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Towards dense volumetric pancreas segmentation in CT using 3D fully convolutional networks
Holger Roth, Masahiro Oda, Natsuki Shimizu +6
Pancreas segmentation in computed tomography imaging has been historically difficult for automated methods because of the large shape and size variations between patients. In this…
Towards Automatic Abdominal Multi-Organ Segmentation in Dual Energy CT using Cascaded 3D Fully Convolutional Network
Shuqing Chen, Holger Roth, Sabrina Dorn +7
Automatic multi-organ segmentation of the dual energy computed tomography (DECT) data can be beneficial for biomedical research and clinical applications. However, it is a challeng…
Employing Weak Annotations for Medical Image Analysis Problems
Martin Rajchl, Lisa M. Koch, Christian Ledig +4
To efficiently establish training databases for machine learning methods, collaborative and crowdsourcing platforms have been investigated to collectively tackle the annotation eff…
Airway segmentation from 3D chest CT volumes based on volume of interest using gradient vector flow
Qier Meng, Takayuki Kitasaka, Masahiro Oda +2
Some lung diseases are related to bronchial airway structures and morphology. Although airway segmentation from chest CT volumes is an important task in the computer-aided diagnosi…
Hierarchical 3D fully convolutional networks for multi-organ segmentation
Holger R. Roth, Hirohisa Oda, Yuichiro Hayashi +5
Recent advances in 3D fully convolutional networks (FCN) have made it feasible to produce dense voxel-wise predictions of full volumetric images. In this work, we show that a multi…
Comparison of the Deep-Learning-Based Automated Segmentation Methods for the Head Sectioned Images of the Virtual Korean Human Project
Mohammad Eshghi, Holger R. Roth, Masahiro Oda +2
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 N…