An improved approach for the segmentation of starch granules in microscopic images

<p>Abstract</p> <p>Background</p> <p>Starches are the main storage polysaccharides in plants and are distributed widely throughout plants including seeds, roots, tubers, leaves, stems and so on. Currently, microscopic observation is one of the most important ways to investigate and analyze the structure of starches. The position, shape, and size of the starch granules are the main measurements for quantitative analysis. In order to obtain these measurements, segmentation of starch granules from the background is very important. However, automatic segmentation of starch granules is still a challenging task because of the limitation of imaging condition and the complex scenarios of overlapping granules.</p> <p>Results</p> <p>We propose a novel method to segment starch granules in microscopic images. In the proposed method, we first separate starch granules from background using automatic thresholding and then roughly segment the image using watershed algorithm. In order to reduce the oversegmentation in watershed algorithm, we use the roundness of each segment, and analyze the gradient vector field to find the critical points so as to identify oversegments. After oversegments are found, we extract the features, such as the position and intensity of the oversegments, and use fuzzy c-means clustering to merge the oversegments to the objects with similar features. Experimental results demonstrate that the proposed method can alleviate oversegmentation of watershed segmentation algorithm successfully.</p> <p>Conclusions</p> <p>We present a new scheme for starch granules segmentation. The proposed scheme aims to alleviate the oversegmentation in watershed algorithm. We use the shape information and critical points of gradient vector flow (GVF) of starch granules to identify oversegments, and use fuzzy c-mean clustering based on prior knowledge to merge these oversegments to the objects. Experimental results on twenty microscopic starch images demonstrate the effectiveness of the proposed scheme.</p

Decision tree algorithm applied to MIMIC-III database for the prediction of acute kidney injury in ICU patients

Objective Acute kidney injury (AKI) is one of the most common complications and fatal factors in intensive care unit (ICU). Accurate prediction of AKI risk and identification of key factors related to AKI can provide effective guidance for clinical decision-making and intervention for patients with AKI risk. Methods A total of 30 020 patients in ICU (including 17 222 AKI patients and 12 798 Non-AKI patients) were selected from the public database MIMIC-III in this study, and basic information, physiological and biochemical indicators, drug use, and comorbidity during their stay in ICU were collected. All patients were randomly divided into training sets and independent testing sets according to the ratio of 4:1, and logistic regression, random forest, and lightgbm were applied to construct models for AKI predication in three time points including 24 h, 48 h and 72 h, respectively. The 10-fold cross validation was used to train and validate various models to predict the occurrence of AKI, and obtain important features. Furthermore, 24 h prediction models were used to predict AKI every 24 h during the 7-day window. Results lightgbm achieved the best performance with AUC values of 0.90, 0.88, 0.87 for 24 h, 48 h, and 72 h prediction, respectively, and F1 values were 0.91, 0.88, and 0.86. In prediction of every 24 h, the success rates of identifying AKI patients were 89%, 83%, and 80% in one day, two days and three days in advance, respectively. It was found that the length of stay in ICU, body weight, albumin, systolic blood pressure, bicarbonate, glucose, white blood cell count, body temperature, diastolic blood pressure and blood urea nitrogen played vital roles in predicting AKI for ICU patients. Using only 24 important features, the models could still achieve prominent prediction performance. Conclusions Based on basic information, physiological and biochemical indicators, drug use, and comorbidity, machine learning methods can be adopted to effectively predict AKI risk for ICU patients at several time points, and determine the dominant factors relative to AKI

Speckle reducing bilateral filter for cattle follicle segmentation

<p>Abstract</p> <p>Background</p> <p>Ultrasound imaging technology has wide applications in cattle reproduction and has been used to monitor individual follicles and determine the patterns of follicular development. However, the speckles in ultrasound images affect the post-processing, such as follicle segmentation and finally affect the measurement of the follicles. In order to reduce the effect of speckles, a bilateral filter is developed in this paper.</p> <p>Results</p> <p>We develop a new bilateral filter for speckle reduction in ultrasound images for follicle segmentation and measurement. Different from the previous bilateral filters, the proposed bilateral filter uses normalized difference in the computation of the Gaussian intensity difference. We also present the results of follicle segmentation after speckle reduction. Experimental results on both synthetic images and real ultrasound images demonstrate the effectiveness of the proposed filter.</p> <p>Conclusions</p> <p>Compared with the previous bilateral filters, the proposed bilateral filter can reduce speckles in both high-intensity regions and low intensity regions in ultrasound images. The segmentation of the follicles in the speckle reduced images by the proposed method has higher performance than the segmentation in the original ultrasound image, and the images filtered by Gaussian filter, the conventional bilateral filter respectively.</p

Anomalous papillary muscle insertion into the mitral valve leaflet in hypertrophic obstructive cardiomyopathy: a lip nevus sign in echocardiography

BackgroundAnomalous papillary muscle (APM) insertion into the mitral valve leaflet is rare but clinically important in hypertrophic obstructive cardiomyopathy (HOCM). In this study, we report the detection rate of APM insertion into the mitral valve using preoperative imaging modalities and the surgical outcomes of the patients.MethodsBy retrospectively reviewing the clinical records of patients with HOCM who underwent surgical treatment by a single operation group at our center from January 2020 to June 2023, patients with APM insertion into the mitral valve leaflet were identified. Baseline data, image characteristics, and surgical outcomes were analyzed.ResultsThe incidence of APM insertion into the mitral valve leaflet was 5.1% (8/157). The insertion site was located at A3 in six cases, which was more common than at A2 (n = 2). Preoperative echocardiography was used to identify two patients (25%) with APM insertion. We observed a particular echocardiographic feature for APM in HOCM patients, which was noted as a “lip nevus sign”, with a higher detection rate (62.5%). All patients successfully underwent septal myectomy with concomitant APM excision or mitral valve replacement via the transaortic (n = 5) or transmitral (n = 3) approach. The mean age was 49.0 ± 17.4 years and seven patients (87.5%) were female. Interventricular septum thickness (17.0 mm vs. 13.3 mm, P = 0.012) and left ventricular outflow gradient (117.5 mmHg vs. 7.5 mmHg, P = 0.012) were significantly decreased after surgery. Residual outflow obstruction, systolic anterior motion, and ≥3+ mitral regurgitation were negative. During the follow-up of 26.2 ± 12.2 months, there were no reported operations, adverse events, mitral regurgitation aggravations, recurrences of outflow obstruction, or instances of SAM.ConclusionsPapillary muscles inserted into the mitral valve leaflet are a subtype of subvalvular malformation in HOCM that requires surgical correction. The lip nevus sign on echocardiography is a characteristic of APM insertion in HOCM and may improve the preoperative detection rate. Adequate myectomy with anomalous papillary muscle excision has achieved good results in reducing the outflow gradient and eliminating mitral regurgitation, with good outcomes at short-to-intermediate follow-up

Conversion Discriminative Analysis on Mild Cognitive Impairment Using Multiple Cortical Features from MR Images

Neuroimaging measurements derived from magnetic resonance imaging provide important information required for detecting changes related to the progression of mild cognitive impairment (MCI). Cortical features and changes play a crucial role in revealing unique anatomical patterns of brain regions, and further differentiate MCI patients from normal states. Four cortical features, namely, gray matter volume, cortical thickness, surface area, and mean curvature, were explored for discriminative analysis among three groups including the stable MCI (sMCI), the converted MCI (cMCI), and the normal control (NC) groups. In this study, 158 subjects (72 NC, 46 sMCI, and 40 cMCI) were selected from the Alzheimer's Disease Neuroimaging Initiative. A sparse-constrained regression model based on the l2-1-norm was introduced to reduce the feature dimensionality and retrieve essential features for the discrimination of the three groups by using a support vector machine (SVM). An optimized strategy of feature addition based on the weight of each feature was adopted for the SVM classifier in order to achieve the best classification performance. The baseline cortical features combined with the longitudinal measurements for 2 years of follow-up data yielded prominent classification results. In particular, the cortical thickness produced a classification with 98.84% accuracy, 97.5% sensitivity, and 100% specificity for the sMCI–cMCI comparison; 92.37% accuracy, 84.78% sensitivity, and 97.22% specificity for the cMCI–NC comparison; and 93.75% accuracy, 92.5% sensitivity, and 94.44% specificity for the sMCI–NC comparison. The best performances obtained by the SVM classifier using the essential features were 5–40% more than those using all of the retained features. The feasibility of the cortical features for the recognition of anatomical patterns was certified; thus, the proposed method has the potential to improve the clinical diagnosis of sub-types of MCI and predict the risk of its conversion to Alzheimer's disease

A 3-D bilateral filter for speckle reduction in 3-D ultrasound images for cattle follicle segmentation

In this paper, we investigate the reduction of speckles in 3-D ultrasound images using a 3-D bilateral filter for cattle follicle segmentation and volume estimation. Adaptive 3-D bilateral filter is developed to reduce the speckles in the ultrasound images effectively. We compared the proposed 3-D bilateral filter with the conventional 3-D bilateral filter and 3-D Gaussian filter using both 3-D synthetic speckled images and real ultrasound images. Quantitative analysis verified the effectiveness of the proposed method. © 2012 IEEE

A generalizable and tunable engineered ecosystem provides a clear route to prosperity and well-being to harness the worlds aquatic blue food systems to help end hunger: A perspective.

Seafood security is essential in modern society. In 2013, Bush and colleagues stated, Aquaculture, farming aquatic organisms, provides close to 50% of the worlds supply of seafood, with a value of United States $125 billion. It makes up 13% of the worlds animal-source protein (excluding eggs and dairy) and employs an estimated 24 million people. With the increase in the human population and reducing fishing resources, humans increasingly rely on aquacultural products as the primary protein sources for many countries. Aquacultural productivity has been improving in recent years, and in certain countries, the aquaculture output is more than the fishing output. For example, Chinese aquaculture production is more than fishing output, which provides one-third of animal protein. Thus, intensive aquaculture has become the main supply with global aquatic products (FAO). In recent years, it is estimated that each person consumption of aquaculture products is 130 kg in some countries (Iceland). Here, we illustrate the road blocker in farmed shrimp production and provide our resolution. The global pandemic of white spot syndrome (WSS), caused by the white spot syndrome virus (WSSV), bears a devastating economic loss in farmed shrimp production, thereby jeopardizing seafood security. Currently, there is no effective control for WSS. Conventional single-species intensive farming removes the spatiotemporal interaction between different species. We hypothesize that establishing the spatiotemporal interface of a predator-prey may control WSS outbreak. We search for the pathways for the mechanisms by which predator-prey species interact and compete across spatial scales to characterize WSSV dispersal at regional scales for the local spatiotemporal structure of viral transmission. Thus, we create a generalizable and tunable engineered ecosystem that provides a clear route to prosperity and well-being to harness the worlds aquatic blue food systems to help end hunger

A Hybrid Framework for Multivariate Time Series Forecasting of Daily Urban Water Demand Using Attention-Based Convolutional Neural Network and Long Short-Term Memory Network

Urban water demand forecasting is beneficial for reducing the waste of water resources and enhancing environmental protection in sustainable water management. However, it is a challenging task to accurately predict water demand affected by a range of factors with nonlinear and uncertainty temporal patterns. This paper proposes a new hybrid framework for urban daily water demand with multiple variables, called the attention-based CNN-LSTM model, which combines convolutional neural network (CNN), long short-term memory (LSTM), attention mechanism (AM), and encoder-decoder network. CNN layers are used to learn the representation and correlation between multivariate variables. LSTM layers are utilized as the building blocks of the encoder-decoder network to capture temporal characteristics from the input sequence, while AM is introduced to the encoder-decoder network to assign corresponding attention according to the importance of water demand multivariable time series at different times. The new hybrid framework considers correlation between multiple variables and neglects irrelevant data points, which helps to improve the prediction accuracy of multivariable time series. The proposed model is contrasted with the LSTM model, the CNN-LSTM model, and the attention-based LSTM to predict the daily water demand time series in Suzhou, China. The results show that the hybrid model achieves higher prediction performance with the smallest mean absolute error (MAE), root mean squared error (RMSE), and mean absolute percentage error (MAPE), and largest correlation coefficient (R2)

Mixed model methods for genomic prediction and variance component estimation of additive and dominance effects using SNP markers.

We established a genomic model of quantitative trait with genomic additive and dominance relationships that parallels the traditional quantitative genetics model, which partitions a genotypic value as breeding value plus dominance deviation and calculates additive and dominance relationships using pedigree information. Based on this genomic model, two sets of computationally complementary but mathematically identical mixed model methods were developed for genomic best linear unbiased prediction (GBLUP) and genomic restricted maximum likelihood estimation (GREML) of additive and dominance effects using SNP markers. These two sets are referred to as the CE and QM sets, where the CE set was designed for large numbers of markers and the QM set was designed for large numbers of individuals. GBLUP and associated accuracy formulations for individuals in training and validation data sets were derived for breeding values, dominance deviations and genotypic values. Simulation study showed that GREML and GBLUP generally were able to capture small additive and dominance effects that each accounted for 0.00005-0.0003 of the phenotypic variance and GREML was able to differentiate true additive and dominance heritability levels. GBLUP of the total genetic value as the summation of additive and dominance effects had higher prediction accuracy than either additive or dominance GBLUP, causal variants had the highest accuracy of GREML and GBLUP, and predicted accuracies were in agreement with observed accuracies. Genomic additive and dominance relationship matrices using SNP markers were consistent with theoretical expectations. The GREML and GBLUP methods can be an effective tool for assessing the type and magnitude of genetic effects affecting a phenotype and for predicting the total genetic value at the whole genome level