ConsisTNet: a spatio-temporal approach for consistent anatomical localization in endoscopic pituitary surgery.

Journal: International journal of computer assisted radiology and surgery
Published Date: Apr 29, 2025

Abstract

PURPOSE: Automated localization of critical anatomical structures in endoscopic pituitary surgery is crucial for enhancing patient safety and surgical outcomes. While deep learning models have shown promise in this task, their predictions often suffer from frame-to-frame inconsistency. This study addresses this issue by proposing ConsisTNet, a novel spatio-temporal model designed to improve prediction stability.

Authors

  • Zhehua Mao
    Department of Computer Science, University College London, London, UK. z.mao@ucl.ac.uk.
  • Adrito Das
    Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, United Kingdom. adrito.das.20@ucl.ac.uk.
  • Danyal Z Khan
    Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, University College, London, United Kingdom; Wellcome EPSRC Centre for Interventional and Surgical Sciences, University College, London, United Kingdom.
  • Simon C Williams
    Department of Neurosurgery, St George's Hospital, London, United Kingdom.
  • John G Hanrahan
    Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, United Kingdom.
  • Danail Stoyanov
    University College London, London, UK.
  • Hani J Marcus
    The Hamlyn Centre for Robotic Surgery, Institute of Global Health Innovation, Imperial College London, Paterson Building (Level 3), Praed Street, London, W2 1NY, UK, hani.marcus10@imperial.ac.uk.
  • Sophia Bano
    Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) and Department of Computer Science, University College London, London, UK. sophia.bano@ucl.ac.uk.

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