Infant Brain Image Segmentation using the Convolutional Neural Networks
Subject Areas : Communication Engineering
Iran
Sarafraz
1
(Islamic Azad University - Shiraz Branch)
Hamed
Agahi2
2
(Islamic Azad University, Shiraz Branch)
Azar
Mahmoodzadeh
3
(Department of Electrical Engineering, Shiraz Branch, Islamic Azad University, Shiraz, Iran)
Keywords: Segmentation, magnetic resonance images, Infants’ brain, Convolutional neural networks, Deep learning,
Abstract :
In this paper, a method based on convolutional neural networks for segmenting neonatal brain images is presented. One of the major challenges in neonatal brain image segmentation is the intensity distribution overlapping between gray matter and white matter tissues, which reduces the segmentation accuracy of these areas. To increase the intensity differentiation between brain tissues, this paper presents a pre-processing method based on convolutional neural networks that effectively increases the segmentation accuracy. To obtain the final segmentation result, another convolutional neural network is proposed which performs segmentation based on T1-T2 images. To evaluate the performance of the proposed method, two databases are used, which include magnetic resonance imaging of infants' brains. The results show the appropriate efficiency of the proposed method in segmenting brain tissues.
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