/TrainEEAr

Tool Tracking to be used in an Endoscopic Endonasal Trainer for Neurosurgical residents

Primary LanguagePython

TrainEEAr

Tool Tracking to be used in an Endoscopic Endonasal Approaches Trainer for Neurosurgical residents

What's the Issue?​

  • Endonasal Endoscopic Approaches (EEA’s) are technically challenging and popular minimally invasive approaches to sinonasal and skull base lesions in both Neurosurgery (NSU) and Otolaryngology (ENT)​
  • NSU & ENT residents have a lower comfort level in these approaches than in open cases, due to the movements, dangers, and lack of training modalities​

Components of the TrainEEAr

  • Anatomically correct anterior and middle cranial fossa (skull base), and sinus structures​
    • Skull base obtained from a CT scan, which was then rendered into a segmented 3D model using 3D slicer​
    • Superior and posterior skull removed for computer vision and electrical access​
    • PLA 3D printing​
  • Computer vision tool tracking to measure tooltip movement and instruct resident how to match expert​
    • Finds max contour in image via masking
    • Kalman filter to smooth tool tracking ​
      • Recursive estimator, noise filter​
  • Electronics to detect tool contact with expert-selected “no go zones” (ICAs, Optic Nerves, Cavernous sinuses)​

Short Youtube Demo

Youtube demo of the Tool Tracking Algorithm (not within the context of the device): https://youtube.com/shorts/aZmWDemw04g?feature=shared