Purpose: To compare global functional parameters determined from a stack of cinematographic MR images of mouse heart by a manual segmentation and an automatic segmentation algorithm.
Materials And Methods: The manual and automatic segmentation results of 22 mouse hearts were compared. The automatic segmentation was based on propagation of a minimum cost algorithm in polar space starting from manually drawn contours in one heart phase. Intra- and interobserver variability as well as validity of the automatic segmentation was determined. To test the reproducibility of the algorithm the variability was calculated from the intra- and interobserver input.
Results: The mean time of segmentation for one dataset was around 10 minutes and approximately 2.5 hours for automatic and manual segmentation, respectively. There were no significant differences between the automatic and the manual segmentation except for the end systolic epicardial volume. The automatically derived volumes correlated well with the manually derived volumes (R(2) = 0.90); left ventricular mass with and without papillary muscle showed a correlation R(2) of 0.74 and 0.76, respectively. The manual intraobserver variability was superior to the interobserver variability and the variability of the automatic segmentation, while the manual interobserver variability was comparable to the variability of the automatic segmentation. The automatic segmentation algorithm reduced the bias of the intra- and interobserver variability.
Conclusion: We conclude that automatic segmentation of the mouse heart provides a fast and valid alternative to manual segmentation of the mouse heart.
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http://dx.doi.org/10.1002/jmri.21236 | DOI Listing |
Sensors (Basel)
December 2024
School of Computer and Information Engineering, Xinjiang Agricultural University, Urumqi 830052, China.
Reducing damage and missed harvest rates is essential for improving efficiency in unmanned cabbage harvesting. Accurate real-time segmentation of cabbage heads can significantly alleviate these issues and enhance overall harvesting performance. However, the complexity of the growing environment and the morphological variability of field-grown cabbage present major challenges to achieving precise segmentation.
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December 2024
Faculty of Medicine and Health Technology, Tampere University, 33720 Tampere, Finland.
Extracting behavioral information from animal sounds has long been a focus of research in bioacoustics, as sound-derived data are crucial for understanding animal behavior and environmental interactions. Traditional methods, which involve manual review of extensive recordings, pose significant challenges. This study proposes an automated system for detecting and classifying animal vocalizations, enhancing efficiency in behavior analysis.
View Article and Find Full Text PDFJ Clin Med
December 2024
Department of Biomedical Informatics and Artificial Intelligence, Silesian University of Technology, 41-800 Zabrze, Poland.
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View Article and Find Full Text PDFBioengineering (Basel)
December 2024
Department of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China.
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