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Brain volume refinement (BVeR): automatic correction tool as an alternative to manual intervention on brain segmentation

Brain volume refinement (BVeR): automatic correction tool as an alternative to manual... PurposeIn neuroimage studies, it is usual to set a sequence of image preprocessing steps to obtain a controlled data analysis. One of the possible data preparation steps is the skull stripping, which removes non-brain tissues from the original image. Although the skull stripping procedure is time-consuming for manual assessments, most automatic approaches often present minor segmentation errors, commonly requiring a manual refinement.MethodsThis study presents an automatic brain volume refinement (BVeR) method. The proposed method is able to refine a prior brain volume segmentation, eliminating local patterns that are inconsistent with the brain borders. This is achieved by an iterative evaluation criteria based on the discontinuity of the pixel signal level and its relationship among the local neighborhood, estimated by use of first and second momentum for a moving window centered at each voxel of the brain surface. The algorithm uses volume stability as a regulatory parameter, i.e., assisting to stop the brain frontier correction when the volume change between iterations reaches a stable level. In order to test the proposed method, we selected two public databases (IXI and RBVM) of structural MRI images of healthy adults and two commonly used brain extraction methods for evaluation, BET and FreeSurfer. We applied quantitative segmentation quality estimation, e.g., DICE, Precision (PREC), Accuracy (ACC), Hausdorff distance (HDIST), and Volume Similarity (VS), and reproducibility estimation, e.g., Intraclass correlation coefficient (ICC) and Pearson correlation, to evaluate whether the BVeR method is robust in real brain extraction scenarios.ResultsThe average brain volume refinement showed a significant improvement in DICE (p < 4.08E-7), VS (p < 6.79E-7), PREC (p < 7.57E-7), ACC (p < 4.16E-5), HDIST (p < 5.48E-8), and ICC (p < 1.20E-7). The BVeR method presents a reliable automatic alternative to the manual correction often requested in neuroimage studies, which can also reduce human subjectivity.ConclusionsThe BVeR algorithm shows to be a promising alternative to manual corrections, with potential impact in studies with large volumes of data. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Research on Biomedical Engineering Springer Journals

Brain volume refinement (BVeR): automatic correction tool as an alternative to manual intervention on brain segmentation

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Publisher
Springer Journals
Copyright
Copyright © Sociedade Brasileira de Engenharia Biomedica 2021
ISSN
2446-4732
eISSN
2446-4740
DOI
10.1007/s42600-021-00168-x
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Abstract

PurposeIn neuroimage studies, it is usual to set a sequence of image preprocessing steps to obtain a controlled data analysis. One of the possible data preparation steps is the skull stripping, which removes non-brain tissues from the original image. Although the skull stripping procedure is time-consuming for manual assessments, most automatic approaches often present minor segmentation errors, commonly requiring a manual refinement.MethodsThis study presents an automatic brain volume refinement (BVeR) method. The proposed method is able to refine a prior brain volume segmentation, eliminating local patterns that are inconsistent with the brain borders. This is achieved by an iterative evaluation criteria based on the discontinuity of the pixel signal level and its relationship among the local neighborhood, estimated by use of first and second momentum for a moving window centered at each voxel of the brain surface. The algorithm uses volume stability as a regulatory parameter, i.e., assisting to stop the brain frontier correction when the volume change between iterations reaches a stable level. In order to test the proposed method, we selected two public databases (IXI and RBVM) of structural MRI images of healthy adults and two commonly used brain extraction methods for evaluation, BET and FreeSurfer. We applied quantitative segmentation quality estimation, e.g., DICE, Precision (PREC), Accuracy (ACC), Hausdorff distance (HDIST), and Volume Similarity (VS), and reproducibility estimation, e.g., Intraclass correlation coefficient (ICC) and Pearson correlation, to evaluate whether the BVeR method is robust in real brain extraction scenarios.ResultsThe average brain volume refinement showed a significant improvement in DICE (p < 4.08E-7), VS (p < 6.79E-7), PREC (p < 7.57E-7), ACC (p < 4.16E-5), HDIST (p < 5.48E-8), and ICC (p < 1.20E-7). The BVeR method presents a reliable automatic alternative to the manual correction often requested in neuroimage studies, which can also reduce human subjectivity.ConclusionsThe BVeR algorithm shows to be a promising alternative to manual corrections, with potential impact in studies with large volumes of data.

Journal

Research on Biomedical Engineering – Springer Journals

Published: Dec 1, 2021

Keywords: Skull-stripping; Brain segmentation; Magnetic resonance imaging; Segmentation refinement; 3D slicer module

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