Computer-Aided Detection of Prostate Cancer in MRI

By Geert J. S. Litjens, Oscar A. Debats, Jelle O. Barentsz, Nico Karssemeijer, Henkjan J. Huisman. IEEE Transactions on Medical Imaging, 2014

467 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Pulmonary and Respiratory Medicine

View Paper: https://www.semanticscholar.org/paper/1b02bd4ef5cbf6d6fe99c1e52814ae27c17cf96b

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In Artificial Intelligence

In Pulmonary and Respiratory Medicine

Citations in the corpus

Artificial intelligence in Cancer Imaging: Clinical Challenges and Applications (2019, methodology): “…including T2-weighted MRI, the apparent diffusion coefficient, high b-values, diffusion-weighted MRI, and T2 estimation by 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…”
Multi-Atlas Segmentation of Biomedical Images: A Survey (2014, methodology): “MAS has also been used to segment brain MRI in animal studies, e.g., mice (Da et al., 2012; Ma et al., 2014; Nie and Shen, 2013) and non-human primates (Ballanger et al., 2013).”
Generalizing Deep Learning for Medical Image Segmentation to Unseen Domains via Deep Stacked Transformation (2020, methodology): “Task 1: Four publicly available 3D prostate MRI datasets are used: MSD-Prostate (MSD-P), 2 PROMISE12 3 [36], NCI-ISBI13, 4 and ProstateX 5 [37].”
Federated learning for medical image analysis: A survey (2023, background): “(2021) [143] Prostate segmentation MSD-Prostate [144] PROMISE12 [135] ProstateX [139] NCI-ISBI 2013 [136] MRI U-Net”
Automated diagnosis of prostate cancer in multi-parametric MRI based on multimodal convolutional neural networks (2017, methodology): “MRI protocols for data of the TCIA dataset are provided in Litjens et al (2014).”
Confidence Calibration and Predictive Uncertainty Estimation for Deep Medical Image Segmentation (2019, methodology): “(A) For prostate segmentation, samples are marked by their domain: PROSTATEx (source domain), and the multi-device multi-institutional PROMISE12 dataset (target domain).”
Computer Aided-Diagnosis of Prostate Cancer on Multiparametric MRI: A Technical Review of Current Research (2014, methodology): “used intensity, texture, shape (blobness), anatomy, and pharmacokinetic features on T2, protondensity weighted, diffusion weighted, and DCE images [57].”