Abstract
Prostate cancer is a leading cause of mortality among men. Diffusion-weighted magnetic resonance imaging (DW-MRI) has shown to be successful at monitoring and detecting prostate tumors. The clinical guidelines to interpret DW-MRI for prostate cancer requires the segmentation of the prostate gland into different zones. Moreover, computer-aided detection tools which are designed to detect prostate cancer automatically, usually require the segmentation of prostate gland as a preprocessing step. In this paper, we present a segmentation algorithm for delineation of the prostate gland in DW-MRI via fully convolutional neural network. The segmentation algorithm was applied to images of 30 (testing) and 104 (training) patients and a median Dice Similarity Coefficient of 0.89 was achieved. This method is faster and returns similar results compared to registration based methods; although it has the potential to produce improved results given a larger training set.
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Clark, T., Wong, A., Haider, M.A., Khalvati, F. (2017). Fully Deep Convolutional Neural Networks for Segmentation of the Prostate Gland in Diffusion-Weighted MR Images. In: Karray, F., Campilho, A., Cheriet, F. (eds) Image Analysis and Recognition. ICIAR 2017. Lecture Notes in Computer Science(), vol 10317. Springer, Cham. https://doi.org/10.1007/978-3-319-59876-5_12
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DOI: https://doi.org/10.1007/978-3-319-59876-5_12
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