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Advancements in prostate zone segmentation: integrating attention mechanisms into the nnU-Net framework

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Τίτλος: Advancements in prostate zone segmentation: integrating attention mechanisms into the nnU-Net framework
Συγγραφείς: Aleksas Vaitulevičius, Jolita Bernatavičienė, Jurgita Markevičiutė, Ieva Naruševičiūtė, Mantas Trakymas, Povilas Treigys
Πηγή: Machine Learning: Science and Technology, Vol 5, Iss 4, p 045003 (2024)
Στοιχεία εκδότη: IOP Publishing
Έτος έκδοσης: 2024
Συλλογή: Directory of Open Access Journals: DOAJ Articles
Θεματικοί όροι: mpMRI, deep learning, artificial intelligence, prostate zone segmentation, computer vision, Computer engineering. Computer hardware, TK7885-7895, Electronic computers. Computer science, QA75.5-76.95
Περιγραφή: Prostate cancer is one of the most lethal cancers in the world. Early diagnosis is essential for successful treatment of prostate cancer. Segmentation of prostate zones in magnetic resonance images is an important task in the diagnosis of prostate cancer. Currently, the state-of-the-art method for this task is no-new U-Net. In this paper, a method to incorporate the attention U-Net architecture into no-new U-Net is proposed and compared with a classical U-net architecture as research. The experimental results indicate that there is no significant statistical difference between the proposed modification of no-new U-Net with the generalizability of the attention mechanism or the ability to achieve more accurate results. Moreover, two novel workflows are proposed for prostate segmentation, transitional zone segmentation and peripheral zone calculation workflow, and separate models for peripheral zone and transitional zone segmentation workflow. These workflows are compared with a baseline single peripheral zone and transitional zone segmentation model workflow. The experimental results indicate that separate models for peripheral zone and transitional zone segmentation workflow generalizes better than the baseline between data sets of different sources. In peripheral zone segmentation separate models for peripheral zone and transitional zone segmentation workflow achieves 1.9% higher median Dice score coefficient than the baseline workflow when using the attention U-Net architecture and 5.6% higher median Dice score coefficient when using U-Net architecture. Moreover, in transitional zone segmentation separate models for peripheral zone and transitional zone segmentation workflow achieves 0.4% higher median Dice score coefficient than the baseline workflow when using the attention U-Net architecture and 0.7% higher median Dice score coefficient when using U-Net architecture. Meanwhile, prostate segmentation, transitional zone segmentation and peripheral zone calculation workflow generalizes worse than the baseline. ...
Τύπος εγγράφου: article in journal/newspaper
Γλώσσα: English
Relation: https://doi.org/10.1088/2632-2153/ad7f24; https://doaj.org/toc/2632-2153; https://doaj.org/article/7b4799afade34de3ad4433481963b495
DOI: 10.1088/2632-2153/ad7f24
Διαθεσιμότητα: https://doi.org/10.1088/2632-2153/ad7f24
https://doaj.org/article/7b4799afade34de3ad4433481963b495
Αριθμός Καταχώρησης: edsbas.7DB141AB
Βάση Δεδομένων: BASE
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  – Url: https://doi.org/10.1088/2632-2153/ad7f24#
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  Data: Advancements in prostate zone segmentation: integrating attention mechanisms into the nnU-Net framework
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  Data: <searchLink fieldCode="AR" term="%22Aleksas+Vaitulevičius%22">Aleksas Vaitulevičius</searchLink><br /><searchLink fieldCode="AR" term="%22Jolita+Bernatavičienė%22">Jolita Bernatavičienė</searchLink><br /><searchLink fieldCode="AR" term="%22Jurgita+Markevičiutė%22">Jurgita Markevičiutė</searchLink><br /><searchLink fieldCode="AR" term="%22Ieva+Naruševičiūtė%22">Ieva Naruševičiūtė</searchLink><br /><searchLink fieldCode="AR" term="%22Mantas+Trakymas%22">Mantas Trakymas</searchLink><br /><searchLink fieldCode="AR" term="%22Povilas+Treigys%22">Povilas Treigys</searchLink>
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  Data: Machine Learning: Science and Technology, Vol 5, Iss 4, p 045003 (2024)
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  Data: 2024
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  Data: Directory of Open Access Journals: DOAJ Articles
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  Data: <searchLink fieldCode="DE" term="%22mpMRI%22">mpMRI</searchLink><br /><searchLink fieldCode="DE" term="%22deep+learning%22">deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22artificial+intelligence%22">artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22prostate+zone+segmentation%22">prostate zone segmentation</searchLink><br /><searchLink fieldCode="DE" term="%22computer+vision%22">computer vision</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+engineering%2E+Computer+hardware%22">Computer engineering. Computer hardware</searchLink><br /><searchLink fieldCode="DE" term="%22TK7885-7895%22">TK7885-7895</searchLink><br /><searchLink fieldCode="DE" term="%22Electronic+computers%2E+Computer+science%22">Electronic computers. Computer science</searchLink><br /><searchLink fieldCode="DE" term="%22QA75%2E5-76%2E95%22">QA75.5-76.95</searchLink>
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  Data: Prostate cancer is one of the most lethal cancers in the world. Early diagnosis is essential for successful treatment of prostate cancer. Segmentation of prostate zones in magnetic resonance images is an important task in the diagnosis of prostate cancer. Currently, the state-of-the-art method for this task is no-new U-Net. In this paper, a method to incorporate the attention U-Net architecture into no-new U-Net is proposed and compared with a classical U-net architecture as research. The experimental results indicate that there is no significant statistical difference between the proposed modification of no-new U-Net with the generalizability of the attention mechanism or the ability to achieve more accurate results. Moreover, two novel workflows are proposed for prostate segmentation, transitional zone segmentation and peripheral zone calculation workflow, and separate models for peripheral zone and transitional zone segmentation workflow. These workflows are compared with a baseline single peripheral zone and transitional zone segmentation model workflow. The experimental results indicate that separate models for peripheral zone and transitional zone segmentation workflow generalizes better than the baseline between data sets of different sources. In peripheral zone segmentation separate models for peripheral zone and transitional zone segmentation workflow achieves 1.9% higher median Dice score coefficient than the baseline workflow when using the attention U-Net architecture and 5.6% higher median Dice score coefficient when using U-Net architecture. Moreover, in transitional zone segmentation separate models for peripheral zone and transitional zone segmentation workflow achieves 0.4% higher median Dice score coefficient than the baseline workflow when using the attention U-Net architecture and 0.7% higher median Dice score coefficient when using U-Net architecture. Meanwhile, prostate segmentation, transitional zone segmentation and peripheral zone calculation workflow generalizes worse than the baseline. ...
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  Data: https://doi.org/10.1088/2632-2153/ad7f24; https://doaj.org/toc/2632-2153; https://doaj.org/article/7b4799afade34de3ad4433481963b495
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  Data: 10.1088/2632-2153/ad7f24
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  Data: https://doi.org/10.1088/2632-2153/ad7f24<br />https://doaj.org/article/7b4799afade34de3ad4433481963b495
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      – SubjectFull: deep learning
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      – SubjectFull: artificial intelligence
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      – SubjectFull: prostate zone segmentation
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      – SubjectFull: computer vision
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