code implementations • 14 Jan 2022 • Masahiro Oda, Tomoaki Suito, Yuichiro Hayashi, Takayuki Kitasaka, Kazuhiro Furukawa, Ryoji Miyahara, Yoshiki Hirooka, Hidemi Goto, Gen Iinuma, Kazunari Misawa, Shigeru Nawano, Kensaku MORI
In this paper, we propose a semi-automated method for generating VU views of the stomach.
no code implementations • 13 Jan 2022 • Masahiro Oda, Kiyohito Tanaka, Hirotsugu TAKABATAKE, Masaki MORI, Hiroshi NATORI, Kensaku MORI
Virtual endoscopic images are generated by using a volume rendering method from a CT volume of a patient.
no code implementations • 12 Jan 2022 • Masahiro Oda, Hayato Itoh, Kiyohito Tanaka, Hirotsugu TAKABATAKE, Masaki MORI, Hiroshi NATORI, Kensaku MORI
The identification accuracy of the network improved from 69. 2% to 74. 1% by using the estimated depth images.
no code implementations • 9 Jan 2022 • Masahiro Oda, Yuichiro Hayashi, Yoshito Otake, Masahiro Hashimoto, Toshiaki Akashi, Kensaku MORI
Our method recognizes and segments lung normal and infection regions in CT volumes.
no code implementations • 9 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.
no code implementations • 19 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.
no code implementations • 20 Oct 2020 • Tong Zheng, Hirohisa ODA, Masahiro Oda, Shota NAKAMURA, Masaki MORI, Hirotsugu TAKABATAKE, Hiroshi NATORI, Kensaku MORI
Unsupervised SR methods are required that do not need paired LR and HR images.
no code implementations • 28 Sep 2020 • Pochuan Wang, Chen Shen, Holger R. Roth, Dong Yang, Daguang Xu, Masahiro Oda, Kazunari Misawa, Po-Ting Chen, Kao-Lang Liu, Wei-Chih Liao, Wei-Chung Wang, Kensaku MORI
The performance of deep learning-based methods strongly relies on the number of datasets used for training.
no code implementations • 7 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.
no code implementations • 4 May 2020 • Masahiro Oda, Takefumi Yamaguchi, Hideki Fukuoka, Yuta Ueno, Kensaku MORI
This paper presents an automated classification method of infective and non-infective diseases from anterior eye images.
no code implementations • 20 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.
no code implementations • 20 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.
no code implementations • 7 Apr 2020 • Tong ZHENG, Hirohisa ODA, Takayasu MORIYA, Takaaki SUGINO, Shota NAKAMURA, Masahiro Oda, Masaki MORI, Hirotsugu TAKABATAKE, Hiroshi NATORI, Kensaku MORI
This paper presents a super-resolution (SR) method with unpaired training dataset of clinical CT and micro CT volumes.
no code implementations • 24 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.
no code implementations • 3 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.
no code implementations • 30 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.
no code implementations • 5 Aug 2019 • Chenglong Wang, Holger R. Roth, Takayuki Kitasaka, Masahiro Oda, Yuichiro Hayashi, Yasushi Yoshino, Tokunori Yamamoto, Naoto Sassa, Momokazu Goto, Kensaku MORI
Then we generate a Voronoi diagram to estimate the renal vascular dominant regions based on the segmented kidney and renal arteries.
no code implementations • 8 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.
no code implementations • 8 Jun 2018 • Masahiro Oda, Natsuki Shimizu, Holger R. Roth, Ken'ichi Karasawa, Takayuki Kitasaka, Kazunari Misawa, Michitaka Fujiwara, Daniel Rueckert, Kensaku MORI
We estimate the position and the size of the pancreas (localized) from global features by regression forests.
no code implementations • 6 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.
no code implementations • 11 Apr 2018 • Takayasu Moriya, Holger R. Roth, Shota NAKAMURA, Hirohisa ODA, Kai Nagara, Masahiro Oda, Kensaku MORI
This paper presents a novel unsupervised segmentation method for 3D medical images.
no code implementations • 11 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.
no code implementations • 23 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.
1 code implementation • 14 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.
Ranked #2 on 3D Medical Imaging Segmentation on TCIA Pancreas-CT
no code implementations • 18 Jan 2018 • Chen Shen, Holger R. Roth, Hirohisa ODA, Masahiro Oda, Yuichiro Hayashi, Kazunari Misawa, Kensaku MORI
Deep learning-based methods achieved impressive results for the segmentation of medical images.
no code implementations • 17 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.
no code implementations • 26 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).
1 code implementation • 21 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.
no code implementations • 15 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).
no code implementations • 27 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.