# Cooperative Control Robotics and Computer Vision: Development of Semi-Autonomous Temporal Bone and Skull Base Surgery

> **NIH NIH K08** · JOHNS HOPKINS UNIVERSITY · 2021 · $191,792

## Abstract

Project Summary
I am an assistant professor in the department of Otolaryngology-Head and Neck Surgery at the Johns Hopkins
School of Medicine, where my practice is focused on neurotology and lateral skull base surgery. I am applying
for a mentored surgeon-scientist career development award (CDA) to obtain further training in robotics, deep
learning and computer vision. This will further my long-term career goals of improving neurotologic surgical
outcomes through novel applications of engineering methods and ultimately to investigate semi-autonomous,
robotic interventions in the inner ear and skull base which are beyond the limits of the human hand alone.
 Operating in the temporal bone and lateral skull base is technically demanding due to complex three-
dimensional anatomy, small working spaces and delicate neurovascular structures. Many of these challenges
are ideally suited to semi-autonomous surgical platforms to augment a surgeon’s skills with robotic and image-
guided assistance. Despite the widespread implementation of robotic surgery and image guidance in other areas
of the body, the field of neurotology has had relatively little adoption of this technology. We believe one reason
for this is the precise registration needed in this field, where millimeter differences differentiate a successful from
a catastrophic result. This CDA proposes expanding on my prior work investigating cooperative control
robotics and virtual safety barriers by using computer vision and deep learning networks to develop highly
accurate surgical image registration. This CDA aims to provide me with multi-disciplinary training in the
departments of Otolaryngology, Biomedical Engineering and Computer Science. Specific training goals include:
(1) Training in robotics, statistical shape modeling and computer tomography landmark segmentation, (2)
Training in deep learning networks and computer vision video image registration, (3) Integrating this training,
with my knowledge of temporal bone and skull base surgery to develop into an independent investigator (4)
Pursue additional training in the ethical and responsible conduct of research.
 The research plan addresses the hypothesis that virtual safety barriers can be accurately enforced by a
cooperative control robot, and computer vision methods can be used to automate accurate placement and
registration of these safety barriers. I believe that the integration of these techniques will allow for semi-
autonomous surgical methods resulting in improved surgical safety and efficiency. The specific aims of the
proposal are to: (1) Develop and Validate Cooperative Control Robot Enforced Virtual Safety Barriers for Cortical
Mastoidectomy (2) Develop and Test Autonomous Segmentation of Lateral Skull Base Anatomy (3) Develop
Video-Based, Fiducial-less, Surgical Image Registration to Detect and Update the 3-D Position of Temporal
Bone Anatomy from Intraoperative Stereoscopic Microscope Video.

## Key facts

- **NIH application ID:** 10283480
- **Project number:** 1K08DC019708-01
- **Recipient organization:** JOHNS HOPKINS UNIVERSITY
- **Principal Investigator:** Francis Xavier Creighton
- **Activity code:** K08 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $191,792
- **Award type:** 1
- **Project period:** 2021-06-01 → 2026-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10283480

## Citation

> US National Institutes of Health, RePORTER application 10283480, Cooperative Control Robotics and Computer Vision: Development of Semi-Autonomous Temporal Bone and Skull Base Surgery (1K08DC019708-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10283480. Licensed CC0.

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