Automatic computer-aided detection of prostate cancer based on multiparametric magnetic resonance image analysis

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Iterative Interaction Training for Segmentation Editing Networks (2018, neutral): “Segmentation is one of the main medical image analysis tasks that when automated substantially facilitates morphological measurements and increase eﬃ-ciency in treatment planning [11,17,18].”
Artificial intelligence in Cancer Imaging: Clinical Challenges and Applications (2019, methodology): “…proton density mapping, 184 or they have only used features extracted from pharmacokinetic analysis and diffusion tensor imaging parameter maps.185 190-192 In the past years, deep learning networks, and particularly CNNs, have been revolutionizing investigative research into prostate cancer…”
Evaluation of prostate segmentation algorithms for MRI: the PROMISE12 challenge (2013, methodology): “In MRI images, the pixel/voxel intensities and therefore appearance characteristics of the prostate can greatly differ between acquisition protocols, ﬁeld strengths and scanners (Dickinson et al., 2011; Barentsz et al., 2012).”
Computer-Aided Detection of Prostate Cancer in MRI (2014, methodology): “[10] recently implemented a two-stage computer-aided detection system for prostate cancer using an initial blob detection approach combined with a candidate segmentation and classification using statistical region features.”