546 research outputs found
Clique Identification and Propagation for Multimodal Brain Tumor Image Segmentation
Brain tumors vary considerably in size, morphology, and location across patients, thus pose great challenge in automated brain tumor segmentation methods. Inspired by the concept of clique in graph theory, we present a clique-based method for multimodal brain tumor segmentation that considers a brain tumor image as a graph and automatically segment it into different sub-structures based on the clique homogeneity. Our proposed method has three steps, neighborhood construction, clique identification, and clique propagation. We constructed the neighborhood of each pixel based on its similarities to the surrounding pixels, and then extracted all cliques with a certain size k to evaluate the correlations among different pixels. The connections among all cliques were represented as a transition matrix, and a clique propagation method was developed to group the cliques into different regions. This method is also designed to accommodate multimodal features, as multimodal neuroimaging data is widely used in mapping the tumor-induced changes in the brain. To evaluate this method, we conduct the segmentation experiments on the publicly available Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) dataset. The qualitative and quantitative results demonstrate that our proposed clique-based method achieved better performance compared to the conventional pixel-based methods
Corrigendum to: Nurses’ knowledge on phlebotomy in tertiary hospitals in China: a cross-sectional multicentric survey
This is a correction of Biochemia Medica
2018;28(1):010703. DOI: https://doi.org/10.11613/
BM.2018.01070
Stochastic uncertainty analysis for unconfined flow systems
This is the published version. Copyright American Geophysical Union[1] A new stochastic approach proposed by Zhang and Lu (2004), called the Karhunen-Loeve decomposition-based moment equation (KLME), has been extended to solving nonlinear, unconfined flow problems in randomly heterogeneous aquifers. This approach is on the basis of an innovative combination of Karhunen-Loeve decomposition, polynomial expansion, and perturbation methods. The random log-transformed hydraulic conductivity field (lnKS) is first expanded into a series in terms of orthogonal Gaussian standard random variables with their coefficients obtained as the eigenvalues and eigenfunctions of the covariance function of lnKS. Next, head h is decomposed as a perturbation expansion series Σh(m), where h(m) represents the mth-order head term with respect to the standard deviation of lnKS. Then h(m) is further expanded into a polynomial series of m products of orthogonal Gaussian standard random variables whose coefficients image are deterministic and solved sequentially from low to high expansion orders using MODFLOW-2000. Finally, the statistics of head and flux are computed using simple algebraic operations on image A series of numerical test results in 2-D and 3-D unconfined flow systems indicated that the KLME approach is effective in estimating the mean and (co)variance of both heads and fluxes and requires much less computational effort as compared to the traditional Monte Carlo simulation technique
Deep Dynamic Epidemiological Modelling for COVID-19 Forecasting in Multi-level Districts
Objective: COVID-19 has spread worldwide and made a huge influence across the
world. Modeling the infectious spread situation of COVID-19 is essential to
understand the current condition and to formulate intervention measurements.
Epidemiological equations based on the SEIR model simulate disease development.
The traditional parameter estimation method to solve SEIR equations could not
precisely fit real-world data due to different situations, such as social
distancing policies and intervention strategies. Additionally, learning-based
models achieve outstanding fitting performance, but cannot visualize
mechanisms. Methods: Thus, we propose a deep dynamic epidemiological (DDE)
method that combines epidemiological equations and deep-learning advantages to
obtain high accuracy and visualization. The DDE contains deep networks to fit
the effect function to simulate the ever-changing situations based on the
neural ODE method in solving variants' equations, ensuring the fitting
performance of multi-level areas. Results: We introduce four SEIR variants to
fit different situations in different countries and regions. We compare our DDE
method with traditional parameter estimation methods (Nelder-Mead, BFGS,
Powell, Truncated Newton Conjugate-Gradient, Neural ODE) in fitting the
real-world data in the cases of countries (the USA, Columbia, South Africa) and
regions (Wuhan in China, Piedmont in Italy). Our DDE method achieves the best
Mean Square Error and Pearson coefficient in all five areas. Further, compared
with the state-of-art learning-based approaches, the DDE outperforms all
techniques, including LSTM, RNN, GRU, Random Forest, Extremely Random Trees,
and Decision Tree. Conclusion: DDE presents outstanding predictive ability and
visualized display of the changes in infection rates in different regions and
countries
Automatic segmentation of overlapping cervical smear cells based on local distinctive features and guided shape deformation
Automated segmentation of cells from cervical smears poses great challenge to biomedical image analysis because of the noisy and complex background, poor cytoplasmic contrast and the presence of fuzzy and overlapping cells. In this paper, we propose an automated segmentation method for the nucleus and cytoplasm in a cluster of cervical cells based on distinctive local features and guided sparse shape deformation. Our proposed approach is performed in two stages: segmentation of nuclei and cellular clusters, and segmentation of overlapping cytoplasm. In the rst stage, a set of local discriminative shape and appearance cues of image superpixels is incorporated and classi ed by the Support Vector Machine (SVM) to segment the image into nuclei, cellular clusters, and background. In the second stage, a robust shape deformation framework is proposed, based on Sparse Coding (SC) theory and guided by representative shape features, to construct the cytoplasmic shape of each overlapping cell. Then, the obtained shape is re ned by the Distance Regularized Level Set Evolution (DRLSE) model. We evaluated our approach using the ISBI 2014 challenge dataset, which has 135 synthetic cell images for a total of 810 cells. Our results show that our approach outperformed existing approaches in segmenting overlapping cells and obtaining accurate nuclear boundaries. Keywords: overlapping cervical smear cells, feature extraction, sparse coding, shape deformation, distance regularized level set
Automated Multi-Stage Segmentation of White Blood Cells Via Optimizing Color Processing
Segmentation of white blood cells (i.e. leukocytes) is a crucial step toward the development of haematological images
analysis of peripheral blood smears due to the complex nature of the different types of white blood cells and their
large variations in shape, texture, color, and density. This study addresses this issue and presents a single fully automatic segmentation framework for both nuclei and cytoplasm of the five classes of leukocytes in a microscope blood smears. The proposed framework integrates a priori information of enhanced nuclei color with Gram-Schmidt orthogonalization, and multi-scale morphological enhancement to localize the nuclei, whereas clustering-based seed extraction and watershed are utilized to segment the cytoplasm. The experimental results on two different datasets show that the proposed method works successfully for both nuclei and cytoplasm segmentation, and achieves more accurate segmentation results compared to the other methods in the literature
Nuclei instance segmentation with dual contour-enhanced adversarial network
The morphology of cancer cells is widely used by pathologists to grade stages of cancers. Accurate cancer cell segmentation is significant to obtain quantitative diagnosis. We proposed a dual contour-enhanced adversarial network to solve this challenge. The dual contour-enhanced masks and adversarial network are incorporated to improve individual cell segmentation capability. By evaluating quantitative individual cell segmentation results on 2017 MICCAI Digital Pathology Challenge, our method achieved best balance between precision and recall rate of individual cell segmentation compared to state-of-the-art cell segmentation methods
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