4.6k citations · 5.3k across the 36 of their papers we have counts for
9 papers · 1 filter
3D FCN Feature Driven Regression Forest-Based Pancreas Localization and Segmentation
Masahiro Oda, Natsuki Shimizu, Holger R. Roth +6
This paper presents a fully automated atlas-based pancreas segmentation method from CT volumes utilizing 3D fully convolutional network (FCN) feature-based pancreas localization. S…
Machine learning-based colon deformation estimation method for colonoscope tracking
Masahiro Oda, Takayuki Kitasaka, Kazuhiro Furukawa +5
This paper presents a colon deformation estimation method, which can be used to estimate colon deformations during colonoscope insertions. Colonoscope tracking or navigation system…
A multi-scale pyramid of 3D fully convolutional networks for abdominal multi-organ segmentation
Holger R. Roth, Chen Shen, Hirohisa Oda +5
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. However, most ne…
Attention U-Net: Learning Where to Look for the Pancreas
Ozan Oktay, Jo Schlemper, Loic Le Folgoc +9
We propose a novel attention gate (AG) model for medical imaging that automatically learns to focus on target structures of varying shapes and sizes. Models trained with AGs implic…
Unsupervised Segmentation of 3D Medical Images Based on Clustering and Deep Representation Learning
Takayasu Moriya, Holger R. Roth, Shota Nakamura +4
This paper presents a novel unsupervised segmentation method for 3D medical images. Convolutional neural networks (CNNs) have brought significant advances in image segmentation. Ho…
Unsupervised Pathology Image Segmentation Using Representation Learning with Spherical K-means
Takayasu Moriya, Holger R. Roth, Shota Nakamura +4
This paper presents a novel method for unsupervised segmentation of pathology images. Staging of lung cancer is a major factor of prognosis. Measuring the maximum dimensions of the…