This work was supported by the funds from the Integrated Diagnostics Program, Department of Radiological Sciences and Pathology, David Geffen School of Medicine, UCLA. Thanks for Dr. Zhong and Dr. Yeejin Lee for the prostate PZ and TZ annotations.
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This is a deep learning-based algorithm for the prostate zonal segmentations (for the paper: "Automatic Prostate Zonal Segmentation Using Fully Convolutional Network With Feature Pyramid Attention" - https://ieeexplore.ieee.org/document/8894451, and ICCV2019 -- https://iccv2019.thecvf.com/program/demos)
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200 patients' MRI (axial view; from ProstateX Datasets (https://wiki.cancerimagingarchive.net/display/Public/SPIE-AAPM-NCI+PROSTATEx+Challenges#23691656b8a499cbc0a24a56b7ea0a7422d51994)) and the prostate zonal segmentation annotations were uploaded.
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label 1 and 2 in the mask indicates the pheripheral and transitional zones, respectively. Their boundaries were enclosed by 255.
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Feel free to use the prostate zonal annotations. If you have any question, please email me (liuyongkai1009@g.ucla.edu).
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References should include the following papers:
"Y. Liu et al., "Automatic Prostate Zonal Segmentation Using Fully Convolutional Network With Feature Pyramid Attention," in IEEE Access, vol. 7, pp. 163626-163632, 2019, doi: 10.1109/ACCESS.2019.2952534"
"Y. Liu et al., "Exploring Uncertainty Measures in Bayesian Deep Attentive Neural Networks for Prostate Zonal Segmentation," in IEEE Access, vol. 8, pp. 151817-151828, 2020, doi: 10.1109/ACCESS.2020.3017168."
Since the public dataset was from ProstateX, you also should cite their paper.
"Litjens G, Debats O, Barentsz J, Karssemeijer N, Huisman H. "Computer-aided detection of prostate cancer in MRI", IEEE Transactions on Medical Imaging 2014;33:1083-1092. DOI: 10.1109/TMI.2014.2303821"
Good luck to your research!