From neonatal to adult brain MR image segmentation in a few seconds using 3D-like fully convolutional network and transfer learning

Abstract : Brain magnetic resonance imaging (MRI) is widely used to assess brain development in neonates and to diagnose a wide range of neurological diseases in adults. Such studies are usually based on quantitative analysis of different brain tissues, so it is essential to be able to classify them accurately. In this paper, we propose a fast automatic method that segments 3D brain MR images into different tissues using fully convo-lutional network (FCN) and transfer learning. As compared to existing deep learning-based approaches that rely either on 2D patches or on fully 3D FCN, our method is way much faster: it only takes a few seconds, and only a single modality (T1 or T2) is required. In order to take the 3D information into account, all 3 successive 2D slices are stacked to form a set of 2D " color " images, which serve as input for the FCN pre-trained on ImageNet for natural image classification. To the best of our knowledge, this is the first method that applies transfer learning to segment both neonatal and adult brain 3D MR images. Our experiments on two public datasets show that our method achieves state-of-the-art results. Index Terms— Brain MRI, Neonatal/Adult brain seg-mentation, Deep learning, Fully convolutional network.
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Yongchao Xu, Thierry Géraud, Isabelle Bloch. From neonatal to adult brain MR image segmentation in a few seconds using 3D-like fully convolutional network and transfer learning. 2017 IEEE International Conference on Image Processing (ICIP), Sep 2017, Beijing, France. ⟨10.1109/ICIP.2017.8297117⟩. ⟨hal-01735727⟩

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