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<p>Skull-base brain metastases pose significant diagnostic and surgical challenges due to their proximity to vital neurological structures. We propose an enhanced Generative Adversarial Network (GAN) model optimised with the Crow Search Algorithm (CSA) to improve detection accuracy and intraoperative decision-making. The GAN framework facilitates high-fidelity image generation and segmentation, while CSA fine-tunes hyperparameters for improved model stability and accuracy. Trained on high-resolution brain MRI datasets with expert annotations, our model achieved a precision of 97.43%, surpassing existing approaches in accuracy and robustness. The system accurately delineates tumour margins and adjacent anatomical structures in real-time, enhancing surgical guidance and reducing operative risks. The inclusion of CSA significantly improved GAN convergence and reduced false positives. This integrated GAN-CSA approach shows promise for revolutionizing neuro-oncology practices by enabling safer and more precise skull base surgeries. As an initial proof-of-concept, the evaluation was limited to 156 MR volumes from a single scanner, and future cross-centre studies will be pursued to establish robustness across varying field strengths, coils, and imaging protocols.</p>

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GAN-CSA: Enhanced Generative Adversarial Networks for Accurate Detection and Surgical Guidance in Skull Base Brain Metastases

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月21日

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  • Surjeet Dalal,
  • Neeraj Dahiya,
  • Shakti Kundu,
  • Amit Verma,
  • Gaytri Devi,
  • Manel Ayadi,
  • Mitiku Dubale,
  • Arshad Hashmi

摘要

Skull-base brain metastases pose significant diagnostic and surgical challenges due to their proximity to vital neurological structures. We propose an enhanced Generative Adversarial Network (GAN) model optimised with the Crow Search Algorithm (CSA) to improve detection accuracy and intraoperative decision-making. The GAN framework facilitates high-fidelity image generation and segmentation, while CSA fine-tunes hyperparameters for improved model stability and accuracy. Trained on high-resolution brain MRI datasets with expert annotations, our model achieved a precision of 97.43%, surpassing existing approaches in accuracy and robustness. The system accurately delineates tumour margins and adjacent anatomical structures in real-time, enhancing surgical guidance and reducing operative risks. The inclusion of CSA significantly improved GAN convergence and reduced false positives. This integrated GAN-CSA approach shows promise for revolutionizing neuro-oncology practices by enabling safer and more precise skull base surgeries. As an initial proof-of-concept, the evaluation was limited to 156 MR volumes from a single scanner, and future cross-centre studies will be pursued to establish robustness across varying field strengths, coils, and imaging protocols.