Ekokardiyografi Görüntülerinde Aort Kapak Kalsifikasyon Segmentasyonu için Veri Artırma Yöntemlerinin İrdelenmesi

Year 2024, Volume: 14 Issue: 3, 1640 - 1653, 15.09.2024

Elif Baykal Kablan

https://doi.org/10.31466/kfbd.1534186

Abstract

Aort kapak kalsiyum skoru, aort darlığı tanısında, tedavisinde, takibinde ve koroner arter hastalığı riskinin belirlenmesinde oldukça önemlidir. Güncel kılavuzlar, düşük akım ve düşük gradyanlı aort darlığı tanısında aort kapak kalsiyum skorlarının dikkate alınmasını önermektedir. Aort kapak kalsiyumunun ölçümünde altın standart yöntem bilgisayarlı tomografidir (BT). Agatston skoru, kalsiyum alanı ile BT dansitesinin çarpılmasıyla hesaplanan yarı otomatik bir yöntem olmakla birlikte BT pahalı ve radyasyon riski taşımaktadır. Alternatif olarak, ekokardiyografi, daha ucuz ve radyasyon içermeyen bir yöntem olup bu görüntüleme üzerinde yapılan çalışmalar gözleme dayalı ve yarı kantitatif olup, objektif sonuçlar vermekte zorlanmaktadır. Bu çalışmada, aort kapak kalsifikasyon ölçümü için gerekli olan kalsifikasyon bölgelerini belirlemek üzere derin öğrenme tabanlı U-Net modeli çeşitli veri artırma teknikleri ile değerlendirilmiştir. Bu amaçla yeni bir veri seti oluşturulmuş ve renk, rijid ve rijid olmayan geometrik dönüşümler gibi farklı artırma yöntemlerinin etkinliği analiz edilmiştir. Elde edilen sonuçlar değerlendirildiğinde, rijid olmayan geometrik dönüşümlerin segmentasyon performansını en anlamlı şekilde artırdığı gözlemlenmiştir.

Keywords

Aort kapak, Kalsifikasyon, Kalsiyum skorlama, Ekokardiyografi, Veri artırma, Derin öğrenme, U-Net

Ethical Statement

KTÜ Farabi Hastanesi'nden (2022/190 nolu) etik kurul onayı alınmıştır.

Supporting Institution

TÜBİTAK

Project Number

222S110

Analysis of Data Augmentation Methods for Aortic Valve Calcification Segmentation in Echocardiographic Images

Abstract

Aortic valve calcium scoring is crucial for diagnosing, treating, monitoring aortic stenosis, and assessing coronary artery disease risk. Current guidelines recommend incorporating aortic valve calcium scores in the diagnosis of low-flow and low-gradient aortic stenosis. The gold standard for measuring aortic valve calcium is computed tomography (CT). The Agatston score is a semi-automatic method for calculating calcium scores by multiplying the calcium area by CT density. However, CT is expensive and carries radiation risks. As an alternative, echocardiography, which is cheaper and radiation-free, has been explored. However, studies on echocardiography are observational and semi-quantitative, and they struggle to provide objective results. In this study, the deep learning-based U-Net model was evaluated for identifying calcification regions necessary for aortic valve calcification measurement using various data augmentation techniques. A new dataset was created for this purpose, and the effectiveness of different augmentation methods, including color adjustments, rigid transformations, and non-rigid geometric transformations, was analyzed. The results indicate that non-rigid geometric transformations significantly enhance segmentation performance.

Keywords

Aortic valve, Calcification, Calcium scoring, Echocardiography, Data augmentation, Deep learning, U-Net

Project Number

222S110

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