Toward automated segmentation of the pathological lung in CT

By Ingrid C. Sluimer, Mathias Prokop, B. van Ginneken. IEEE Transactions on Medical Imaging, 2005

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Combination Strategies in Multi-Atlas Image Segmentation: Application to Brain MR Data (2009, methodology): “Apart from SI, the mean average surface distance (MASD) between the segmented structures was also evaluated [ 2 ], [31].”
Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problem (2020, unknown): “They can be categorized into rule-based [8], [9], [10], [11], atlas-based [12], [13], [14], ML-based [15],”
Evaluation of Optimization Methods for Nonrigid Medical Image Registration Using Mutual Information and B-Splines (2007, methodology): “The segmentations were made by means of a region-growing method based on the work of Hu et al. [1], [47].”
Adaptive Stochastic Gradient Descent Optimisation for Image Registration (2009, methodology): “The lung airways were segmented using an automatic region-growing algorithm, described in (Hu et al. 2001; Sluimer et al. 2005).”
A large-scale evaluation of automatic pulmonary nodule detection in chest CT using local image features and k-nearest-neighbour classification (2009, methodology): “Lung segmentation was carried out using an algorithm by Sluimer et al. (2005) based on that of Hu et al. (2001).”