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Precise neurosurgical guidance is critical for successful brain surgeries and plays a vital role in all phases of image-guided neurosurgery (IGN). Neuronavigation software enables real-time tracking of surgical tools, ensuring their presentation with high precision in relation to a virtual patient model. Therefore, this work focuses on the development of a novel multimodal IGN system, leveraging deep learning and explainable AI to enhance brain tumor surgery outcomes. The study establishes the clinical and technical requirements of the system for brain tumor surgeries. NeuroIGN adopts a modular architecture, including brain tumor segmentation, patient registration, and explainable output prediction, and integrates open-source packages into an interactive neuronavigational display. The NeuroIGN system components underwent validation and evaluation in both laboratory and simulated operating room (OR) settings. Experimental results demonstrated its accuracy in tumor segmentation and the success of ExplainAI in increasing the trust of medical professionals in deep learning. The proposed system was successfully assembled and set up within 11 min in a pre-clinical OR setting with a tracking accuracy of 0.5 (± 0.1) mm. NeuroIGN was also evaluated as highly useful, with a high frame rate (19 FPS) and real-time ultrasound imaging capabilities. In conclusion, this paper describes not only the development of an open-source multimodal IGN system but also demonstrates the innovative application of deep learning and explainable AI algorithms in enhancing neuronavigation for brain tumor surgeries. By seamlessly integrating pre- and intra-operative patient image data with cutting-edge interventional devices, our experiments underscore the potential for deep learning models to improve the surgical treatment of brain tumors and long-term post-operative outcomes.

Titel:	NeuroIGN: Explainable Multimodal Image-Guided System for Precise Brain Tumor Surgery.
Autor/in / Beteiligte Person:	Zeineldin, RA ; Karar, ME ; Burgert, O ; Mathis-Ullrich, F
Link:	Full Text Finder (Volltext)
Zeitschrift:	Journal of medical systems, Jg. 48 (2024-02-23), Heft 1, S. 25
Veröffentlichung:	1999- : New York, NY : Kluwer Academic/Plenum Publishers ; <i>Original Publication</i>: New York, Plenum Press., 2024
Medientyp:	academicJournal
ISSN:	1573-689X (electronic)
DOI:	10.1007/s10916-024-02037-3
Schlagwort:	Humans Neuronavigation methods Neurosurgical Procedures methods Ultrasonography Magnetic Resonance Imaging methods Brain Neoplasms diagnostic imaging Brain Neoplasms surgery Brain Neoplasms pathology Surgery, Computer-Assisted methods
Sonstiges:	Nachgewiesen in: MEDLINE Sprachen: English Publication Type: Journal Article Language: English [J Med Syst] 2024 Feb 23; Vol. 48 (1), pp. 25. <i>Date of Electronic Publication: </i>2024 Feb 23. MeSH Terms: Brain Neoplasms* / diagnostic imaging ; Brain Neoplasms* / surgery ; Brain Neoplasms* / pathology ; Surgery, Computer-Assisted* / methods ; Humans ; Neuronavigation / methods ; Neurosurgical Procedures / methods ; Ultrasonography ; Magnetic Resonance Imaging / methods References: Schipmann-Miletić S, Stummer W (2020) Image-Guided Brain Surgery. In: Molecular Imaging in Oncology. Recent Results in Cancer Research. pp 813–841. https://doi.org/10.1007/978-3-030-42618-7_26. ; Miner RC (2017) Image-Guided Neurosurgery. J Med Imaging Radiat Sci 48 (4):328–335. https://doi.org/10.1016/j.jmir.2017.06.005. (PMID: 10.1016/j.jmir.2017.06.00531047466) ; Sanai N, Polley MY, McDermott MW, Parsa AT, Berger MS (2011) An extent of resection threshold for newly diagnosed glioblastomas. 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(PMID: 10.1007/s11548-021-02374-5339379668295168) Grant Information: 91705803 Deutscher Akademischer Austausch Dienst Kairo Contributed Indexing: Keywords: Deep learning; Explainability; IGN; MRI; Neuronavigation; iUS Entry Date(s): Date Created: 20240223 Date Completed: 20240226 Latest Revision: 20240226 Update Code: 20240226

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NeuroIGN: Explainable Multimodal Image-Guided System for Precise Brain Tumor Surgery.