Magnetic resonance imaging of the newborn brain: automatic segmentation of brain images into 50 anatomical regions.

Ioannis S Gousias,Mary A Rutherford,Alexander Hammers,Serena J Counsell,Latha Srinivasan,Jo V Hajnal,Rolf A Heckemann,A David Edwards,Daniel Rueckert,Antoni Rodriguez-Fornells

doi:10.1371/journal.pone.0059990

Abstract

We studied methods for the automatic segmentation of neonatal and developing brain images into 50 anatomical regions, utilizing a new set of manually segmented magnetic resonance (MR) images from 5 term-born and 15 preterm infants imaged at term corrected age called ALBERTs. Two methods were compared: individual registrations with label propagation and fusion; and template based registration with propagation of a maximum probability neonatal ALBERT (MPNA). In both cases we evaluated the performance of different neonatal atlases and MPNA, and the approaches were compared with the manual segmentations by means of the Dice overlap coefficient. Dice values, averaged across regions, were 0.81±0.02 using label propagation and fusion for the preterm population, and 0.81±0.02 using the single registration of a MPNA for the term population. Segmentations of 36 further unsegmented target images of developing brains yielded visibly high-quality results. This registration approach allows the rapid construction of automatically labeled age-specific brain atlases for neonates and the developing brain.

Highlights

Anatomical structures can be segmented in biological images by transfer of voxel labels from an analogous image previously segmented into anatomical regions, or atlas [1]
Segmentation methods often first register the atlas to the target image and segment the target image into anatomical structures based on transferred information [5,7,8,9,10,11,12], registering multiple atlases to the same target with subsequent fusion of different segmentations will frequently improve the final segmentation result, compensating for nonsystematic errors in single registrations [13,14,15,16,17]
In this paper we present two methods for the automatic segmentation of neonatal and developing brain Magnetic Resonance (MR) images into 50 regions of interest (ROI) utilizing a new set of manually defined neonatal atlases called a label-based encephalic ROI template (ALBERT) [21]

Summary

Introduction

Anatomical structures can be segmented in biological images by transfer of voxel labels from an analogous image previously segmented into anatomical regions, or atlas [1]. Segmentation methods often first register the atlas to the target image and segment the target image into anatomical structures based on transferred information [5,7,8,9,10,11,12], registering multiple atlases to the same target with subsequent fusion of different segmentations will frequently improve the final segmentation result, compensating for nonsystematic errors in single registrations [13,14,15,16,17]. The second approach is based on propagation of labels from a maximum probability neonatal ALBERT (MPNA) For both methods we evaluated the performance of different atlases and MPNAs and compared the results to the gold-standard manual segmentations

Methods

Results

Conclusion