BackgroundRice panicle phenotyping is important in rice breeding, and rice panicle segmentation is the first and key step for image-based panicle phenotyping. Because of the challenge of illumination differentials, panicle shape deformations, rice accession variations, different reproductive stages and the field’s complex background, rice panicle segmentation in the field is a very large challenge.ResultsIn this paper, we propose a rice panicle segmentation algorithm called Panicle-SEG, which is based on simple linear iterative clustering superpixel regions generation, convolutional neural network classification and entropy rate superpixel optimization. To build the Panicle-SEG-CNN model and test the segmentation effects, 684 training images and 48 testing images were randomly selected, respectively. Six indicators, including Qseg, Sr, SSIM, Precision, Recall and F-measure, are employed to evaluate the segmentation effects, and the average segmentation results for the 48 testing samples are 0.626, 0.730, 0.891, 0.821, 0.730, and 76.73%, respectively. Compared with other segmentation approaches, including HSeg, i2 hysteresis thresholding and jointSeg, the proposed Panicle-SEG algorithm has better performance on segmentation accuracy. Meanwhile, the executing speed is also improved when combined with multithreading and CUDA parallel acceleration. Moreover, Panicle-SEG was demonstrated to be a robust segmentation algorithm, which can be expanded for different rice accessions, different field environments, different camera angles, different reproductive stages, and indoor rice images. The testing dataset and segmentation software are available online.ConclusionsIn conclusion, the results demonstrate that Panicle-SEG is a robust method for panicle segmentation, and it creates a new opportunity for nondestructive yield estimation.Electronic supplementary materialThe online version of this article (10.1186/s13007-017-0254-7) contains supplementary material, which is available to authorized users.

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Abstract-Because it is more convenient to measure photoplethysmography (PPG) than ECG, PPG is supposed as a surrogate of ECG for heart rate variability (HRV) analysis. In this study, we measured the spectrum of pulse rate variability (PRV) from PPG and its square coherence spectrum with HRV from ECG before and after exercise. The results showed that the spectrum of PRV corresponds almost well with HRV, especially for the healthy subjects at rest. However, stimuli conditions such as exercise will decrease the correlation, especially for the high frequency (HF) component. It indicates that whether PRV can be used as an alternative to HRV depends on the applications and conditions.

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Carotid intima-media thickness (IMT) has been one widely used index of early carotid atherosclerosis. We speculated that the influence of blood pressure variability (BPV) on early carotid atherosclerosis may be varied by the location of the carotid artery and diabetes history. Thus, the goal of this study was to evaluate the effects of BPV on early arteriosclerosis progression in different segments of the carotid artery for hypertension with and without diabetes.A total of 148 hypertension patients who underwent 24 hours ambulatory blood pressure (BP) monitoring and carotid ultrasonography were enrolled in this study. Of them, 84 subjects were without diabetes, and 64 subjects were with diabetes. Short-term BPV during daytime, nighttime, and over 24 hours were evaluated through standard deviation (SD) and average real variability (ARV). We measured carotid IMT at left and right common carotid artery (CCA), carotid bulb, and the origin of the internal carotid artery (ICA). The associations between segment-specific measurements of carotid IMT and 24 hours ambulatory BPV were analyzed.We found that IMT at the common carotid artery (CCA-IMT) and IMT at the internal carotid artery (ICA-IMT) were more closely associated with BPV than was carotid bulb IMT. In addition, for all subjects, BPV was clearly associated with left CCA-IMT but not with right CCA-IMT. Furthermore, in diabetes patients, nighttime systolic BPV was independently related to mean CCA-IMT (P < 0.01) and mean bulb IMT (P < 0.01). In contrast, in nondiabetes patients, daytime and 24 hours systolic BPV was positively associated with mean CCA-IMT (P < 0.05), but not independent after adjusting for baseline characteristics such as age and sex.The findings of our study indicate a segment-specific association between carotid IMT and 24 hours ambulatory BPV, and the associations also vary according to the diabetes history. We conclude that BPV plays a distinct role in early carotid arteriosclerosis progression within different segments of the carotid artery, especially for the hypertensions with and without diabetes.

BackgroundHigh blood pressure (BP) is among significant risk factor for stroke and other vascular occurrences, it experiences nonstop fluctuations over time as a result of a complex interface among cardiovascular control mechanisms. Large blood pressure variability (BPV) has been proved to be promising in providing potential regulatory mechanisms of the cardiovascular system. Although the previous studies also showed that BPV is associated with increased carotid intima-media thickness (IMT) and plaque, whether the correlation between variability in blood pressure and left common carotid artery-intima-media thickness (LCCA-IMT) is stronger than right common carotid artery-intima-media thickness (RCCA-IMT) remains uncertain in hypertension.MethodsWe conduct a study (78 hypertensive subjects, aged 28–79) to evaluate the relationship between BPV and carotid intima-media thickness in Shenzhen. The blood pressure was collected using the 24 h ambulatory blood pressure monitoring, and its variability was evaluated using standard deviation (SD), coefficient of variation (CV), and average real variability (ARV) during 24 h, daytime and nighttime. All the IMT measurements are collected by ultrasound.ResultsAs the results showed, 24 h systolic blood pressure variability (SBPV) evaluated by SD and ARV were significantly related to LCCA-IMT (r1 = 0.261, P = 0.021; r1 = 0.262, P = 0.021, resp.). For the daytime diastolic blood pressure variability (DBPV), ARV indices were significantly related to LCCA-IMT (r1 = 0.239, P = 0.035), which differed form BPV evaluated by SD and CV. For the night time, there is no significant correlation between the BPV and IMT. Moreover, for all the subjects, there is no significant correlation between the BPV and RCCA-IMT/number of plaques, whereas, the SD, CV, and ARV of daytime SBP showed a positive correlation with LCCA-IMT (r1 = 0.312, P = 0.005; r1 = 0.255, P = 0.024; r1 = 0.284, P = 0.012, resp.). Moreover, the ARV of daytime SBPV, 24 h SBPV and nighttime DBPV showed a positive correlation with the number of plaques of LCCA (r1 = 0.356, P = 0.008; r1 = 0.297, P = 0.027; r1 = 0.278, P = 0.040, resp.). In addition, the number of plaques in LCCA had higher correlation with pulse pressure and diastolic blood pressure than that in RCCA. And multiple regression analysis indicated LCCA-IMT might not only be influenced by age or smoking but also by the SD index of daytime SBPV (p = 0.035).ConclusionsThe results show that SBPV during daytime and 24 h had significant correlation with IMT, for the hypertensive subjects from the southern area of China. Moreover, we also found the daytime SBPV to be the best predictor for the progression of IMT in multivariate regression analysis. In addition, the present study suggests that the correlation between BPV and left common carotid artery—intima-media thickness/number of plaques is stronger than right common carotid artery-intima-media thickness/number of plaques.

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Personalization of hemodynamic modeling plays a crucial role in functional prediction of the cardiovascular system (CVS). While reduced-order models of one-dimensional (1D) blood vessel models with zero-dimensional (0D) blood vessel and heart models have been widely recognized to be an effective tool for reasonably estimating the hemodynamic functions of the whole CVS, practical personalized models are still lacking. In this paper, we present a novel 0-1D coupled, personalized hemodynamic model of the CVS that can predict both pressure waveforms and flow velocities in arteries. Methods: We proposed a methodology by combining the multiscale CVS model with the Levenberg-Marquardt optimization algorithm for effectively solving an inverse problem based on measured blood pressure waveforms. Hemodynamic characteristics including brachial arterial pressure waveforms, artery diameters, stroke volumes, and flow velocities were measured noninvasively for 62 volunteers aged from 20 to 70 years for developing and validating the model. Results: The estimated arterial stiffness shows a physiologically realistic distribution. The model-fitted individual pressure waves have an averaged mean square error (MSE) of 7.1 mmHg 2 ; simulated blood flow velocity waveforms in carotid artery match ultrasound measurements well, achieving an average correlation coefficient of 0.911. Conclusion: The model is efficient, versatile, and capable of obtaining well-fitting individualized pressure waveforms while reasonably predicting flow waveforms. Significance: The proposed methodology of personalized hemodynamic modeling may therefore facilitate individualized patient-specific assessment of both physiological and pathological functions of the CVS.