Paper

Automated Multiscale 3D Feature Learning for Vessels Segmentation in Thorax CT Images

Kröger, Thorben, Garbe, Christoph S, Hesser, Jürgen, Konopczyński, Tomasz, Zheng, Lei

2019

Overview

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\\(\\)1.10% to 97.24\\(\\)0.90%.

• Publisher: Cornell University Library, arXiv.org
• Subject: Annotations / Computed tomography / Feature maps / Image segmentation / Multiscale analysis / Representation learning / Thorax