Brain Age Estimation from MRI Images using 2D-CNN instead of 3D-CNN

Abstract

Human Brain Age has become a popular aging biomarker and is used to detect differences among healthy individuals. Because of the specific changes in the human brain with aging, it is possible to estimate patients’ brain ages from their brain images. Due to developments of the ability of CNN in classification and regression from images, in this study, one of the most popular state of the art models, the DenseNet model, is utilized to estimate human brain ages using transfer learning. Since this process requires high memory load with 3D-CNN, 2D-CNN is preferred for the task of Brain Age Estimation (BAE). In this study, some experiments are carried out to reduce the number of computations while preserving the total performance. With this aim, center slices of each three brain planes are used as the inputs of the DenseNet model, and different optimizers such as Adam, Adamax and Adagrad are used for each model. The dataset is selected from the IXI (Information Extraction from Images) MRI data repository. The MAE evaluation metric is used for each model with different input set to evaluate performance. The best achieved Mean Absolute Error (MAE) is 6.3 with the input set which consisted of center slices of the sagittal plane of brain scan and the Adamax parameter.

Keywords

• Brain Age Estimation
• Brain MRI
• 2D-CNN

MR Görüntülerinden 3D-ESA Yerine 2D-ESA Kullanılarak Beyin Yaşı Tahmini

Abstract

İnsan Beyin Yaşı, son zamanlarda popüler bir yaşlanma biyobelirteci haline geldi ve sağlıklı kişiler arasındaki farklılıkları tespit etmek için kullanıldı. Yaşlanmayla birlikte insan beynindeki spesifik değişiklikler nedeniyle, hastaların beyin yaşlarını beyin görüntülerinden tahmin etmek mümkündür. Evrişimsel Sinir Ağlarının (ESA) gelişen görüntü sınıflama ve regresyon yeteneğinden yola çıkılarak, bu çalışmada en popüler ESA modellerinden biri olan DenseNet modeli öğrenme aktarımı yöntemiyle kullanılarak insan beyni yaşı tahmini g erçekleştirilmiştir. 3D-ESA y üksek bellek yükü gerektirdiğinden Beyin Yaşı Tahmin (BAE) görevi için 2D-CNN tercih edilmiştir. Bu deneyde, toplam performans korunurken hesaplama yükünü azaltmak için bazı deneyler yapılmıştır. Bu amaçla, her üç beyin düzleminin merkez dilimleri DenseNet modelinin girdileri olarak kullanılmıştır ve her model için Adam, Adamax ve Adagrad gibi f arklı optimizerlar k ullanılmıştır. Veri k ümesi, I XI M RI veri havuzundan seçilmiştir. Performansı değerlendirmek için ortalama mutlak hata (MAE) metriği her model için kullanılmıştır. Bu çalışmada en düşük Ortalama Mutlak Hata (MAE), beynin sagital düzleminin merkez dilimlerini içeren giriş kümesiyle ve Adamax parametresiyle 6.3 olarak elde edilmiştir.

Keywords

• Beyin Yaşı Tahmini
• Beyin MR
• 2D-ESA

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