Dataset Information

Ccoffinn: Automated Wave Tracking in Cultured Cardiac Monolayers.

ABSTRACT: Cardiac arrhythmias are one of the most frequent causes of death worldwide. A popular biological model used to study arrhythmogenesis is the cultured cardiac cell monolayer, which provides a good trade-off between physiological relevance and experimental access. Excitation wave patterns are imaged using high-bandwidth detectors, producing large data sets that are typically analyzed manually. To make such analysis less time consuming and less subjective, we have designed and implemented a toolkit for segmentation and tracking of cardiac waves in optical mapping recordings. The toolkit is optimized for high-resolution detectors to accommodate the growing availability of inexpensive high-resolution detectors for life science imaging applications (e.g., scientific CMOS cameras). The software extracts key features of propagating waves, such as wavefront speed and entropy. The methods have been validated using synthetic data, and real-world examples are provided, showing a difference in conduction velocity between two different types of cardiac cell cultures.

SUBMITTER: Tomek J

PROVIDER: S-EPMC5072981 | biostudies-literature | 2016 Oct

REPOSITORIES: biostudies-literature

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Ccoffinn: Automated Wave Tracking in Cultured Cardiac Monolayers.

Tomek Jakub J Burton Rebecca A B RAB Bub Gil G

Biophysical journal 20161001 8

Cardiac arrhythmias are one of the most frequent causes of death worldwide. A popular biological model used to study arrhythmogenesis is the cultured cardiac cell monolayer, which provides a good trade-off between physiological relevance and experimental access. Excitation wave patterns are imaged using high-bandwidth detectors, producing large data sets that are typically analyzed manually. To make such analysis less time consuming and less subjective, we have designed and implemented a toolkit ...[more]

PMID: 27760347

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Project description:BackgroundAutomated 4D flow MRI valvular flow quantification without time-consuming manual segmentation might improve workflow.PurposeCompare automated valve segmentation (AS) to manual (MS), and manually corrected automated segmentation (AMS), in corrected atrioventricular septum defect (c-AVSD) patients and healthy volunteers, for assessing net forward volume (NFV) and regurgitation fraction (RF).Study typeRetrospective.Population27 c-AVSD patients (median, 23 years; interquartile range, 16-31 years) and 24 healthy volunteers (25 years; 12.5-36.5 years).Field strength/sequenceWhole-heart 4D flow MRI and cine steady-state free precession at 3T.AssessmentAfter automatic valve tracking, valve annuli were segmented on time-resolved reformatted trans-valvular velocity images by AS, MS, and AMS. NFV was calculated for all valves, and RF for right and left atrioventricular valves (RAVV and LAVV). NFV variation (standard deviation divided by mean NFV) and NFV differences (NFV difference of a valve vs. mean NFV of other valves) expressed internal NFV consistency.Statistical testsComparisons between methods were assessed by Wilcoxon signed-rank tests, and intra/interobserver variability by intraclass correlation coefficients (ICCs). P < 0.05 was considered statistically significant, with multiple testing correction.ResultsAMS mean analysis time was significantly shorter compared with MS (5.3 ± 1.6 minutes vs. 9.1 ± 2.5 minutes). MS NFV variation (6.0%) was significantly smaller compared with AMS (6.3%), and AS (8.2%). Median NFV difference of RAVV, LAVV, PV, and AoV between segmentation methods ranged from -0.7-1.0 mL, -0.5-2.8 mL, -1.1-3.6 mL, and - 3.1--2.1 mL, respectively. Median RAVV and LAVV RF, between 7.1%-7.5% and 3.8%-4.3%, respectively, were not significantly different between methods. Intraobserver/interobserver agreement for AMS and MS was strong-to-excellent for NFV and RF (ICC ≥0.88).Data conclusionMS demonstrates strongest internal consistency, followed closely by AMS, and AS. Automated segmentation, with or without manual correction, can be considered for 4D flow MRI valvular flow quantification.Level of evidence3 TECHNICAL EFFICACY: Stage 3.

Dataset Information

Ccoffinn: Automated Wave Tracking in Cultured Cardiac Monolayers.

Publications

Ccoffinn: Automated Wave Tracking in Cultured Cardiac Monolayers.

Similar Datasets

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