In modern neurosurgical practice, a surgeon can see a patient’s fiber tracts (nerve tracts) on a monitor in the operating room. This design study investigates the benefit of adding the uncertainty of the tracts and aims to improve the surgeon’s orientation while reducing visual clutter.

Based on an interview with a neurosurgeon and our experience during a surgery visit, we designed, implemented, and evaluated a new visualization method for intraoperative use. We present two new visualization techniques: a partial hull that appears when a surgeon approaches it and a distance meter that serves as a warning tool.

We conclude that there is a need for the intraoperative visualization of fiber uncertainty and recommend doing clinical trials using these methods.

A Camera Submodule for the Environment Observation Module for a Zebro robot was designed. It is able to collect and process data about obstacles on a Raspberry Pi and send this to the main processor of the Environment Observation module using UART.
The two main functions that were implemented are a laser rangefinder based on triangulation and a motion detector based on optical flow. The laser rangefinder calculates the distance to an obstacle by finding its own laser point in a frame. Testing showed that it is especially accurate at short distances (0-4 cm), and the range can differ between 0.30 to 6m due to environmental light and obstacle color.
The motion detector uses dense optical flow data that is provided by the h264 encoder of the Pi Camera. This data is filtered and moving obstacles are grouped together. In the tests, a moving obstacle was detected every time, but 29% also showed false positives due to large egomotion.