Experience‐based SEEG planning: from retrospective data to automated electrode trajectories suggestions. Issue 5 (14th September 2018)

• Record Type: Journal Article
• Authors: Scorza, Davide; Amoroso, Gaetano; Cortés, Camilo; Artetxe, Arkaitz; Bertelsen, Álvaro; Rizzi, Michele; Castana, Laura; De Momi, Elena; Cardinale, Francesco; Kabongo, Luis
• Is Part Of: Healthcare technology letters. Volume 5:Issue 5(2018)
• Page Start: 167
• Page End: 171
• Publication Date: 2018-09-14
• Subjects: medical signal processing -- biomedical electrodes -- diseases -- data mining -- neurophysiology -- electroencephalography -- brain -- surgery -- medical image processing -- radiation therapy; detecting analysing similar plans -- planning strategy -- main tailored sets -- different patient cases -- experience‐based SEEG planning -- retrospective data -- automated electrode trajectories suggestions -- minimally invasive technique -- multiple intracranial electrodes -- epileptogenic focus -- cumbersome time consuming task -- current approaches -- planning focus -- electrode trajectory optimisation -- geometrical constraints -- initial electrode set -- planning procedure -- analyses retrospective planning data -- average trajectories -- anonymised patients -- 30 exploratory patterns -- 61 mean trajectories -- average brain space -- test set -- manual trajectories -- insertion angle; Biomedical engineering -- Periodicals; Medical technology -- Periodicals; 610.28
• DOI: 10.1049/htl.2018.5075
• Languages: English
• ISSNs: 2053-3713

Abstract

Abstract : StereoElectroEncephaloGraphy (SEEG) is a minimally invasive technique that consists of the insertion of multiple intracranial electrodes to precisely identify the epileptogenic focus. The planning of electrode trajectories is a cumbersome and time‐consuming task. Current approaches to support the planning focus on electrode trajectory optimisation based on geometrical constraints but are not helpful to produce an initial electrode set to begin with the planning procedure. In this work, the authors propose a methodology that analyses retrospective planning data and builds a set of average trajectories, representing the practice of a clinical centre, which can be mapped to a new patient to initialise planning procedure. They collected and analysed the data from 75 anonymised patients, obtaining 30 exploratory patterns and 61 mean trajectories in an average brain space. A preliminary validation on a test set showed that they were able to correctly map 90% of those trajectories and, after optimisation, they have comparable or better values than manual trajectories in terms of distance from vessels and insertion angle. Finally, by detecting and analysing similar plans, they were able to identify eight planning strategies, which represent the main tailored sets of trajectories that neurosurgeons used to deal with the different patient cases.