A qualitative and a quantitative analysis of an auto-segmentation module for prostate cancer.

By D. Huyskens, P. Maingon, L. Vanuytsel, V. Remouchamps, T. Roques, B. Dubray, B. Haas, P. Kunz, Thomas Coradi, René Bühlman and 3 more. 2009

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Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges (2019, background): “The results in [61] show that the fully adaption approach has a better average Dice score (ADS) [38] compared to zero and partial adaption in ultrasound kidney segmentation, since the modality has lots of noises and also organ has huge appearance variation.”
Fully Automatic Segmentation of the Prostate using Active Appearance Models (2012, background): “In particular accurate prostate volume measurement combined with prostate-specific antigen level is thought to be a good indicator of treatment outcome [9], and robust measurements of the prostate boundary is an important component of radiotherapy planning [5] It is well known that prostate segmenta”