Abstract
This study focuses on comparing deep learning methods for the segmentation and quantification of uncertainty in prostate segmentation from MRI images. The aim is to improve the workflow of prostate cancer detection and diagnosis. Seven different U-Net-based architectures, augmented with Monte-Carlo dropout, are evaluated for automatic segmentation of the central zone, peripheral zone, transition zone, and tumor, with uncertainty estimation. The top-performing model in this study is the Attention R2U-Net, achieving a mean Intersection over Union (IoU) of \(76.3\% \pm 0.003\) and Dice Similarity Coefficient (DSC) of \(85\% \pm 0.003\) for segmenting all zones. Additionally, Attention R2U-Net exhibits the lowest uncertainty values, particularly in the boundaries of the transition zone and tumor, when compared to the other models.
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Acknowledgments
The authors wish to acknowledge the Mexican Council for Science and Technology (CONACYT) for the support in terms of postgraduate scholarships in this project, and the Data Science Hub at Tecnologico de Monterrey for their support on this project. This work has been supported by Azure Sponsorship credits granted by Microsoft’s AI for Good Research Lab through the AI for Health program. The authors would also like to thank the financial support from Tecnologico de Monterrey through the “Challenge-Based Research Funding Program 2022”. Project ID # E120 - EIC-GI06 - B-T3 - D.
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Quihui-Rubio, P.C., Flores-Araiza, D., Ochoa-Ruiz, G., Gonzalez-Mendoza, M., Mata, C. (2023). Assessing the Performance of Deep Learning-Based Models for Prostate Cancer Segmentation Using Uncertainty Scores. In: Ali, S., van der Sommen, F., van Eijnatten, M., Papież, B.W., Jin, Y., Kolenbrander, I. (eds) Cancer Prevention Through Early Detection. CaPTion 2023. Lecture Notes in Computer Science, vol 14295. Springer, Cham. https://doi.org/10.1007/978-3-031-45350-2_7
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