title: SEEG assistant: a 3DSlicer extension to support epilepsy surgery.
abstract: BACKGROUND
  In the evaluation of Stereo-Electroencephalography (SEEG) signals, the physicist's workflow involves several operations, including determining the position of individual electrode contacts in terms of both relationship to grey or white matter and location in specific brain regions. These operations are (i) generally carried out manually by experts with limited computer support, (ii) hugely time consuming, and (iii) often inaccurate, incomplete, and prone to errors.
  
  
  RESULTS
  In this paper we present SEEG Assistant, a set of tools integrated in a single 3DSlicer extension, which aims to assist neurosurgeons in the analysis of post-implant structural data and hence aid the neurophysiologist in the interpretation of SEEG data. SEEG Assistant consists of (i) a module to localize the electrode contact positions using imaging data from a thresholded post-implant CT, (ii) a module to determine the most probable cerebral location of the recorded activity, and (iii) a module to compute the Grey Matter Proximity Index, i.e. the distance of each contact from the cerebral cortex, in order to discriminate between white and grey matter location of contacts. Finally, exploiting 3DSlicer capabilities, SEEG Assistant offers a Graphical User Interface that simplifies the interaction between the user and the tools. SEEG Assistant has been tested on 40 patients segmenting 555 electrodes, and it has been used to identify the neuroanatomical loci and to compute the distance to the nearest cerebral cortex for 9626 contacts. We also performed manual segmentation and compared the results between the proposed tool and gold-standard clinical practice. As a result, the use of SEEG Assistant decreases the post implant processing time by more than 2 orders of magnitude, improves the quality of results and decreases, if not eliminates, errors in post implant processing.
  
  
  CONCLUSIONS
  The SEEG Assistant Framework for the first time supports physicists by providing a set of open-source tools for post-implant processing of SEEG data. Furthermore, SEEG Assistant has been integrated into 3D Slicer, a software platform for the analysis and visualization of medical images, overcoming limitations of command-line tools.
authors.affiliation: Department of Informatics, Bioengineering Robotics and System engineering (DIBRIS), University of Genoa, Viale Causa 13, Genova, 16143, Italy.
authors.forename: Massimo
authors.initials: M
authors.lastname: Narizzano
authors.affiliation: Department of Informatics, Bioengineering Robotics and System engineering (DIBRIS), University of Genoa, Viale Causa 13, Genova, 16143, Italy. gabriele.arnulfo@gmail.com.
authors.forename: Gabriele
authors.initials: G
authors.lastname: Arnulfo
authors.affiliation: Departement of Physiology Pharmacology and Neuroscience, CUNY Medical School, New York, New York, USA.
authors.forename: Serena
authors.initials: S
authors.lastname: Ricci
authors.affiliation: Department of Informatics, Bioengineering Robotics and System engineering (DIBRIS), University of Genoa, Viale Causa 13, Genova, 16143, Italy.
authors.forename: Benedetta
authors.initials: B
authors.lastname: Toselli
authors.affiliation: Great Ormond Street Hospital for Children NHS Foundation Trust, Great Ormond St, WC1N 3JH, London, UK.
authors.forename: Martin
authors.initials: M
authors.lastname: Tisdall
authors.affiliation: Department of Informatics, Bioengineering Robotics and System engineering (DIBRIS), University of Genoa, Viale Causa 13, Genova, 16143, Italy.
authors.forename: Andrea
authors.initials: A
authors.lastname: Canessa
authors.affiliation: Department of Informatics, Bioengineering Robotics and System engineering (DIBRIS), University of Genoa, Viale Causa 13, Genova, 16143, Italy.
authors.forename: Marco Massimo
authors.initials: MM
authors.lastname: Fato
authors.affiliation: "Claudio Munari" Center for Epilepsy Surgery, Niguarda Hospital, Milan, Italy.
authors.forename: Francesco
authors.initials: F
authors.lastname: Cardinale
mesh_terms: D000328:Adult; D001921:Brain / Q000000981:diagnostic imaging; D001931:Brain Mapping; D004567:Electrodes, Implanted; D004569:Electroencephalography; D004827:Epilepsy / Q000473:pathology / Q000601:surgery*; D005260:Female; D006801:Humans; D021621:Imaging, Three-Dimensional; D014584:User-Computer Interface
keywords: Automatic segmentation; Epilepsy; Epileptic zone detections; GMPI; Medical imaging; SEEG