Semi-automated left ventricular endocardial detection versus hand-tracing in the measurement of left ventricular volumes and ejection fraction in daily clinical practice

Background The common method of assessing left ventricle (LV) volumes and ejection fraction (EF) is hand-tracing Biplane Simpson method. Alternatively, ultrasound vendors offer different semi-automated LV endocardial border detection software with anatomical intelligence to assess LV volumes and EF. By using speckle-tracking technique, this software tracks the LV endocardium throughout the cardiac cycle and computes the LV volumes in every image frame using the disk summation method from which a volume-curve is generated, and the EF is calculated using the maximum and minimum volumes obtained. Data on the performance of this method in comparison with the hand-tracing Biplane Simpson method in daily clinical practice is scarce. Purpose To determine the accuracy of LV volumes and EF using semi-automated LV endocardial detection tracing, and to compare the reproducibility of this method with the hand-tracing Biplane Simpson method, among operators with varying level of experience in echocardiography. Methods This was a single center retrospective observational study, conducted in year 2020. 127 patients, aged >18 years, who underwent clinically indicated transthoracic echocardiography were recruited. The echocardiographic images were analyzed independently in a blinded fashion by 3 operators – a sonographer, a fellow-in-training and a cardiologist specialized in echocardiography. The LV volumes and EF were first measured using hand-tracing Biplane Simpson method, then repeated using semi-automated tracing at a different time and the operator were blinded to the initial hand-tracing measurements. Results The mean age of patients was 50±16 years, 35.4% were male, mean body surface area was 1.62±0.18m2, 92.1% were in sinus rhythm, and 61.4% had good acoustic window. Table 1 shows the LV end-diastolic volume (EDV), end-systolic volume (ESV) and EF, measured using different method, by the 3 operators. There were excellent correlation and agreement between semi-automated tracing measurements and hand-tracing measurements of LV EDV (r=0.985, LOA [mean ± 1.96 SD] 16.9 ml, ICC 0.991), ESV (r=0.990, LOA 12.7 ml, ICC 0.994) and EF (r=0.962, LOA 7.43%, ICC 0.967) by experienced cardiologist. The limit of agreement (LOA) between cardiologist and sonographer for semi-automated tracing measurement of LV EDV, ESV and EF were 29.13 ml, 19.74 ml and 9.25% respectively, which was comparable with that of hand-tracing measurement. The agreement between cardiologist and fellow-in-training for semi-automated tracing measurement of LV volumes and EF was slightly better than hand-tracing method, with a LOA of 25.60 ml, 17.48 ml and 7.08%, for EDV, ESV and EF respectively (Table 2). Conclusion In daily clinical practice, measurement of LV volumes and EF using semi-automated LV endocardial tracing method is accurate and demonstrates comparable reproducibility with hand-tracing Biplane Simpson method among operators with different level of experience in echocardiography. FUNDunding Acknowledgement Type of funding sources: None.

Semi-automated Tracing of Hamstring Muscle Architecture for B-mode Ultrasound Images

Hamstring strains are the most prevalent injury sustained by field-sport athletes. Insufficiencies in the architectural characteristics of the hamstring muscles can heighten an athlete’s risk of incurring a hamstring strain. To evaluate the influence of hamstring muscle architectural characteristics (i. e., fascicle length, pennation angle, muscle thickness) on injury risk, it is necessary to precisely evaluate these characteristics. Considering this, our aim was to develop and evaluate the precision of a novel semi-automated tracing software to measure the architectural characteristics of the biceps femoris long head (the most commonly injured hamstring muscle) in B-mode ultrasound images. We acquired static sonograms of the biceps femoris long head from ten healthy male field-sport athletes. The architectural characteristics (fascicle length, pennation angle, and muscle thickness) of participants’ biceps femoris long head were evaluated 10 times using the tracing software, with the specific purpose of determining its measurement precision. The tracing software precisely measured the architectural characteristics of the participants’ biceps femoris long head: fascicle length (% CV: 0.64–1.12), pennation angle (% CV: 2.58–10.70), muscle thickness (% CV: 0.48–2.04) Our semi-automated skeletal muscle tracing algorithm precisely measures fascicle length, pennation angles, and muscle thickness of the biceps femoris long head in static B-mode ultrasound images.

TopoStats – a program for automated tracing of biomolecules from AFM images

We present TopoStats, a Python toolkit for automated editing and analysis of Atomic Force Microscopy images. The program automates identification and tracing of individual molecules in circular and linear conformations without user input. TopoStats was able to identify and trace a range of molecules within AFM images, finding, on average, 90% of all individual molecules and molecular assemblies within a wide field of view, and without the need for prior processing. DNA minicircles of varying size, DNA origami rings and pore forming proteins were identified and accurately traced with contour lengths of traces typically within 10 nm of the predicted contour length. TopoStats was also able to reliably identify and trace linear and enclosed circular molecules within a mixed population. The program is freely available via GitHub (https://github.com/afm-spm/TopoStats) and is intended to be modified and adapted for use if required.

Multispectral tracing in densely labeled mouse brain with nTracer

Summary This note describes nTracer, an ImageJ plug-in for user-guided, semi-automated tracing of multispectral fluorescent tissue samples. This approach allows for rapid and accurate reconstruction of whole cell morphology of large neuronal populations in densely labeled brains. Availability and implementation nTracer was written as a plug-in for the open source image processing software ImageJ. The software, instructional documentation, tutorial videos, sample image and sample tracing results are available at https://www.cai-lab.org/ntracer-tutorial. Supplementary information Supplementary data are available at Bioinformatics online.