A Deep Spatial Context Guided Framework for Infant Brain Subcortical Segmentation

the UNC/UMN Baby Connectome Program Consortium

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)


Accurate subcortical segmentation of infant brain magnetic resonance (MR) images is crucial for studying early subcortical structural growth patterns and related diseases diagnosis. However, dynamic intensity changes, low tissue contrast, and small subcortical size of infant brain MR images make subcortical segmentation a challenging task. In this paper, we propose a spatial context guided, coarse-to-fine deep convolutional neural network (CNN) based framework for accurate infant subcortical segmentation. At the coarse stage, we propose a signed distance map (SDM) learning UNet (SDM-UNet) to predict SDMs from the original multi-modal images, including T1w, T2w, and T1w/T2w images. By doing this, the spatial context information, including the relative position information across different structures and the shape information of the segmented structures contained in the ground-truth SDMs, is used for supervising the SDM-UNet to remedy the bad influence from the low tissue contrast in infant brain MR images and generate high-quality SDMs. To improve the robustness to outliers, a Correntropy based loss is introduced in SDM-UNet to penalize the difference between the ground-truth SDMs and predicted SDMs in training. At the fine stage, the predicted SDMs, which contains spatial context information of subcortical structures, are combined with the multi-modal images, and then fed into a multi-source and multi-path UNet (M2-UNet) for delivering refined segmentation. We validate our method on an infant brain MR image dataset with 24 scans by evaluating the Dice ratio between our segmentation and the manual delineation. Compared to four state-of-the-art methods, our method consistently achieves better performances in both qualitative and quantitative evaluations.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings
EditorsAnne L. Martel, Purang Abolmaesumi, Danail Stoyanov, Diana Mateus, Maria A. Zuluaga, S. Kevin Zhou, Daniel Racoceanu, Leo Joskowicz
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages11
ISBN (Print)9783030597276
Publication statusPublished - 2020
Event23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020 - Lima, Peru
Duration: 2020 Oct 42020 Oct 8

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12267 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020


  • Coarse-to-fine framework
  • Infant brain
  • Spatial context information
  • Subcortical segmentation

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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