Deep learning-based automatic planning with risk minimization for brain tumor biopsy

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dc.contributor.authorSahin, Mustafa
dc.contributor.authorSahin, Emrullah
dc.contributor.authorOzdemir, Edanur
dc.contributor.authorTalu, Muhammed Fatih
dc.contributor.authorOzturk, Sait
dc.date.accessioned2026-04-04T13:32:58Z
dc.date.available2026-04-04T13:32:58Z
dc.date.issued2025
dc.departmentİnönü Üniversitesi
dc.description.abstractiopsy emerges as a critical procedure for determining tumor types and establishing pathological diagnoses.This process encompasses two primary stages: planning and surgical intervention. During the planning stage,anatomical points in the patient's brain are marked based on MRI data, known to take an average of fourhours. However, the accuracy deficiencies, subjective variations, and time consumption associated withmanual marking reveal the critical need for an automated planning tool. In this study, we propose a biopsyplanning method, entirely automated and incorporating cutting-edge deep learning architectures, on MRIand MRA data. The suggested approach aims to execute biopsy planning rapidly, consistently, andrepeatably. The method consists of four main stages: 1) Removal of the brain's upper shell, 2) Tumordetection and target point determination, 3) Segmentation of the brain's vascular network, and 4) Combination of the three stages and risk calculation for optimal trajectory determination. This automaticmethod has been validated with 42 patient data in ITKTubeTK. Furthermore, this study, prepared as a 3DSlicer plugin, is offered as a free computer-assisted tool for clinics. In subsequent phases of the research,integration of fMRI data is planned to further enhance risk calculation
dc.description.sponsorshipWe would like to express our sincere gratitude to TuBTAK ARDEB for generously supporting our research with project number 122E495. The funding they provided played a critical role in the realization of our work, for which we are deeply grateful.
dc.identifier.doi10.17341/gazimmfd.1348325
dc.identifier.issn1300-1884
dc.identifier.issn1304-4915
dc.identifier.issue1
dc.identifier.orcid0009-0006-1701-4566
dc.identifier.orcid0000-0002-3390-6285
dc.identifier.orcid0000-0002-0311-9838
dc.identifier.orcid0000-0002-7655-0127
dc.identifier.orcid0000-0003-1166-8404
dc.identifier.scopus2-s2.0-85201685319
dc.identifier.scopusqualityQ2
dc.identifier.trdizinid1302370
dc.identifier.urihttps://doi.org/10.17341/gazimmfd.1348325
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/1302370
dc.identifier.urihttps://hdl.handle.net/11616/108836
dc.identifier.volume40
dc.identifier.wosWOS:001329048500006
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakTR-Dizin
dc.language.isoen
dc.publisherGazi Univ, Fac Engineering Architecture
dc.relation.ispartofJournal of the Faculty of Engineering and Architecture of Gazi University
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20250329
dc.subjectStereotactic brain surgery
dc.subjectautomatic surgical trajectory planning
dc.subjectsurgical risk reduction
dc.subjectcomputer-assisted planning
dc.subjectdeep learning
dc.titleDeep learning-based automatic planning with risk minimization for brain tumor biopsy
dc.typeArticle

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