Automated lung segmentation for thoracic CT impact on computer-aided diagnosis.

By S. Armato, W. Sensakovic. Academic Radiology, 2004

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A deep learning framework for supporting the classification of breast lesions in ultrasound images (2017, methodology): “In conventional machine learning methods one of the most important issues (Chen et al 2003, Joo et al 2004, Armato and Sensakovic 2004, Way et al 2006, 2009, Cheng et al 2010, Newell et al 2010, Sun et al 2013) is the extraction of an effective feature for each problem.”
Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problem (2020, unknown): “same time, the segmentation algorithm itself can act as a source of bias. For example, a segmentation algorithm can hamper subsequent analysis by systematically excluding dense areas in the lung ﬁeld [2]. All authors are with the Department of Biomedical Imaging and Imageguided Therapy, Computationa”
Computer analysis of computed tomography scans of the lung: a survey (2006, background): “Armato and Sensakovic [9] illustrated the importance of accurate segmentation as a preprocessing step in a CAD scheme.”