Search Results for author: Holger R. Roth

Found 38 papers, 5 papers with code

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

Diminishing Uncertainty within the Training Pool: Active Learning for Medical Image Segmentation

no code implementations7 Jan 2021 Vishwesh Nath, Dong Yang, Bennett A. Landman, Daguang Xu, Holger R. Roth

The primary advantage being that active learning frameworks select data points that can accelerate the learning process of a model and can reduce the amount of data needed to achieve full accuracy as compared to a model trained on a randomly acquired data set.

Active Learning Hippocampus +2

Going to Extremes: Weakly Supervised Medical Image Segmentation

2 code implementations25 Sep 2020 Holger R. Roth, Dong Yang, Ziyue Xu, Xiaosong Wang, Daguang Xu

Here, we suggest using minimal user interaction in the form of extreme point clicks to train a segmentation model which, in effect, can be used to speed up medical image annotation.

Medical Image Segmentation Semantic Segmentation

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.

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.

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

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)

Improving Vertebra Segmentation through Joint Vertebra-Rib Atlases

no code implementations1 Feb 2016 Yinong Wang, Jianhua Yao, Holger R. Roth, Joseph E. Burns, Ronald M. Summers

The use of joint vertebra-rib atlases produced a statistically significant increase in the Dice coefficient from 92. 5 $\pm$ 3. 1% to 93. 8 $\pm$ 2. 1% for the left and right transverse processes and a decrease in the mean and max surface distance from 0. 75 $\pm$ 0. 60mm and 8. 63 $\pm$ 4. 44mm to 0. 30 $\pm$ 0. 27mm and 3. 65 $\pm$ 2. 87mm, respectively.

Computed Tomography (CT)

Deep convolutional networks for automated detection of posterior-element fractures on spine CT

no code implementations29 Jan 2016 Holger R. Roth, Yinong Wang, Jianhua Yao, Le Lu, Joseph E. Burns, Ronald M. Summers

In this work, we apply deep convolutional networks (ConvNets) for the automated detection of posterior element fractures of the spine.

Multi-Atlas Segmentation with Joint Label Fusion of Osteoporotic Vertebral Compression Fractures on CT

no code implementations13 Jan 2016 Yinong Wang, Jianhua Yao, Holger R. Roth, Joseph E. Burns, Ronald M. Summers

The precise and accurate segmentation of the vertebral column is essential in the diagnosis and treatment of various orthopedic, neurological, and oncological traumas and pathologies.

DeepOrgan: Multi-level Deep Convolutional Networks for Automated Pancreas Segmentation

no code implementations22 Jun 2015 Holger R. Roth, Le Lu, Amal Farag, Hoo-chang Shin, Jiamin Liu, Evrim Turkbey, Ronald M. Summers

We propose and evaluate several variations of deep ConvNets in the context of hierarchical, coarse-to-fine classification on image patches and regions, i. e. superpixels.

Automated Pancreas Segmentation Computed Tomography (CT) +2

A Bottom-up Approach for Pancreas Segmentation using Cascaded Superpixels and (Deep) Image Patch Labeling

no code implementations22 May 2015 Amal Farag, Le Lu, Holger R. Roth, Jiamin Liu, Evrim Turkbey, Ronald M. Summers

We present a bottom-up approach for pancreas segmentation in abdominal CT scans that is based on a hierarchy of information propagation by classifying image patches at different resolutions; and cascading superpixels.

Pancreas Segmentation Superpixels

Improving Computer-aided Detection using Convolutional Neural Networks and Random View Aggregation

no code implementations12 May 2015 Holger R. Roth, Le Lu, Jiamin Liu, Jianhua Yao, Ari Seff, Kevin Cherry, Lauren Kim, Ronald M. Summers

By leveraging existing CAD systems, coordinates of regions or volumes of interest (ROI or VOI) for lesion candidates are generated in this step and function as input for a second tier, which is our focus in this study.

Computer-assisted polyp matching between optical colonoscopy and CT colonography: a phantom study

no code implementations15 Jan 2015 Holger R. Roth, Thomas E. Hampshire, Emma Helbren, Mingxing Hu, Roser Vega, Steve Halligan, David J. Hawkes

Furthermore, we evaluate the matching of the reconstructed polyp from OC with other colonic endoluminal surface structures such as haustral folds and show that there is a minimum at the correct polyp from CTC.

3D Reconstruction

Detection of Sclerotic Spine Metastases via Random Aggregation of Deep Convolutional Neural Network Classifications

no code implementations22 Jul 2014 Holger R. Roth, Jianhua Yao, Le Lu, James Stieger, Joseph E. Burns, Ronald M. Summers

In testing, the CNN is employed to assign individual probabilities for a new set of N random views that are averaged at each ROI to compute a final per-candidate classification probability.

Computed Tomography (CT)

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