This repository enables the segmentation of prostatic zones (peripheral zone, central gland, AFS and distal prostatic urethra) from T2-weighted MRI. The work was published in 'Towards Patient-Individual PI-RADS v2 Sector Map: CNN for Automatic Segmentation of Prostatic Zones ror T2-Weighted MRI' (ISBI 2019). The preprint can be found in this repository.
Details on the algorithm can be found in the attached paper. The inputs for the preprocessing of the algorithm are the three orthogonal T2-weighted volumes (transversal, coronal and sagittal). They are used to create a ROI that contains the prostate gland. For the CNN only the transversal ROI is of imprortance, the other volumes are not needed. If multi-planar data is not available, the ROI can also be extracted in other ways, e.g. interactively or with a detection algorithm. But this needs to be implemented in future. Data used in this research was obtained from the ProstateX Challenge [1-3]. The outputs of the algorithm are the transversal ROI and the zonal segmentation for this ROI.
To start the segmentation process, run Unet_zones.py.
For commercial use, please contact: office@isg.cs.uni-magdeburg.de
We uploaded the ground truth segmentations created in this work (for 98 Prostate-X T2w axial cases) at: http://isgwww.cs.uni-magdeburg.de/cas/isbi2019.
If you use this data or our trained model for your research, please cite our publication:
A. Meyer, M. Rak, D. Schindele, S. Blaschke, M. Schostak, A. Fedorov, C. Hansen. "Towards patient-individual PI-Rads v2 sector map: CNN for automatic segmentation of prostatic zones from T2-weighted MRI". IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), pp 696-700.
The model was trained on publicly available data from The Cancer Imaging Archive (TCIA) sponsored by the SPIE, NCI/NIH, AAPM, and Radboud University [1]. Whether it works on other data has not been tested yet.
We would like to thank the NVIDIA Corporation for donating the Titan Xp used for this research.
[1] G. Litjens, O. Debats, J. Barentsz, N. Karssemeijer, and H. Huisman. "ProstateX Challenge data", The Cancer Imaging Archive (2017). https://doi.org/10.7937/K9TCIA.2017.MURS5CL
[2] G. Litjens, O. Debats, J. Barentsz, N. Karssemeijer and H. Huisman. "Computer-aided detection of prostate cancer in MRI", IEEE Transactions on Medical Imaging 2014;33:1083-1092.
[3] Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F. The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository, Journal of Digital Imaging, Volume 26, Number 6, December, 2013, pp 1045-1057.