Efficient Prealignment of CT Scans for Registration through a Bodypart Regressor

2019/09/19 by Hans Meine, Meine, Hans, Alessa Hering +1
Computer Science · Engineering · #AI in cancer detection #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Machine Learning (cs.LG) #Medical Image Segmentation Techniques #Medical Imaging and Analysis #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.1909.08898

openalex created_date 2019/07/12 · openalex publication_date 2019/09/19 · openalex updated_date 2026/07/28

Abstract

Convolutional neural networks have not only been applied for classification of voxels, objects, or images, for instance, but have also been proposed as a bodypart regressor. We pick up this underexplored idea and evaluate its value for registration: A CNN is trained to output the relative height within the human body in axial CT scans, and the resulting scores are used for quick alignment between different timepoints. Preliminary results confirm that this allows both fast and robust prealignment compared with iterative approaches.

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