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| Evaluation of Semi-automatic Segmentation Methods for Persistent Ground Glass Nodules on Thin-Section CT Scans |
| Young Jae Kim, Seung Hyun Lee, Chang Min Park, Kwang Gi Kim |
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Healthc Inform Res. 2016;22(4):305-315. Published online October 31, 2016 DOI: https://doi.org/10.4258/hir.2016.22.4.305 |
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Evaluation of Semi-automatic Segmentation Methods for Persistent Ground Glass Nodules on Thin-Section CT Scans Healthcare Informatics Research. 2016;22(4):305  Clinical, pathological and thin-section CT features of persistent multiple ground-glass opacity nodules: Comparison with solitary ground-glass opacity nodule Lung Cancer. 2009;64(2):171-178 Persistent Pulmonary Nodular Ground-Glass Opacity at Thin-Section CT: Histopathologic Comparisons Yearbook of Diagnostic Radiology. 2008;2008:25-28 Predictive CT findings of malignancy in ground-glass nodules on thin-section chest CT: the effects on radiologist performance European Radiology. 2008;19(3):552-560 Automatic Atlas-Free Multiorgan Segmentation of Contrast-Enhanced CT Scans Cloud-Based Benchmarking of Medical Image Analysis. 2017;145-164 Clinical evaluation of semi-automatic landmark-based lesion tracking software for CT-scans Cancer Imaging. 2014;14(1): Automatic segmentation of small pulmonary nodules on multidetector-row CT images International Congress Series. 2003;1256:1389 Usefulness of model-based iterative reconstruction in semi-automatic volumetry for ground-glass nodules at ultra-low-dose CT: a phantom study Radiological Physics and Technology. 2018;11(2):235-241 Automatic lung segmentation from thoracic computed tomography scans using a hybrid approach with error detection Medical Physics. 2009;36(7):2934-2947 Automatic segmentation of phase-correlated CT scans through nonrigid image registration using geometrically regularized free-form deformation Medical Physics. 2007;34(7):3054-3066 |
