Automated multiscale 3D feature learning for vessels segmentation in Thorax CT images
| Autor: | Jürgen Hesser, Lei Zheng, Thorben Kröger, Christoph S. Garbe, Tomasz Konopczynski |
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| Rok vydání: | 2016 |
| Předmět: |
FOS: Computer and information sciences
Computer science business.industry Computer Vision and Pattern Recognition (cs.CV) Least-angle regression Feature extraction Computer Science - Computer Vision and Pattern Recognition Pattern recognition 02 engineering and technology Image segmentation computer.software_genre 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine Voxel 0202 electrical engineering electronic engineering information engineering Medical imaging 020201 artificial intelligence & image processing Segmentation Artificial intelligence business Feature learning computer Classifier (UML) |
| Zdroj: | 2016 IEEE Nuclear Science Symposium, Medical Imaging Conference and Room-Temperature Semiconductor Detector Workshop (NSS/MIC/RTSD). |
| DOI: | 10.1109/nssmic.2016.8069570 |
| Popis: | We address the vessel segmentation problem by building upon the multiscale feature learning method of Kiros et al., which achieves the current top score in the VESSEL12 MICCAI challenge. Following their idea of feature learning instead of hand-crafted filters, we have extended the method to learn 3D features. The features are learned in an unsupervised manner in a multi-scale scheme using dictionary learning via least angle regression. The 3D feature kernels are further convolved with the input volumes in order to create feature maps. Those maps are used to train a supervised classifier with the annotated voxels. In order to process the 3D data with a large number of filters a parallel implementation has been developed. The algorithm has been applied on the example scans and annotations provided by the VESSEL12 challenge. We have compared our setup with Kiros et al. by running their implementation. Our current results show an improvement in accuracy over the slice wise method from 96.66$\pm$1.10% to 97.24$\pm$0.90%. Published in: 2016 IEEE Nuclear Science Symposium, Medical Imaging Conference and Room-Temperature Semiconductor Detector Workshop (NSS/MIC/RTSD) |
| Databáze: | OpenAIRE |
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