Beyin tümörü biyopsisi için derin öğrenme tabanlı risk minimizasyonlu otomatik planlama

Öz

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.

Anahtar Kelimeler

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

Destekleyen Kurum

TÜBİTAK ARDEB

Proje Numarası

122E495

Teşekkür

Araştırmamıza 122E495 proje numarası ile cömertçe destek olan TÜBİTAK ARDEB'e içtenlikle teşekkürlerimizi sunarız. Sağladıkları fon, çalışmamızın gerçekleşmesinde kritik bir rol oynamıştır ve bu nedenle kendilerine derin bir minnettarlık duymaktayız.

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

Öz

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.

Anahtar Kelimeler

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

Proje Numarası

122E495

Kaynakça

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