2022
DOI: 10.21037/qims-22-115
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Segmentation of the prostate, its zones, anterior fibromuscular stroma, and urethra on the MRIs and multimodality image fusion using U-Net model

Abstract: Background: Due to the large variability in the prostate gland of different patient groups, manual segmentation is time-consuming and subject to inter-and intra-reader variations. Hence, we propose a U-Net model to automatically segment the prostate and its zones, including the peripheral zone (PZ), transitional zone (TZ), anterior fibromuscular stroma (AFMS), and urethra on the MRI [T2-weighted (T2W), diffusionweighted (DWI), and apparent diffusion coefficient (ADC)], and multimodality image fusion.Methods: A… Show more

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Cited by 35 publications
(21 citation statements)
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“…Moreover, when the size of the thyroid gland increases, the PTGs are easily deformed by pressure due to their soft texture, thus resulting in a decrease in the display rate. Given the significance of parathyroid glands for the body’s metabolism, it is hoped that multimodality imaging [ 31 , 32 ] will be used to identify normal parathyroid glands in future research and improve the display rate of normal parathyroid glands.…”
Section: Discussionmentioning
confidence: 99%
“…Moreover, when the size of the thyroid gland increases, the PTGs are easily deformed by pressure due to their soft texture, thus resulting in a decrease in the display rate. Given the significance of parathyroid glands for the body’s metabolism, it is hoped that multimodality imaging [ 31 , 32 ] will be used to identify normal parathyroid glands in future research and improve the display rate of normal parathyroid glands.…”
Section: Discussionmentioning
confidence: 99%
“…Treatment planning could optimize dose distributions considering each patient’s toxicity risks by leveraging the predictive model. Collecting multi-region omics data advances precision oncology, proactively mitigating complications to ultimately improve patient outcomes (Rezaeijo et al 2022 ; Whybra et al 2019 ; Jahangirimehr et al 2022 ).…”
Section: Discussionmentioning
confidence: 99%
“…Additionally, given the widespread integration of artificial intelligence in the medical domain, Radiomics emerges as a promising technology with immense potential for investigating the intricate relationship between radiological texture features and clinical outcomes, alongside molecular characteristics [ 28 ]. Radiomics, by extracting comprehensive quantitative data that include both visible and subvisual elements from medical images, offers a more detailed and valuable perspective than traditional visual assessments by physicians [ 29 ]. Deep learning (DL) networks, in contrast to conventional manual segmentation methods, provide accurate, objective automatic segmentation, mitigating errors and limitations inherent in manual processes [ 30 ].…”
Section: Discussionmentioning
confidence: 99%