Automated model-based vertebra detection, identification, and segmentation in CT images

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NAS-Unet: Neural Architecture Search for Medical Image Segmentation (2019, methodology): “region based method [1] or threshold based method [2]) and mathematical modeling to construct compound rulebased systems that solved particular tasks [3].”
VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images (2020, background): “Landmark frameworks tackling fully automated vertebral labelling and segmentation from a shape-modelling perspective exist (Klinder et al., 2009; Korez et al., 2015).”
Detection and Labeling of Vertebrae in MR Images Using Deep Learning with Clinical Annotations as Training Data (2017, background): “However, several of these have required training data in the form of fully segmented vertebrae [11 – 14], others with annotations in the form of bounding boxes [15 – 17] and just a few requiring only the position of each vertebra [18, 19].”
A visual analytics approach to understanding cycling behaviour (2012, neutral): “An introduction to C-IL 2 P is also presented, followed by a review of propositionalization and feature selection.”