Statistical segmentation model for accurate electrode positioning in Parkinson’s deep brain stimulation based on clinical low-resolution image data and electrophysiology

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00023752%3A_____%2F24%3A43921277" target="_blank" >RIV/00023752:_____/24:43921277 - isvavai.cz</a>

Alternative codes found

RIV/68378050:_____/24:00585289 RIV/68407700:21230/24:00379522 RIV/00023884:_____/24:00010019

Result on the web

<a href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0298320" target="_blank" >https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0298320</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1371/journal.pone.0298320" target="_blank" >10.1371/journal.pone.0298320</a>

Result language

angličtina

Original language name

Statistical segmentation model for accurate electrode positioning in Parkinson’s deep brain stimulation based on clinical low-resolution image data and electrophysiology

Original language description

Background Deep Brain Stimulation (DBS), applying chronic electrical stimulation of subcortical structures, is a clinical intervention applied in major neurologic disorders. In order to achieve a good clinical effect, accurate electrode placement is necessary. The primary localisation is typically based on presurgical MRI imaging, often followed by intra-operative electrophysiology recording to increase the accuracy and to compensate for brain shift, especially in cases where the surgical target is small, and there is low contrast: e.g., in Parkinson&apos;s disease (PD) and in its common target, the subthalamic nucleus (STN).Methods We propose a novel, fully automatic method for intra-operative surgical navigation. First, the surgical target is segmented in presurgical MRI images using a statistical shape-intensity model. Next, automated alignment with intra-operatively recorded microelectrode recordings is performed using a probabilistic model of STN electrophysiology. We apply the method to a dataset of 120 PD patients with clinical T2 1.5T images, of which 48 also had available microelectrode recordings (MER).Results The proposed segmentation method achieved STN segmentation accuracy around dice = 0.60 compared to manual segmentation. This is comparable to the state-of-the-art on low-resolution clinical MRI data. When combined with electrophysiology-based alignment, we achieved an accuracy of 0.85 for correctly including recording sites of STN-labelled MERs in the final STN volume.Conclusion The proposed method combines image-based segmentation of the subthalamic nucleus with microelectrode recordings to estimate their mutual location during the surgery in a fully automated process. Apart from its potential use in clinical targeting, the method can be used to map electrophysiological properties to specific parts of the basal ganglia structures and their vicinity.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10700 - Other natural sciences

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

PLoS One

ISSN

1932-6203

e-ISSN

1932-6203

Volume of the periodical

19

Issue of the periodical within the volume

3

Country of publishing house

US - UNITED STATES

Number of pages

23

Pages from-to

"Article number e0298320"

UT code for WoS article

001192363700089

EID of the result in the Scopus database

2-s2.0-85187839829