Abstract #0692

Multi-modal Isointense Infant Brain Image Segmentation with Deep Learning based Methods

Dong Nie^1,2, Li Wang¹, Roger Trullo¹, Ehsan Adeli¹, Weili Lin¹, and Dinggang Shen¹

¹Department of Radiology and BRIC, UNC-Chapel Hill, USA, Chapel Hill, NC, United States, ²Department of Computer Science, UNC-Chapel Hill, USA, Chapel Hill, NC, United States

Accurate segmentation of infant brain images into different regions of interest is one of the most important fundamental steps in studying early brain development. In this paper, we propose a framework based on the recently well-received and prominent deep learning methods. Specifically, we propose a novel 3D multimodal fully convolutional network (FCN) architecture for segmentation of isointense phase brain MR images. Our proposed framework can model the brain tissue structures more accurately compared to the traditional methods. The conducted experiments show that our proposed 3D multimodal FCN model outperforms all previous methods by a large margin, in terms of segmentation accuracy.

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