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MRI-CropNet for Automated Crop** of Prostate Cancer in Magnetic Resonance Imaging

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Abstract

Auto-crop** of the prostate is one f the most significant tasks to detect the desired Region of Interest (ROI) and reduce the image size for increased accuracy of Computer Aided Diagnosis (CAD) systems for furthermore detection of Prostate Cancer. Prostate cancer (PCa) is responsible for many deaths worldwide and CAD systems can help radiologists in evaluating prostate cancer using Magnetic Resonance Imaging (MRI), as it offers improved visualization of soft tissues as compared to other imaging modalities. PCa is more commonly found in the prostate's Peripheral zone (PZ) rather than the Transition zone (TZ) or Central zone (CZ). By implementing advanced auto-crop** and denoising techniques, this research can lead to more precise identification of prostate cancer regions, significantly reducing the rate of misdiagnosis and ensuring patients receive appropriate treatments earlier.Automating the tedious and error-prone tasks of image crop** and noise reduction can free up valuable time for clinicians and radiologists, allowing them to focus more on patient care rather than image analysis.Thus, there exist different assessment criteria for diverse regions within the Prostate imaging announcing and information framework (PI-RADS). In examining suspected prostate cancer, PI-RADS is a standardized reporting scheme for multiparametric prostate MRI. Traditional approaches used by doctors were manual and time-consuming, thus CAD systems significantly helped in the early detection while doctors and radiologists were at ease. This paper proposes a deep learning-based auto crop** methodology named MRI-CropNet for automatic crop** of PCa region from MRI. This architecture involves an increase in the dilation rate in the newly added patch of convolution layer over the state of art methods. The expansion in filter size guarantees more adaptable element extraction, thus an increment in the exactness of the result. Based on the experimental analysis, values of Mathews correlation coefficient (MCC), Dice similarity coefficient (DSC), F1-Score, Average precision (AP), and Loss for auto-crop** using MRI-CropNet were observed to be 0.98, 0.99, 0.98, 98.81 and 0.07 respectively and therefore giving an edge to the proposed approach while outperforming state of the art approaches. Auto crop** is one of the most crucial steps in the detection of PCa because it is crucial that it be found early on so that treatment can begin promptly. To develop preventive measures, improve treatment outcomes, and ultimately mitigate the impact of this widespread illness, urgent research and awareness campaigns are imperative. A deep learning-based auto crop** methodology was suggested in this paper, which helped create a system that was quicker and performed better. Tailored solutions for prostate cancer detection employing deep learning techniques are imperative due to inherent anatomical and histological heterogeneity within the prostate gland. Tumors exhibit diverse morphological characteristics across glandular zones, influencing their appearance, size, and grade. Deep learning algorithms trained on region-specific datasets can effectively capture these nuanced features, optimizing detection accuracy.

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Acknowledgements

The authors are grateful to the Ministry of Human Resource Development (MHRD), Govt. of India for funding this project (17-11/2015-PN-1) under the sub-theme Medical Devices & Restorative Technologies (MDaRT) of the Design and Innovation Centre (DIC).

Funding

The project was funded by the Ministry of Human Resource Development (MHRD), Govt. of India with grant number (17–11/2015-PN-1).

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  1. University Institute of Engineering and Technology, Panjab University, Chandigarh, India

    Mamta Juneja, Sumindar Kaur Saini, Chirag Chanana & Prashant **dal

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Juneja, M., Saini, S.K., Chanana, C. et al. MRI-CropNet for Automated Crop** of Prostate Cancer in Magnetic Resonance Imaging. Wireless Pers Commun (2024). https://doi.org/10.1007/s11277-024-11335-5

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