Comprehensive Brain MRI Segmentation in High Risk Preterm Newborns

PLoS One

Sushmita Datta¹  Nehal A. Parikh²  Yanjie Zhang³  Ponnada A. Narayana³  Robert E. Lasky³  Xintian Yu³ 

[1] Center for Clinical Research and Evidence Based Medicine, University of Texas Health Science Center at Houston Medical School, Houston, Texas, United States of America;Department of Diagnostic and Interventional Imaging, University of Texas Health Science Center at Houston Medical School, Houston, Texas, United States of America;Division of Neonatal-Perinatal Medicine, Department of Pediatrics, University of Texas Health Science Center at Houston Medical School, Houston, Texas, United States of America

关键词: Magnetic resonance imaging;    Cerebrospinal fluid;    Central nervous system;    Neonates;    Cardiac ventricles;    Caudate nucleus;    Cerebellum;    Amygdala;   

DOI  :  10.1371/journal.pone.0013874

学科分类：医学（综合）

来源: Public Library of Science

【 摘 要 】

Most extremely preterm newborns exhibit cerebral atrophy/growth disturbances and white matter signal abnormalities on MRI at term-equivalent age. MRI brain volumes could serve as biomarkers for evaluating the effects of neonatal intensive care and predicting neurodevelopmental outcomes. This requires detailed, accurate, and reliable brain MRI segmentation methods. We describe our efforts to develop such methods in high risk newborns using a combination of manual and automated segmentation tools. After intensive efforts to accurately define structural boundaries, two trained raters independently performed manual segmentation of nine subcortical structures using axial T2-weighted MRI scans from 20 randomly selected extremely preterm infants. All scans were re-segmented by both raters to assess reliability. High intra-rater reliability was achieved, as assessed by repeatability and intra-class correlation coefficients (ICC range: 0.97 to 0.99) for all manually segmented regions. Inter-rater reliability was slightly lower (ICC range: 0.93 to 0.99). A semi-automated segmentation approach was developed that combined the parametric strengths of the Hidden Markov Random Field Expectation Maximization algorithm with non-parametric Parzen window classifier resulting in accurate white matter, gray matter, and CSF segmentation. Final manual correction of misclassification errors improved accuracy (similarity index range: 0.87 to 0.89) and facilitated objective quantification of white matter signal abnormalities. The semi-automated and manual methods were seamlessly integrated to generate full brain segmentation within two hours. This comprehensive approach can facilitate the evaluation of large cohorts to rigorously evaluate the utility of regional brain volumes as biomarkers of neonatal care and surrogate endpoints for neurodevelopmental outcomes.

【 授权许可】

CC BY