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Title: Multi-Atlas Segmentation Using Robust Feature-Based Registration
Authors: Fejne, Frida and Landgren, Matilda and Alvén, Jennifer and Ulén, Johannes and Fredriksson, Johan and Larsson, Viktor and Enqvist, Olof and Kahl, Fredrik
Year: 2016
Publication: Cloud-Based Benchmarking ofMedical Image Analysis
Document Type:Book chapter
Status: Published
Refereed: Yes
Publisher: Springer
BibTeX item:BibTeX
Abstract: This paper presents a pipeline which uses a multi-atlas approach for multiorgan segmentation in whole-body CT images. In order to obtain accurate registrations between the target and the atlas images, we develop an adapted feature-based method which uses organ specific features. These features are learnt during an offline pre-processing step, and thus the algorithm still benefits from the speed of feature-based registration methods. These feature sets are then used to obtain pairwise non-rigid transformations using RANSAC, followed by a thin plate spline refinement or NIFTYREG. The fusion of the transferred atlas labels is performed using a random forest classifier, and finally the segmentation is obtained using graph cuts with a Potts model as interaction term. Our pipeline was evaluated on 20 organs in 10 whole-body CT images at the VISCERAL Anatomy Grand Challenge, in conjunction with the International Symposium on Biomedical Imaging, Brooklyn, New York, in April 2015. It performed best on a majority of the organs, with respect to the Dice index.




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