Validation and Development of a New Automatic Algorithm for Time-Resolved Segmentation of the Left Ventricle in
Jane Tufvesson1, Erik Hedström2, Katarina Steding-Ehrenborg3
1Department of Clinical Physiology, Lund University Hospital, Lund University, 221 85 Lund, Sweden ; Department of Numerical Analysis, Centre for Mathematical Sciences, Faculty of Engineering, Lund University, 221 00 Lund, Sweden.
Insights
This study developed an automatic algorithm for left ventricle (LV) segmentation in cardiac MRI, achieving accuracy comparable to manual methods. This innovation aims to improve efficiency and consistency in cardiovascular image analysis for clinical use.
Area of Science:
- Cardiovascular Imaging
- Medical Image Analysis
- Computational Anatomy
Background:
- Manual left ventricle (LV) segmentation in cardiovascular magnetic resonance (CMR) is standard but time-consuming and subjective.
- Existing automated methods often neglect long-axis motion, a key factor in stroke volume.
- Accurate LV quantification is crucial for diagnosing and managing cardiovascular diseases.
Purpose of the Study:
- To develop and validate an automated algorithm for time-resolved LV segmentation.
- To address limitations of previous methods by incorporating long-axis motion.
- To provide a tool for accurate and efficient LV volume and mass quantification.
Main Methods:
- A novel automated algorithm utilizing a deformable model with expectation-maximization was developed.
- The algorithm incorporates automatic removal of papillary muscles and outflow tract detection.
- Validation was performed on 90 subjects (40 training, 50 testing) using manual delineation as the reference standard.
Main Results:
- The automated algorithm demonstrated high accuracy in segmenting the LV.
- Mean differences from manual delineation in the test set were: EDV -11 mL, ESV 1 mL, EF -3%, and LVM 4 g.
- The algorithm's performance was comparable to interobserver variability in manual segmentation.
Conclusions:
- The developed automated LV segmentation algorithm achieves accuracy suitable for clinical application.
- This method offers a more efficient and reproducible alternative to manual delineation.
- The algorithm and associated data are publicly available for benchmarking and further research.
Introduction:
Manual delineation of the left ventricle is clinical standard for quantification of cardiovascular magnetic resonance images despite being time consuming and observer dependent. Previous automatic methods generally do not account for one major contributor to stroke volume, the long-axis motion. Therefore, the aim of this study was to develop and validate an automatic algorithm for time-resolved segmentation covering the whole left ventricle, including basal slices affected by long-axis motion.
Methods:
Ninety subjects imaged with a cine balanced steady state free precession sequence were included in the study (training set n = 40, test set n = 50). Manual delineation was reference standard and second observer analysis was performed in a subset (n = 25). The automatic algorithm uses deformable model with expectation-maximization, followed by automatic removal of papillary muscles and detection of the outflow tract.
Results:
The mean differences between automatic segmentation and manual delineation were EDV -11 mL, ESV 1 mL, EF -3%, and LVM 4 g in the test set.
Conclusions:
The automatic LV segmentation algorithm reached accuracy comparable to interobserver for manual delineation, thereby bringing automatic segmentation one step closer to clinical routine. The algorithm and all images with manual delineations are available for benchmarking.
