Morphological segmentation and partial volume analysis for volumetry of solid pulmonary lesions in thoracic CT scans

By J. Kuhnigk, V. Dicken, L. Bornemann, A. Bakai, D. Wormanns, S. Krass, H. Peitgen. IEEE Transactions on Medical Imaging, 2006

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The Liver Tumor Segmentation Benchmark (LiTS) (2019, background): “Further, tumor burden is also important in assessing the effectiveness of different treatment and can therefore potentially replace the RECIST protocol [16, 17, 18, 19, 20, 21].”
Fifty years of computer analysis in chest imaging: rule-based, machine learning, deep learning (2017, methodology): “Together with automated nodule detection (discussed above), rule-based nodule segmentation [56, 57], robust emphysema quantiﬁcation [58], and lobe segmentation [59], all elements are in place for automatically performing PanCan malignancy probability assessment for all nodules in a scan.”
Central focused convolutional neural networks: Developing a data-driven model for lung nodule segmentation (2017, background): “For instance, Kuhnigk et al. (2006) showed that the radius of vessels decreases while the vessels evolve along the periphery of the lungs.”
Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The LUNA16 challenge (2016, methodology): “Subsequently, a dedicated nodule segmentation method (Kuhnigk et al. (2006)) is applied to separate nodules from”
Computer-Aided Detection of Prostate Cancer in MRI (2014, methodology): “The SmartOpening-algorithm, which has had successful applications in both nodule segmentation in CT and cancer segmentation in breast MRI, was used [31], [32].”