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Journals / Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi / 2026 / Cilt: 12 Sayı: 3

Segmentation of fetal brain tissues using 3D U-Net and the effect of gestational age on segmentation performance

Fetüs beyin dokularının 3B U-Net ile segmentasyonu ve gestasyonel yaşın segmentasyon performansına etkisi

Uğurcan Akyüz(ERCİYES ÜNİVERSİTESİ)0000-0002-0917-3552
Tayyip Özcan(ERCİYES ÜNİVERSİTESİ)0000-0002-3111-5260
Pages
637–643
Publication Date
2023-07-15
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Average Dice scores of 0.83, 0.91, and 0.92 were achieved for the gestational-age groups, respectively.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Environmental and genetic factors are thought to cause diseases in the fetal brain that may later manifest as autism, hyperactivity, and bipolar disorder.
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Key Findings & Data:In this study, seven different fetal brain tissues were segmented fully-automated with 3D U-Net.
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Conclusion & Impact:Average Dice scores of 0.83, 0.91, and 0.92 were achieved for the gestational-age groups, respectively.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:fetüs beyin dokusu · MR görüntüleme · segmentasyon

Abstract

Environmental and genetic factors are thought to cause diseases in the fetal brain that may later manifest as autism, hyperactivity, and bipolar disorder. Structural analysis of fetal brain tissue helps to explore the etiology of these diseases by providing information about the size and shape of the tissues. MR (magnetic resonance) imaging and ultrasound are the two imaging modalities most used to analyze fetal brain tissue. Especially in MR imaging, brain tissues can be seen clearly. However, structural analysis of MR images is a time-consuming process. Because fetal brain tissues develop and change rapidly, it is difficult to perform manual or semi-automated MR analysis weekly or monthly. In this study, seven different fetal brain tissues were segmented fully-automated with 3D U-Net. The FeTA2021 dataset was divided into the early, middle, and late gestational age groups, different 3D U-Net models were trained for each age group, and segmentation performance was analyzed. Average Dice scores of 0.83, 0.91, and 0.92 were achieved for the gestational-age groups, respectively.

Özet

Gebelik esnasında fetüs beyninde, çevresel veya genetik etmenlerden kaynaklanan bozuklukların ilerleyen yaşlarda otizm, hiperaktivite ve bipolar bozukluklar olarak ortaya çıktığı düşünülmektedir. Fetüs beyin dokularının yapısal analizi, dokuların büyüklükleri ve şekilleri hakkında bilgi sağlayarak bu hastalıkların etiyolojisinin araştırılmasına yardımcı olmaktadır. MR (manyetik rezonans) ve ultrason fetüs beyin dokusu analizi için en sık kullanılan iki görüntüleme tekniğidir. Özellikle MR görüntülemede, fetüs beyin dokuları net bir şekilde görülebilmesine rağmen bu görüntülerin yapısal analizi zaman alıcı bir iştir. Hızla gelişen ve değişen fetüs beyin dokuları, haftalık veya aylık periyotlarla, elle veya yarı-otomatik şekilde gerçekleştirilen MR analizinin yapılmasını zorlaştırmaktadır. Bu araştırmada, 3B U-Net ile MR görüntüleri üzerinde yedi farklı fetüs beyin dokusunun tam otomatik segmentasyonu gerçekleştirilmiştir. FeTA2021 veri seti, erken, orta ve geç gestasyonel yaş gruplarına bölünerek her bir yaş grubu için farklı 3B U-Net modelleri eğitilmiş ve segmentasyon performansı analiz edilmiştir. Gestasyonel yaş gruplarına göre sırasıyla ortalama 0.83, 0.91 ve 0.92 Dice skoruna ulaşılmıştır.

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Cite This Article

Akyüz, U., Özcan, T. (2023). Segmentation of fetal brain tissues using 3D U-Net and the effect of gestational age on segmentation performance. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, Cilt: 12 Sayı: 3, 637–643. https://doi.org/10.28948/ngumuh.1228788

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