A review on automatic fetal and neonatal brain MRI segmentation

Abstract

In recent years, a variety of segmentation methods have been proposed for automatic delineation of the fetal and neonatal brain MRI. These methods aim to define regions of interest of different granularity: brain, tissue types or more localised structures. Different methodologies have been applied for this segmentation task and can be classified into unsupervised, parametric, classification, atlas fusion and deformable models. Brain atlases are commonly utilised as training data in the segmentation process. Challenges relating to the image acquisition, the rapid brain development as well as the limited availability of imaging data however hinder this segmentation task. In this paper, we review methods adopted for the perinatal brain and categorise them according to the target population, structures segmented and methodology. We outline different methods proposed in the literature and discuss their major contributions. Different approaches for the evaluation of the segmentation accuracy and benchmarks used for the segmentation quality are presented. We conclude this review with a discussion on shortcomings in the perinatal domain and possible future directions.

The relationship between biological and psychosocial risk factors and resting‐state functional connectivity in 2‐month‐old Bangladeshi infants: A feasibility and pilot study

“…Deformable Registration via Attribute Matching and Mutual‐Saliency Weighting (DRAMMS) (Ou, Sotiras, Paragios, & Davatzikos, ) was used as part of registering atlas and subject data. It should also be noted, however, that there is still no consensus about the segmentation pipeline for infant imaging (Makropoulos, Counsell, & Rueckert, ; Zhang, Shen, & Lin, ). These segmentations were subsequently consolidated into gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) regions‐of‐interest (ROIs) for each participant using in‐house MATLAB 2015b ( MathWorks ) code.…”

Relating anthropometric indicators to brain structure in 2-month-old Bangladeshi infants growing up in poverty: a pilot study

“…3A) were submitted to the FreeSurfer recon-all pipeline for infant MRI images (Zöllei et al, 2017) and subject data. It should be noted that there is still no consensus regarding the best segmentation pipeline for infant imaging (Makropoulos et al, 2018;Zhang et al, 2019).…”

“…After careful review, 24 of 53 participants were excluded due to poor image, leaving 29 infants with segmentation quality scores ≥7/10. Indeed, segmenting infant brains with high accuracy and precision is extremely challenging with currently available methods, given lower contrast-to-noise, higher rates of head motion, increased WM-CSF partial volume effects, and other obstacles compared with segmentations for other age groups (Makropoulos et al, 2018).…”

The developing human connectome project: A minimal processing pipeline for neonatal cortical surface reconstruction

“…A variety of techniques have been proposed for tissue segmentation of the neonatal brain in recent years: unsupervised techniques (Gui et al, 2012), atlas fusion techniques (Weisenfeld and Warfield, 2009;Gousias et al, 2013;Kim et al, 2016), parametric techniques (Prastawa et al, 2005;Song et al, 2007;Xue et al, 2007;Shi et al, 2010;Cardoso et al, 2013;Makropoulos et al, 2012;Wang et al, 2012;Wu and Avants, 2012;Beare et al, 2016;Liu et al, 2016), classification techniques (Anbeek et al, 2008;Srhoj-Egekher et al, 2012;Chiţȃ et al, 2013;Wang et al, 2015;Sanroma et al, 2016;Moeskops et al, 2016) and deformable models (Wang et al, 2011;Dai et al, 2013;Wang et al, 2013Wang et al, , 2014. A review of neonatal segmentation methods can be found in Devi et al (2015); Makropoulos et al (2017). The majority of these techniques have been applied to images with a lower resolution than those acquired within the dHCP, and typically to images of preterm-born subjects.…”