Recent Automatic Segmentation Algorithms of MRI Prostate Regions: A Review

Khan, Z. and Yahya, N. and Alsaih, K. and Al-Hiyali, M.I. and Meriaudeau, F. (2021) Recent Automatic Segmentation Algorithms of MRI Prostate Regions: A Review. IEEE Access, 9 . pp. 97878-97905.

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World-wide incidence rate of prostate cancer has progressively increased with time especially with the increased proportion of elderly population. Early detection of prostate cancer when it is confined to the prostate gland has the best chance of successful treatment and increase in surviving rate. Prostate cancer occurrence rate varies over the three prostate regions, peripheral zone (PZ), transitional zone (TZ), and central zone (CZ) and this characteristic is one of the important considerations is development of segmentation algorithm. In fact, the occurrence rate of cancer PZ, TZ and CZ regions is respectively. at 70-80, 10-20, 5 or less. In general application of medical imaging, segmentation tasks can be time consuming for the expert to delineate the region of interest, especially when involving large numbers of images. In addition, the manual segmentation is subjective depending on the expert's experience. Hence, the need to develop automatic segmentation algorithms has rapidly increased along with the increased need of diagnostic tools for assisting medical practitioners, especially in the absence of radiologists. The prostate gland segmentation is challenging due to its shape variability in each zone from patient to patient and different tumor levels in each zone. This survey reviewed 22 machine learning and 88 deep learning-based segmentation of prostate MRI papers, including all MRI modalities. The review coverage includes the initial screening and imaging techniques, image pre-processing, segmentation techniques based on machine learning and deep learning techniques. Particular attention is given to different loss functions used for training segmentation based on deep learning techniques. Besides, a summary of publicly available prostate MRI image datasets is also provided. Finally, the future challenges and limitations of current deep learning-based approaches and suggestions of potential future research are also discussed. © 2013 IEEE.

Item Type:Article
Impact Factor:cited By 0
Uncontrolled Keywords:Deep learning; Diagnosis; Diseases; Learning systems; Magnetic resonance imaging; Medical imaging; Urology, Automatic segmentations; General applications; Learning-based approach; Learning-based segmentation; Medical practitioner; Segmentation algorithms; Segmentation techniques; Shape variabilities, Image segmentation
ID Code:23937
Deposited By: Ms Sharifah Fahimah Saiyed Yeop
Deposited On:19 Aug 2021 13:23
Last Modified:19 Aug 2021 13:23

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