de Jong, J. | bioRxiv Science

bioRxiv2021

Quality control strategies for brain MRI segmentation and parcellation: practical approaches and recommendations - insights from The Maastricht Study

BackgroundQuality control of brain segmentation is a fundamental step to ensure data quality. Manual quality control is the current gold standard, despite unfeasible in large neuroimaging samples. Several options for automated quality control have been proposed, providing potential time efficient and reproducible alternatives. However, those have never been compared side to side, which prevents to reach consensus in the appropriate QC strategy to use. This study aims to elucidate the changes manual editing of brain segmentations produce in morphological estimates, and to analyze and compare the effects of different quality control strategies in the reduction of the measurement error. MethodsWe used structural MR images from 259 participants of The Maastricht Study. Morphological estimates were automatically extracted using FreeSurfer 6.0. A subsample of the brain segmentations with inaccuracies was manually edited, and morphological estimates were compared before and after editing. In parallel, 11 quality control strategies were applied to the full sample. Those included: a manual strategy, manual-QC, in which images were visually inspected and manually edited; five automated strategies where outliers were excluded based on the tools MRIQC and Qoala-T, and the metrics morphological global measures, Euler numbers and Contrast-to-Noise ratio; and five semi-automated strategies, were the outliers detected through the mentioned tools and metrics were not excluded, but visually inspected and manually edited. We used a regression of morphological brain measures against age as a test case to compare the changes in relative unexplained variance that each quality control strategy produces, using the reduction of relative unexplained variance as a measure of increase in quality. ResultsManually editing brain surfaces produced changes particularly high in subcortical brain volumes and moderate in cortical surface area, thickness and hippocampal volumes. The exclusion of outliers based on Euler numbers yielded a larger reduction of relative unexplained variance for measurements of cortical area, subcortical volumes and hippocampal subfields, while manual editing of brain segmentations performed best for cortical thickness. MRIQC produced a lower, but consistent for all types of measures, reduction in relative unexplained variance. Unexpectedly, the exclusion of outliers based on global morphological measures produced an increase of relative unexplained variance, potentially removing more morphological information than noise from the sample. ConclusionOverall, the automatic exclusion of outliers based on Euler numbers or MRIQC are reliable and time efficient quality control strategies that can be applied in large neuroimaging cohorts.

neuroscience↗

bioRxiv2020

Advanced in silico validation framework forthree-dimensional Traction Force Microscopy andapplication to an in vitro model of sproutingangiogenesis

In the last decade, cellular forces in three-dimensional hydrogels that mimic the extracellular matrix have been calculated by means of Traction Force Microscopy (TFM). However, characterizing the accuracy limits of a traction recovery method is critical to avoid obscuring physiological information due to traction recovery errors. So far, 3D TFM algorithms have only been validated using simplified cell geometries, bypassing image processing steps or arbitrarily simulating focal adhesions. Moreover, it is still uncertain which of the two common traction recovery methods, i.e., forward and inverse, is more robust against the inherent challenges of 3D TFM. In this work, we established an advanced in silico validation framework that is applicable to any 3D TFM experimental setup and that can be used to correctly couple the experimental and computational aspects of 3D TFM. Advancements relate to the simultaneous incorporation of complex cell geometries, simulation of microscopy images of varying bead densities and different focal adhesion sizes and distributions. By measuring the traction recovery error with respect to ground truth solutions, we found that while highest traction recovery errors occur for cases with sparse and small focal adhesions, our implementation of the inverse method improves two-fold the accuracy with respect to the forward method (average error of 23% vs. 50%). This advantage was further supported by recovering cellular tractions around angiogenic sprouts in an in vitro model of angiogenesis. The inverse method recovered more realistic traction patterns than the forward method, showing higher traction peaks and a clearer pulling pattern at the sprout protrusion tips.

bioengineering↗