Authors
Geert Litjens, Robert Toth, Wendy Van De Ven, Caroline Hoeks, Sjoerd Kerkstra, Bram Van Ginneken, Graham Vincent, Gwenael Guillard, Neil Birbeck, Jindang Zhang, Robin Strand, Filip Malmberg, Yangming Ou, Christos Davatzikos, Matthias Kirschner, Florian Jung, Jing Yuan, Wu Qiu, Qinquan Gao, Bianca Maan, Ferdinand van der Heijden, Soumya Ghose, Jhimli Mitra, Jason Dowling, Dean Barratt, Henkjan Huisman, Anant Madabhushi
Publication date
2014/2/1
Journal
Medical image analysis
Volume
18
Issue
2
Pages
359-373
Publisher
Elsevier
Description
Prostate MRI image segmentation has been an area of intense research due to the increased use of MRI as a modality for the clinical workup of prostate cancer. Segmentation is useful for various tasks, eg to accurately localize prostate boundaries for radiotherapy or to initialize multi-modal registration algorithms. In the past, it has been difficult for research groups to evaluate prostate segmentation algorithms on multi-center, multi-vendor and multi-protocol data. Especially because we are dealing with MR images, image appearance, resolution and the presence of artifacts are affected by differences in scanners and/or protocols, which in turn can have a large influence on algorithm accuracy. The Prostate MR Image Segmentation (PROMISE12) challenge was setup to allow a fair and meaningful comparison of segmentation methods on the basis of performance and robustness. In this work we will discuss the initial …
Total citations
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Scholar articles
G Litjens, R Toth, W Van De Ven, C Hoeks, S Kerkstra… - Medical image analysis, 2014