Blood Harmonisation of Endoscopic Transsphenoidal Surgical Video Frames on Phantom Models.

Mahrukh Saeed, Julien Quarez, Hassna Irzan, Bava Kesavan, Matthew Elliot, Oscar Maccormac, James Knight, Sebastien Ourselin, Jonathan Shapey, Alejandro Granados
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Abstract

Physical phantom models have been integral to surgical training, yet they lack realism and are unable to replicate the presence of blood resulting from surgical actions. Existing domain transfer methods aim to enhance realism, but none facilitate blood simulation. This study investigates the overlay of blood on images acquired during endoscopic transsphenoidal pituitary surgery on phantom models. The process involves employing manual techniques using the GIMP image manipulation application and automated methods using pythons Blend Modes module. We then approach this as an image harmonisation task to assess its practicality and feasibility. Our evaluation uses Structural Similarity Index Measure and Laplacian metrics. The results we obtained emphasize the significance of image harmonisation, offering substantial insights within the surgical field. Our work is a step towards investigating data-driven models that can simulate blood for increased realism during surgical training on phantom models.

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内窥镜经蝶手术视频帧在模型上的血液协调。
物理模型一直是外科手术培训不可或缺的一部分,但它们缺乏真实感,无法复制手术操作过程中产生的血液。现有的域转移方法旨在增强逼真度,但都无法实现血液模拟。本研究调查了在模型上进行内窥镜经蝶垂体手术时获取的图像上叠加血液的情况。这一过程包括使用 GIMP 图像处理应用程序的手动技术和使用 pythons 混合模式模块的自动方法。然后,我们将其作为一项图像协调任务来处理,以评估其实用性和可行性。我们的评估使用了结构相似性指数测量和拉普拉斯度量。我们获得的结果强调了图像协调的重要性,为外科领域提供了实质性的见解。我们的工作是朝着研究数据驱动模型迈出的一步,这些模型可以模拟血液,以提高在模型上进行手术训练时的真实感。
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