iSeg-WNet: Bebek Beyin MRI Görüntülerinin Volumetrik Bölütlemesi

Year 2022, Volume: 38 Issue: 3, 508 - 518, 30.12.2022

Gaffari Çelik

Abstract

Bebek beyin gelişiminin incelenmesi, doğabilecek beyin fonksiyon bozuklarının erken teşhisi açısından son derece önemlidir. Beyin MRI’ larının, beyaz madde (WM), gri madde (GM) ve beyin omurilik sıvısı (CSF) dokularının bölütleme işlemi ile incelenmektedir. Bebek beyinlerinde dokular arasındaki düşük yoğunluklu kontrast bölütleme işlemini zorlaştırmaktadır. Son dönemlerde geliştirilen Derin Öğrenme mimarileri ile bölütleme işleminin son derece çok iyi yapıldığı görülmektedir. Bu çalışmada bebek beyin MRI görüntülerinin bölütlenmesi için Derin Öğrenme tabanlı iSeg-WNet adıyla bir mimari önerilmiştir. Farklı çalışmalar ile uygun hiperparametreler belirlenmiş ve farklı mimarilerin performansları karşılaştırılmıştır. Performans karşılaştırılması Dice metriğine göre yapılmıştır. Yapılan deneysel çalışmalarda, T1w ve T2w çekimlerdeki MRI görüntülerinin beraber kullanılması bölütleme performansının artırdığı gözlemlenmiştir. Aynı zamanda maliyet fonksiyonu olarak Dice Loss ve veri normalizasyon işlemi olarak da MinMax normalizasyonun kullanılması ile yüksek başarım elde edilmiştir. Farklı mimarilerin bölütleme performansları incelendiğinde, önerilen mimari ile CSF, GM ve WM dokularını en yüksek başarı ile bölütlediği görülmüştür. Önerilen mimariye https://github.com/GaffariCelik/iSeg-WNet adresinden erişilebilir.

Keywords

Derin Öğrenme, CNN, Bölütleme, Dice Loss, 3D MRI Bölütleme, iseg-2019, iseg-2017

iSeg-WNet: Volumetric Segmentation of Infant Brain MRI Images

Abstract

Examination of infant brain development is extremely important in terms of early diagnosis of possible brain dysfunctions. Brain MRIs are examined by segmentation of white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF) tissues. Low-density contrast between tissues in infant brains complicates the segmentation process. It is seen that the segmentation process is done very well with the Deep Learning architectures that have been developed recently. In this study, an architecture called Deep Learning-based iSeg-WNet is proposed for segmentation of infant brain MRI images. Appropriate hyperparameters were determined by different studies and the performances of different architectures were compared. Performance comparison was made according to Dice metric. In experimental studies, it has been observed that the use of MRI images in T1w and T2w images together increases the segmentation performance. At the same time, high performance was obtained by using Dice Loss as a cost function and MinMax normalization as a data normalization process. When the segmentation performances of different architectures are examined, it is seen that the proposed architecture segments CSF, GM and WM textures with the highest success. The proposed architecture is available at https://github.com/GaffariCelik/iSeg-WNet.

Keywords

Deep Learning, CNN, Segmentation, Dice Loss, 3D MRI Segmentation, iseg-2019, iseg-2017

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