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<p>The seminal vesicle region plays a crucial role in male reproductive health, and its accurate evaluation is essential for diagnosing infertility and carcinoma. Magnetic resonance imaging (MRI) is the primary modality for assessment; however, manual evaluation is time-consuming and subject to interobserver variability, necessitating automated approaches. This study presents a modified ResNet-based deep learning model specifically developed for automated localization of the seminal vesicle region in prostate MRI scans. Unlike segmentation-based methods, the focus is on robust region-level localization as a precursor to detailed analysis. To the best of our knowledge, this is the first study to address seminal vesicle localization directly from MRI using a deep learning model. Performance was evaluated using classification accuracy, inference time, and true positive (TP) coverage, with a sliding window approach to detect high-confidence regions. The modified ResNet-34 achieved the highest TP coverage (0.885) and classification accuracy (0.979), demonstrating improved localization with minimal computational overhead. Heatmap visualizations confirmed the model’s focus on relevant anatomical structures. The proposed approach provides a practical solution for reducing manual effort and interobserver variability, offering a reliable foundation for subsequent segmentation and abnormality detection. Future work may explore the integration of multi-view imaging or 3D CNN architectures to further improve performance.</p>

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A modified deep learning approach for seminal vesicle region localization in prostate MRI

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月16日

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  • Ebru Hasbay,
  • Çağlar Cengizler

摘要

The seminal vesicle region plays a crucial role in male reproductive health, and its accurate evaluation is essential for diagnosing infertility and carcinoma. Magnetic resonance imaging (MRI) is the primary modality for assessment; however, manual evaluation is time-consuming and subject to interobserver variability, necessitating automated approaches. This study presents a modified ResNet-based deep learning model specifically developed for automated localization of the seminal vesicle region in prostate MRI scans. Unlike segmentation-based methods, the focus is on robust region-level localization as a precursor to detailed analysis. To the best of our knowledge, this is the first study to address seminal vesicle localization directly from MRI using a deep learning model. Performance was evaluated using classification accuracy, inference time, and true positive (TP) coverage, with a sliding window approach to detect high-confidence regions. The modified ResNet-34 achieved the highest TP coverage (0.885) and classification accuracy (0.979), demonstrating improved localization with minimal computational overhead. Heatmap visualizations confirmed the model’s focus on relevant anatomical structures. The proposed approach provides a practical solution for reducing manual effort and interobserver variability, offering a reliable foundation for subsequent segmentation and abnormality detection. Future work may explore the integration of multi-view imaging or 3D CNN architectures to further improve performance.