Anatomy-guided joint tissue segmentation and topological correction for 6-month infant brain MRI with risk of autism

Li Wang, Gang Li, Ehsan Adeli, Mingxia Liu, Zhengwang Wu, Yu Meng, Weili Lin, Dinggang Shen

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)

Abstract

Tissue segmentation of infant brain MRIs with risk of autism is critically important for characterizing early brain development and identifying biomarkers. However, it is challenging due to low tissue contrast caused by inherent ongoing myelination and maturation. In particular, at around 6 months of age, the voxel intensities in both gray matter and white matter are within similar ranges, thus leading to the lowest image contrast in the first postnatal year. Previous studies typically employed intensity images and tentatively estimated tissue probabilities to train a sequence of classifiers for tissue segmentation. However, the important prior knowledge of brain anatomy is largely ignored during the segmentation. Consequently, the segmentation accuracy is still limited and topological errors frequently exist, which will significantly degrade the performance of subsequent analyses. Although topological errors could be partially handled by retrospective topological correction methods, their results may still be anatomically incorrect. To address these challenges, in this article, we propose an anatomy-guided joint tissue segmentation and topological correction framework for isointense infant MRI. Particularly, we adopt a signed distance map with respect to the outer cortical surface as anatomical prior knowledge, and incorporate such prior information into the proposed framework to guide segmentation in ambiguous regions. Experimental results on the subjects acquired from National Database for Autism Research demonstrate the effectiveness to topological errors and also some levels of robustness to motion. Comparisons with the state-of-the-art methods further demonstrate the advantages of the proposed method in terms of both segmentation accuracy and topological correctness.

Original languageEnglish
Pages (from-to)2609-2623
Number of pages15
JournalHuman Brain Mapping
Volume39
Issue number6
DOIs
Publication statusPublished - 2018 Jun

Bibliographical note

Funding Information:
Data used in the preparation of this manuscript were obtained from the NIH-supported National Database for Autism Research (NDAR). NDAR is a collaborative informatics system created by the National Institutes of Health to provide a national resource to support and accelerate research in autism. This paper reflects the views of the authors and may not reflect the opinions or views of the NIH or of the Submitters submitting original data to NDAR. This work was supported in part by National Institutes of Health grants MH109773, MH100217, MH070890, EB006733, EB008374, EB009634, AG041721, AG042599, MH088520, MH108914, and MH107815.

Publisher Copyright:
© 2018 Wiley Periodicals, Inc.

Keywords

  • anatomical guidance
  • autism
  • isointense phase
  • level set
  • segmentation

ASJC Scopus subject areas

  • Anatomy
  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Neurology
  • Clinical Neurology

Fingerprint

Dive into the research topics of 'Anatomy-guided joint tissue segmentation and topological correction for 6-month infant brain MRI with risk of autism'. Together they form a unique fingerprint.

Cite this