ArticleJournal of neuroimaging : official journal of the American Society of Neuroimaging2022
Improved neonatal brain MRI segmentation by interpolation of motion corrupted slices.
Article in Journal of neuroimaging : official journal of the American Society of Neuroimaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed, 5 citations in OpenAlex.
- Application of multimodal deep learning and multi-instance learning fusion techniques in predicting STN-DBS outcomes for Parkinson's disease patients.Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics · 2024Article
- Improved neonatal brain MRI segmentation by interpolation of motion corrupted slices.Journal of neuroimaging : official journal of the American Society of Neuroimaging · 2022Article
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10 authors at 5 institutions in 2 countries.
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No grant is acknowledged in the PubMed record.
Abstract
background and purposeTo apply and evaluate an intensity-based interpolation technique, enabling segmentation of motion-affected neonatal brain MRI.
methodsModerate-late preterm infants were enrolled in a prospective cohort study (Brain Imaging in Moderate-late Preterm infants "BIMP-study") between August 2017 and November 2019. T2-weighted MRI was performed around term equivalent age on a 3T MRI. Scans without motion (n = 27 [24%], control group) and with moderate-severe motion (n = 33 [29%]) were included. Motion-affected slices were re-estimated using intensity-based shape-preserving cubic spline interpolation, and automatically segmented in eight structures. Quality of interpolation and segmentation was visually assessed for errors after interpolation. Reliability was tested using interpolated control group scans (18/54 axial slices). Structural similarity index (SSIM) was used to compare T2-weighted scans, and Sørensen-Dice was used to compare segmentation before and after interpolation. Finally, volumes of brain structures of the control group were used assessing sensitivity (absolute mean fraction difference) and bias (confidence interval of mean difference).
resultsVisually, segmentation of 25 scans (22%) with motion artifacts improved with interpolation, while segmentation of eight scans (7%) with adjacent motion-affected slices did not improve. Average SSIM was .895 and Sørensen-Dice coefficients ranged between .87 and .97. Absolute mean fraction difference was ≤0.17 for less than or equal to five interpolated slices. Confidence intervals revealed a small bias for cortical gray matter (0.14-3.07 cm
conclusionAccording to qualitative and quantitative assessment, intensity-based interpolation reduced the percentage of discarded scans from 29% to 7%.
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