Year: 2022
Type: article
Abstract: The quantification of subcortical volume development from 3D fetal ultrasound can provide important diagnostic information during pregnancy monitoring. However, manual segmentation of subcortical structures in ultrasound volumes is time-consuming and challenging due to low soft tissue contrast, speckle and shadowing artifacts. For this reason, we developed a convolutional neural network (CNN) for the automated segmentation of the choroid plexus (CP), lateral posterior ventricle horns (LPVH), cavum septum pellucidum et vergae (CSPV), and cerebellum (CB) from 3D ultrasound. As ground-truth labels are scarce and expensive to obtain, we applied few-shot learning, in which only a small number of manual annotations (n = 9) are used to train a CNN. We compared training a CNN with only a few individually annotated volumes versus many weakly labelled volumes obtained from atlas-based segmentations. This showed that segmentation performance close to intra-observer variability can be obtained with only a handful of manual annotations. Finally, the trained models were applied to a large number (n = 278) of ultrasound image volumes of a diverse, healthy population, obtaining novel US-specific growth curves of the respective structures during the second trimester of gestation.
Source: NeuroImage
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,450
Awards: G1110878898, G1126217917, Wellcome Centre for Integrative Neuroimaging, Computer Aided Diagnosis for the Analysis of the Fetal Brain and Heart on Ultrasound, G168048790, Seebibyte: Visual Search for the Era of Big Data, Integrative imaging of brain structure and function in populations and individuals, G2911363437, G295669725, G3387446185, Integrative imaging of brain structure and function in populations and individuals, EPSRC and MRC Centre for Doctoral Training in Biomedical Imaging, G4632173870, G4743594581, Visual AI: An Open World Interpretable Visual Transformer, G5219700674, G7288767457, G7306818829, Wellcome Centre for Integrative Neuroimaging, G7938143389, G8355813019, G8883764206, G9000353647
OpenAlex ID: W4220855438