4.6k citations · 5.2k across the 12 of their papers we have counts for
6 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…
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…
Deep learning and its application to medical image segmentation
Holger R. Roth, Chen Shen, Hirohisa Oda +4
One of the most common tasks in medical imaging is semantic segmentation. Achieving this segmentation automatically has been an active area of research, but the task has been prove…
An application of cascaded 3D fully convolutional networks for medical image segmentation
Holger R. Roth, Hirohisa Oda, Xiangrong Zhou +7
Recent advances in 3D fully convolutional networks (FCN) have made it feasible to produce dense voxel-wise predictions of volumetric images. In this work, we show that a multi-clas…
On the influence of Dice loss function in multi-class organ segmentation of abdominal CT using 3D fully convolutional networks
Chen Shen, Holger R. Roth, Hirohisa Oda +4
Deep learning-based methods achieved impressive results for the segmentation of medical images. With the development of 3D fully convolutional networks (FCNs), it has become feasib…