Abstract
Automatic segmentation of prostate gland in magnetic resonance (MR) images is a challenging task due to large variations of prostate shapes and indistinct boundaries with adjacent tissues. In this paper, we propose an automatic pipeline to segment prostate gland in diffusion magnetic resonance images (dMRI). The most common approach for segmenting prostate in MR images is based on image registration, which is computationally expensive and solely relies on the pre-segmented images (also known as atlas). In contrast, the proposed method uses a clustering method applied to the dMRI to separate prostate gland from the surrounding tissues followed by a postprocessing stage via active contours. The proposed pipeline was validated on prostate MR images of 25 patients and the segmentation results were compared to manually delineated prostate contours. The proposed method achieves an overall accuracy with mean Dice Similarity Coefficient (DSC) of 0.84\(\ \pm \ \)0.04, while being the most effective in the middle prostate gland producing a mean DSC of 0.91\(\ \pm \ \)0.03. The proposed method has the potential to be integrated into clinical decision support systems that aid radiologists in monitoring prostate cancer.
F. Khalvati—This work was partially supported by Ontario Institute of Cancer Research (OICR) and Cancer Care Ontario (CCO) - Imaging Network of Ontario (CINO).
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Zhang, J., Baig, S., Wong, A., Haider, M.A., Khalvati, F. (2017). Segmentation of Prostate in Diffusion MR Images via Clustering. 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_52
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DOI: https://doi.org/10.1007/978-3-319-59876-5_52
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