Yıl: 2023 Cilt: 12 Sayı: 3 Sayfa Aralığı: 144 - 151 Metin Dili: İngilizce DOI: 10.46810/tdfd.1339665 İndeks Tarihi: 03-10-2023

Brain Extraction from Magnetic Resonance Images Using UNet modified with Residual and Dense Layers

Öz:
The main goal of brain extraction is to separate the brain from non-brain parts, which enables accurate detection or classification of abnormalities within the brain region. The precise brain extraction process significantly influences the quality of successive neuroimaging analyses. Brain extraction is a challenging task mainly due to the similarity of intensity values between brain and non-brain structure. In this study, a UNet model improved with ResNet50 or DenseNet121 feature extraction layers was proposed for brain extraction from Magnetic Resonance Imaging (MRI) images. Three publicly available datasets (IBSR, NFBS and CC-359) were used for training the deep learning models. The findings of a comparison between different feature extraction layer types added to UNet shows that residual connections taken from ResNet50 is more successful across all datasets. The ResNet50 connections proved effective in enhancing the distinction of weak but significant gradient values in brain boundary regions. In addition, the best results were obtained for CC-359. The improvement achieved with CC-359 can be attributed to its larger number of samples with more slices, indicating that the model learned better. The performance of our proposed model, evaluated using test data, is found to be comparable to the results obtained in the literature.
Anahtar Kelime: Brain extraction Skull stripping Deep learning Dense connection Residual connection UNet.

Artık ve Yoğun Katmanlarla Değiştirilmiş UNet Kullanılarak Manyetik Rezonans Görüntülerinden Beyin Çıkarımı

Öz:
Beyin çıkarımının temel amacı, beyni beyin dışı kısımlardan ayırarak beyin bölgesi içindeki anormalliklerin doğru tespitini veya sınıflandırılmasını mümkün kılmaktır. Hassas beyin çıkarma işlemi, ardışık nörogörüntüleme analizlerinin kalitesini önemli ölçüde etkiler. Beyin çıkarımı, beyin ve beyin dışı yapılar arasındaki yoğunluk değerlerinin benzerliği nedeniyle zorlu bir görevdir. Bu çalışmada, Manyetik Rezonans Görüntüleme (MRG) görüntülerinden beyin çıkarımı için ResNet50 veya DenseNet121 özellik çıkarma katmanları ile geliştirilmiş bir UNet modeli önerilmiştir. Derin öğrenme modellerini eğitmek için IBSR, NFBS ve CC-359 adlı üç halka açık veri kümesi kullanılmıştır. UNet’e eklenen öznitelik çıkarma katman türleri arasındaki karşılaştırma sonuçları, ResNet50’den alınan artık bağlantıların tüm veri kümelerinde daha başarılı olduğunu göstermektedir. ResNet50 bağlantılarının, beyin sınır bölgelerindeki zayıf ancak önemli gradyan değerlerinin ayrımını artırmada etkili olduğu anlaşılmaktadır. Ayrıca, en iyi sonuçlar CC-359 için elde edilmiştir. CC-359 ile elde edilen gelişme, verisetinin daha fazla kesit ve örnek içermesinden dolayı modelin daha iyi öğrenmesinden kaynaklanmıştır. Önerilen modelin performansı, test verileri kullanılarak değerlendirildiğinde, literatürde elde edilen sonuçlarla karşılaştırılabilir bulunmuştur.
Anahtar Kelime:

Belge Türü: Makale Makale Türü: Araştırma Makalesi Erişim Türü: Erişime Açık
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APA GÜRKAHRAMAN K, DAŞGIN Ç (2023). Brain Extraction from Magnetic Resonance Images Using UNet modified with Residual and Dense Layers. , 144 - 151. 10.46810/tdfd.1339665
Chicago GÜRKAHRAMAN KALI,DAŞGIN Çağrı Brain Extraction from Magnetic Resonance Images Using UNet modified with Residual and Dense Layers. (2023): 144 - 151. 10.46810/tdfd.1339665
MLA GÜRKAHRAMAN KALI,DAŞGIN Çağrı Brain Extraction from Magnetic Resonance Images Using UNet modified with Residual and Dense Layers. , 2023, ss.144 - 151. 10.46810/tdfd.1339665
AMA GÜRKAHRAMAN K,DAŞGIN Ç Brain Extraction from Magnetic Resonance Images Using UNet modified with Residual and Dense Layers. . 2023; 144 - 151. 10.46810/tdfd.1339665
Vancouver GÜRKAHRAMAN K,DAŞGIN Ç Brain Extraction from Magnetic Resonance Images Using UNet modified with Residual and Dense Layers. . 2023; 144 - 151. 10.46810/tdfd.1339665
IEEE GÜRKAHRAMAN K,DAŞGIN Ç "Brain Extraction from Magnetic Resonance Images Using UNet modified with Residual and Dense Layers." , ss.144 - 151, 2023. 10.46810/tdfd.1339665
ISNAD GÜRKAHRAMAN, KALI - DAŞGIN, Çağrı. "Brain Extraction from Magnetic Resonance Images Using UNet modified with Residual and Dense Layers". (2023), 144-151. https://doi.org/10.46810/tdfd.1339665
APA GÜRKAHRAMAN K, DAŞGIN Ç (2023). Brain Extraction from Magnetic Resonance Images Using UNet modified with Residual and Dense Layers. Türk Doğa ve Fen Dergisi, 12(3), 144 - 151. 10.46810/tdfd.1339665
Chicago GÜRKAHRAMAN KALI,DAŞGIN Çağrı Brain Extraction from Magnetic Resonance Images Using UNet modified with Residual and Dense Layers. Türk Doğa ve Fen Dergisi 12, no.3 (2023): 144 - 151. 10.46810/tdfd.1339665
MLA GÜRKAHRAMAN KALI,DAŞGIN Çağrı Brain Extraction from Magnetic Resonance Images Using UNet modified with Residual and Dense Layers. Türk Doğa ve Fen Dergisi, vol.12, no.3, 2023, ss.144 - 151. 10.46810/tdfd.1339665
AMA GÜRKAHRAMAN K,DAŞGIN Ç Brain Extraction from Magnetic Resonance Images Using UNet modified with Residual and Dense Layers. Türk Doğa ve Fen Dergisi. 2023; 12(3): 144 - 151. 10.46810/tdfd.1339665
Vancouver GÜRKAHRAMAN K,DAŞGIN Ç Brain Extraction from Magnetic Resonance Images Using UNet modified with Residual and Dense Layers. Türk Doğa ve Fen Dergisi. 2023; 12(3): 144 - 151. 10.46810/tdfd.1339665
IEEE GÜRKAHRAMAN K,DAŞGIN Ç "Brain Extraction from Magnetic Resonance Images Using UNet modified with Residual and Dense Layers." Türk Doğa ve Fen Dergisi, 12, ss.144 - 151, 2023. 10.46810/tdfd.1339665
ISNAD GÜRKAHRAMAN, KALI - DAŞGIN, Çağrı. "Brain Extraction from Magnetic Resonance Images Using UNet modified with Residual and Dense Layers". Türk Doğa ve Fen Dergisi 12/3 (2023), 144-151. https://doi.org/10.46810/tdfd.1339665