NeuroIGN: Explainable Multimodal Image-Guided System for Precise Brain Tumor Surgery.
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 m...
| Published in: | Journal of Medical Systems Vol. 48; no. 1; pp. 1 - 12 |
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| Main Authors: | , , , |
| Format: | diagnostic images pictorial research tables/charts Journal Article |
| Published: |
Springer Nature
2/23/2024
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=175634690&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 175634690 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: 2/23/2024 vid: 48 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 175634690 175634690 175634690 10.1007/s10916-024-02037-3 175634690 ppf: 1 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: NeuroIGN: Explainable Multimodal Image-Guided System for Precise Brain Tumor Surgery. aug: au: Zeineldin, Ramy A. Karar, Mohamed E. Burgert, Oliver Mathis-Ullrich, Franziska affil: https://ror.org/00f7hpc57 Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander University Erlangen-Nürnberg, 91052, Erlangen, Germany sug: subj: Brain Neoplasms Surgery Stereotaxic Techniques Methods Surgery, Computer-Assisted Methods Image Interpretation, Computer Assisted Deep Learning Quality Improvement Treatment Outcomes Human Neurosurgery Methods Magnetic Resonance Imaging Methods Algorithms Neural Networks (Computer) Ultrasonography Methods Funding Source ab: 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. pubtype: Academic Journal doctype: diagnostic images pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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