Abstract
In 2020 1,414,259 new cases and 375,304 deaths were estimated for prostate cancer worldwide. Diagnosis of prostate cancer is primarily based on prostate-specific antigen (PSA) screening and trans-rectal ultrasound (TRUS)-guided prostate biopsy. PSA has a low specificity of 36% since benign conditions can elevate the PSA levels. The data set used for prostate cancer consists of t2-weighted MR images for 1,151 patients and 61,119 images. This paper presents an approach to applying knowledge-based artificial intelligence together with image segmentation to improve the diagnosis of prostate cancer using publicly available data. Complete and reliable segmentation into the transition zone (TZ) and peripheral zone (PZ) is required in order to automate and enhance the process of prostate cancer diagnosis.
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Jacobson, L.E.O., Hopgood, A.A., Bader-El-Den, M., Tamma, V., Prendergast, D., Osborn, P. (2023). Hybrid System for Prostate MR Image Segmentation Using Expert Knowledge and Machine Learning. In: Bramer, M., Stahl, F. (eds) Artificial Intelligence XL. SGAI 2023. Lecture Notes in Computer Science(), vol 14381. Springer, Cham. https://doi.org/10.1007/978-3-031-47994-6_43
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DOI: https://doi.org/10.1007/978-3-031-47994-6_43
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