Analysis of a Pulse Rate Variability Measurement Using a Smartphone Camera.

Background: Heart rate variability (HRV) provides information about the activity of the autonomic nervous system. Because of the small amount of data collected, the importance of HRV has not yet been proven in clinical practice. To collect population-level data, smartphone applications leveraging photoplethysmography (PPG) and some medical knowledge could provide the means for it. Objective: To assess the capabilities of our smartphone application, we compared PPG (pulse rate variability (PRV)) with ECG (HRV). To have a baseline, we also compared the differences among ECG channels. Method: We took fifty parallel measurements using iPhone 6 at a 240 Hz sampling frequency and Cardiax PC-ECG devices. The correspondence between the PRV and HRV indices was investigated using correlation, linear regression, and Bland-Altman analysis. Results: High PPG accuracy: the deviation of PPG-ECG is comparable to that of ECG channels. Mean deviation between PPG-ECG and two ECG channels: RR: 0.01 ms-0.06 ms, SDNN: 0.78 ms-0.46 ms, RMSSD: 1.79 ms-1.21 ms, and pNN50: 2.43%-1.63%. Conclusions: Our iPhone application yielded good results on PPG-based PRV indices compared to ECG-based HRV indices and to differences among ECG channels. We plan to extend our results on the PPG-ECG correspondence with a deeper analysis of the different ECG channels.

Automatic segmentation of the liver from multi- and single-phase contrast-enhanced CT images.

Segmentation of contrast-enhanced abdominal CT images is required by many clinical applications of computer aided diagnosis and therapy planning. The research on automated methods involves different organs among which the liver is the most emphasized. In the current clinical practice more images (at different phases) are acquired from the region of interest in case of a contrast-enhanced abdominal CT examination. The majority of the existing methods, however, use only the portal-venous image to segment the liver. This paper presents a method that automatically segments the liver by combining more phases of the contrast-enhanced CT examination. The method uses region-growing facilitated by pre- and post-processing functions, which incorporate anatomical and multi-phase information to eliminate over- and under-segmentation. Another method, which uses only the portal-venous phase to segment the liver automatically, is also presented. Both methods were evaluated using different datasets, which showed that the result of multi-phase method can be used without or after minor correction in nearly 94% of the cases, and the single-phase method can provide result comparable with non-expert manual segmentation in 90% of the cases. The comparison of the two methods demonstrates that automatic segmentation is more reliable when the information of more phases is combined.

Comparison and evaluation of methods for liver segmentation from CT datasets.

This paper presents a comparison study between 10 automatic and six interactive methods for liver segmentation from contrast-enhanced CT images. It is based on results from the "MICCAI 2007 Grand Challenge" workshop, where 16 teams evaluated their algorithms on a common database. A collection of 20 clinical images with reference segmentations was provided to train and tune algorithms in advance. Participants were also allowed to use additional proprietary training data for that purpose. All teams then had to apply their methods to 10 test datasets and submit the obtained results. Employed algorithms include statistical shape models, atlas registration, level-sets, graph-cuts and rule-based systems. All results were compared to reference segmentations five error measures that highlight different aspects of segmentation accuracy. All measures were combined according to a specific scoring system relating the obtained values to human expert variability. In general, interactive methods reached higher average scores than automatic approaches and featured a better consistency of segmentation quality. However, the best automatic methods (mainly based on statistical shape models with some additional free deformation) could compete well on the majority of test images. The study provides an insight in performance of different segmentation approaches under real-world conditions and highlights achievements and limitations of current image analysis techniques.

Geometrical model-based segmentation of the organs of sight on CT images.

Segmentation of organs of sight such as the eyeballs, lenses, and optic nerves is a time consuming task for clinicians. The small size of the organs and the similar density of the surrounding tissues make the segmentation difficult. We developed a new algorithm to segment these organs with minimal user interaction. The algorithm needs only three seed points to fit an initial geometrical model to start an effective segmentation. The clinical evaluation shows that the output of our method is useful in clinical practice.