Semantic volume segmentation with iterative context integration for bio-medical image stacks
- 作者: Sickert S.1, Rodner E.1, Denzler J.1
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隶属关系:
- Computer Vision Group
- 期: 卷 26, 编号 1 (2016)
- 页面: 197-204
- 栏目: Applied Problems
- URL: https://journals.rcsi.science/1054-6618/article/view/194598
- DOI: https://doi.org/10.1134/S1054661816010223
- ID: 194598
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详细
Automatic recognition of biological structures like membranes or synapses is important to analyze organic processes and to understand their functional behavior. To achieve this, volumetric images taken by electron microscopy or computer tomography have to be segmented into meaningful semantic regions. We are extending iterative context forests which were developed for 2D image data to image stack segmentation. In particular, our method is able to learn high-order dependencies and import contextual information, which often can not be learned by conventional Markov random field approaches usually used for this task. Our method is tested on very different and challenging medical and biological segmentation tasks.
作者简介
S. Sickert
Computer Vision Group
编辑信件的主要联系方式.
Email: sven.sickert@uni-jena.de
德国, Jena, 07743
E. Rodner
Computer Vision Group
Email: sven.sickert@uni-jena.de
德国, Jena, 07743
J. Denzler
Computer Vision Group
Email: sven.sickert@uni-jena.de
德国, Jena, 07743
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