31 citations · 70 across the 5 of their papers we have counts for
5 papers
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…
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…
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…
Multi-scale Image Fusion Between Pre-operative Clinical CT and X-ray Microtomography of Lung Pathology
Holger R. Roth, Kai Nagara, Hirohisa Oda +4
Computational anatomy allows the quantitative analysis of organs in medical images. However, most analysis is constrained to the millimeter scale because of the limited resolution…