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Consistent segmentation of repeat CT scans for growth assessment in pulmonary nodules., , , , and . Medical Imaging: Image Processing, volume 3661 of SPIE Proceedings, SPIE, (1999)Evaluation of 1D, 2D and 3D nodule size estimation by radiologists for spherical and non-spherical nodules through CT thoracic phantom imaging., , , , , , , , , and 2 other author(s). Medical Imaging: Computer-Aided Diagnosis, volume 7963 of SPIE Proceedings, page 79630D. SPIE, (2011)A Comparison of Lung Nodule Segmentation Algorithms: Methods and Results from a Multi-institutional Study., , , , , , , , and . J. Digital Imaging, 29 (4): 476-487 (2016)The effects of iterative reconstruction in CT on low-contrast liver lesion volumetry: a phantom study., , , , , , , and . Medical Imaging: Computer-Aided Diagnosis, volume 10134 of SPIE Proceedings, page 101340Z. SPIE, (2017)Automated segmentation of mesothelioma volume on CT scan., , , , , , and . Medical Imaging: Image Processing, volume 5747 of SPIE Proceedings, SPIE, (2005)Implementation Strategy of a CNN Model Affects the Performance of CT Assessment of EGFR Mutation Status in Lung Cancer Patients., , , , , , and . IEEE Access, (2019)Effects of quantization and truncation strategies on image quality during lossy image compression., and . Medical Imaging: Image Processing, volume 3031 of SPIE Proceedings, SPIE, (1997)Automatic Detection and Segmentation of Ground Glass Opacity Nodules., , , , , and . MICCAI (1), volume 4190 of Lecture Notes in Computer Science, page 784-791. Springer, (2006)