# Automatic Localization of Deep Stimulation Electrodes Using Trajectory-based Segmentation Approach

## TL;DR

This paper presents an automated, threshold-based segmentation method for accurately localizing deep brain stimulation electrodes in CT images, aiding precise VTA identification in Parkinson's disease patients.

## Contribution

It introduces an adaptive threshold segmentation approach for robust electrode localization in noisy CT scans, improving accuracy over manual methods.

## Key findings

- High noise tolerance demonstrated in CT image segmentation
- Automatic threshold detection improves localization accuracy
- Method facilitates better VTA estimation in DBS surgery

## Abstract

Parkinson's disease (PD) is a degenerative condition of the nervous system, which manifests itself primarily as muscle stiffness, hypokinesia, bradykinesia, and tremor. In patients suffering from advanced stages of PD, Deep Brain Stimulation neurosurgery (DBS) is the best alternative to medical treatment, especially when they become tolerant to the drugs. This surgery produces a neuronal activity, a result from electrical stimulation, whose quantification is known as Volume of Tissue Activated (VTA). To locate correctly the VTA in the cerebral volume space, one should be aware exactly the location of the tip of the DBS electrodes, as well as their spatial projection. In this paper, we automatically locate DBS electrodes using a threshold-based medical imaging segmentation methodology, determining the optimal value of this threshold adaptively. The proposed methodology allows the localization of DBS electrodes in Computed Tomography (CT) images, with high noise tolerance, using automatic threshold detection methods.