Automatic lung segmentation for accurate quantitation of volumetric X-ray CT images

By Shiying Hu, E. Hoffman, J. Reinhardt. IEEE Transactions on Medical Imaging, 2001

1,091 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/c85695495b9e5d2848f783e6d8e7f9773b81c34f

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In Biomedical Engineering

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Morphological segmentation and partial volume analysis for volumetry of solid pulmonary lesions in thoracic CT scans (2006, background): “, [7]–[11]) which could provide helpful information about the nodule location (such as proximity to chest wall, lobar fissure,”
A Visual Analytics Approach Using the Exploration of Multidimensional Feature Spaces for Content-Based Medical Image Retrieval (2015, methodology): “We used a well-established adaptive thresholding algorithm with refinements to segment the lung ROIs from the CT [35].”
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 new computationally efficient CAD system for pulmonary nodule detection in CT imagery (2010, methodology): “The lungs are identified by employing a 3-D connected components labeling (6-connectivity voxels) and preserving the largest and the second largest (if present) components in the mask volume (Hu et al., 2001).”
Computer analysis of computed tomography scans of the lung: a survey (2006, background): “tion [12]–[14], or the soft tissue interface is traced by searching for a minimum cost path through the gray values [9]–[11].”
Shape-Based Computer-Aided Detection of Lung Nodules in Thoracic CT Images (2009, methodology): “[23] used optimal thresholding to automatically segment lungs.”
Registration-based estimates of local lung tissue expansion compared to xenon CT measures of specific ventilation (2008, methodology): “Prior to Xe-CT data analysis, the lung region was defined using the method from Hu et al. (2001), followed, when necessary, by manual editing.”
Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problem (2020, background): “machine-learning-based and hybrid approaches. Rule-based methods rely on thresholding, edge detection, region-growing, morphological operations, and other ”classical” image processing techniques [7], [8], [9], [10]. Atlas-based methods rely on non-rigid image registration between the unlabeled image”
Toward automated segmentation of the pathological lung in CT (2005, background): “borders between them (see [11]–[13] and references therein).”
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).”