Multi-task Learning for Neonatal Brain Segmentation Using 3D Dense-Unet with Dense Attention Guided by Geodesic Distance

  • Toan Duc Bui
  • Li WangEmail author
  • Jian Chen
  • Weili Lin
  • Gang LiEmail author
  • Dinggang ShenEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11795)


The deep convolutional neural network has achieved outstanding performance on neonatal brain MRI tissue segmentation. However, it may fail to produce reasonable results on unseen datasets that have different imaging appearance distributions with the training data. The main reason is that deep learning models tend to have a good fitting to the training dataset, but do not lead to a good generalization on the unseen datasets. To address this problem, we propose a multi-task learning method, which simultaneously learns both tissue segmentation and geodesic distance regression to regularize a shared encoder network. Furthermore, a dense attention gate is explored to force the network to learn rich contextual information. By using three neonatal brain datasets with different imaging protocols from different scanners, our experimental results demonstrate superior performance of our proposed method over the existing deep learning-based methods on the unseen datasets.


Neonatal brain segmentation Multi-task learning Attention Geodesic distance 



This work was supported in part by NIH Grants MH107815, MH109773, MH116225, and MH117943.


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Radiology and Biomedical Research Imaging CenterUniversity of North Carolina at Chapel HillChapel HillUSA
  2. 2.School of Information Science and EngineeringFujian University of TechnologyFuzhouChina
  3. 3.Department of Brain and Cognitive EngineeringKorea UniversitySeoulRepublic of Korea

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