Bladder volume estimation based on USG images

• Autorzy: Mosorov Volodymyr , Baradziej Daniel , Chodyka Marta

Identyfikatory

DOI

10.24425/ijet.2024.152073

• Języki publikacji: EN

Abstrakty

EN

The article explores deep learning models in urological diagnostics to measure urinary bladder volume from medical images. It addresses the shortcomings of traditional methods by introducing advanced imaging techniques for more objective and precise analysis. The research employs Convolutional Neural Networks (CNNs) and the MONAI platform for image segmentation and analysis, using data from The Cancer Imaging Archive to focus on urological regions. Findings suggest these models enhance diagnostic accuracy but also highlight the need for further modifications to tailor them to specific medical data, underscoring machine learning's significant role in accurate medical assessments for urology.

Słowa kluczowe

EN

deep learning; bladder volume estimation; medical imaging convolutional neural networks; image segmentation; MONAI platform; diagnostic accuracy

• Wydawca: Polish Academy of Sciences, Committee of Electronics and Telecommunication
• Czasopismo: International Journal of Electronics and Telecommunications
• Rocznik: 2024
• Tom: Vol. 70, No. 4
• Strony: 879--886
• Opis fizyczny: Bibliogr. 29 poz., rys., tab.

Twórcy

autor

Mosorov Volodymyr

• Lodz University of Technology

autor

Baradziej Daniel

• Lodz University of Technology

autor

Chodyka Marta

• John Paul II University in Biala Podlaska

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).