Prostate MR Image Segmentation

William Glover


Supervised by Frank C Langbein; Moderated by Alia I Abdelmoty

The aim of this project is to investigate approaches towards automatically segmenting the prostate in MRI datasets, based on the PROMISE12 grand challenge, (promise12.grand-challenge.org) and also our own, internal data set (other data sets may also be used, of course). This is part of a research project for early-stage prostate cancer detection. You may develop your own approach or test and then extent already published approaches. Which technique you are using is your choice, but it is likely that a deep learning approach (e.g. U-NET and variants thereof) will perform well.

You must have excellent mathematical and programming skills for this project, an understanding of image processing and related segmentation or machine learning algorithms. Algorithms should be implemented in Python. To be useful for our applications, we need any developed software to run under Linux and it must be released under the GNU AGPL v3 or a compatible license.

Final Report (16/09/2022) [Zip Archive]

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