gummfd

Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi

1300-1884 1304-4915

Gazi Üniversitesi

10.17341/gazimmfd.1348325

Computer Vision Image Processing Deep Learning Artificial Intelligence (Other) Biomedical Imaging Biomedical Diagnosis

Bilgisayar Görüşü Görüntü İşleme Derin Öğrenme Yapay Zeka (Diğer) Biyomedikal Görüntüleme Biyomedikal Tanı

Beyin tümörü biyopsisi için derin öğrenme tabanlı risk minimizasyonlu otomatik planlama

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

https://orcid.org/0009-0006-1701-4566

Şahin

Mustafa

İnönü Üniversitesi Fen Bilimleri Enstitüsü

https://orcid.org/0000-0002-3390-6285

Şahin

Emrullah

KÜTAHYA DUMLUPINAR ÜNİVERSİTESİ, MÜHENDİSLİK FAKÜLTESİ, YAZILIM MÜHENDİSLİĞİ

https://orcid.org/0000-0002-0311-9838

Özdemir

Edanur

İNÖNÜ ÜNİVERSİTESİ, MÜHENDİSLİK FAKÜLTESİ, BİLGİSAYAR MÜHENDİSLİĞİ BÖLÜMÜ

https://orcid.org/0000-0003-1166-8404

Talu

Fatih

İNÖNÜ ÜNİVERSİTESİ, MÜHENDİSLİK FAKÜLTESİ, BİLGİSAYAR MÜHENDİSLİĞİ BÖLÜMÜ

https://orcid.org/0000-0002-7655-0127

Öztürk

Sait

FIRAT ÜNİVERSİTESİ, TIP FAKÜLTESİ

08 16 2024

40 1 487 500 08 23 2023 03 23 2024

1986

Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi

Biyopsi, tümör türünün belirlenmesi ve patolojik teşhisin konulması için kritik bir işlem olarak karşımıza çıkar. Bu süreç, özellikle tümörlü yapıdan parça alınarak gerçekleştirilen planlama ve cerrahi müdahale olmak üzere iki temel aşamayı içerir. Planlama aşamasında, MRI verisi üzerinden hastanın beynindeki anatomik noktaların işaretlemesi yapılır ve bu sürecin ortalama dört saat sürdüğü bilinmektedir. Ancak, manuel işaretlemeyle yapılan bu tür planlamaların doğruluk eksiklikleri, sübjektif varyasyonlar ve zaman alıcılığı, otomatik bir planlama aracının kritik bir ihtiyaç olduğunu göstermektedir. Bu çalışmada, MRI ve MRA verisi üzerinden tam otomatik, son teknoloji derin öğrenme mimarilerini içeren bir biyopsi planlama yöntemi önerilmektedir. Önerilen bu yöntem, biyopsi planlamasını hızlı, tutarlı ve tekrarlanabilir bir şekilde gerçekleştirmeyi amaçlamaktadır. Yöntem dört ana aşamadan oluşmaktadır: 1-) Beyinin üst kabuk bölgesinin çıkarılması, 2-) Tümör tespiti ve hedef noktasının belirlenmesi, 3-) Beyin damar ağacının bölütlenmesi, 4-) Optimum yörünge tespiti için üç aşamanın kombinasyonu ve risk hesaplanması. Bu otomatik yöntem, ITKTubeTK'deki 42 hasta verisiyle doğrulanmıştır. Ayrıca, "3D Slicer" eklentisi olarak hazırlanan bu çalışma, klinikler için ücretsiz bir bilgisayar destekli araç olarak sunulmaktadır. Araştırmanın ilerleyen aşamalarında, risk hesaplamasını daha da geliştirmek amacıyla fMRI verisinin entegrasyonu üzerine çalışılması planlanmaktadır.

Biopsy 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 four hours. However, the accuracy deficiencies, subjective variations, and time consumption associated with manual marking reveal the critical need for an automated planning tool. In this study, we propose a biopsy planning method, entirely automated and incorporating cutting-edge deep learning architectures, on MRI and MRA data. The suggested approach aims to execute biopsy planning rapidly, consistently, and repeatably. The method consists of four main stages: 1) Removal of the brain's upper shell, 2) Tumor detection 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 automatic method has been validated with 42 patient data in ITKTubeTK. Furthermore, this study, prepared as a "3D Slicer" 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.

Stereotaktik beyin cerrahisi otomatik cerrahi yörünge planlama cerrahi risk azaltma bilgisayar destekli planlama derin öğrenme

Stereotactic brain surgery automatic surgical trajectory planning surgical risk reduction computer-assisted planning deep learning

TÜBİTAK ARDEB

122E495

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