Fundus Retinal Vessels Image Segmentation Method Based on Improved U-Net

ObjectivesAlthough the segmentation of retinal vessels in the fundus is of great significance for screening and diagnosing retinal vascular diseases, it remains difficult to detect the low contrast and the information around the lesions provided by retinal vessels in the fundus and to locate and segment micro-vessels in the fine-grained area. To overcome this problem, we propose herein an improved U-Net segmentation method NoL-UNet.Material and methodsThis work introduces NoL-UNet. First of all, the ordinary convolution block of the U-Net network is changed to random dropout convolution blocks, which can better extract the relevant features of the image and effectively alleviate the network overfitting. Next, a NoL-Block attention mechanism added to the bottom of the encoding-decoding structure expands the receptive field and enhances the correlation of pixel information without increasing the number of parameters.ResultsThe proposed method is verified by applying it to the fundus image datasets DRIVE, CHASE_DB1, and HRF. The AUC for DRIVE, CHASE_DB1 and HRF is 0.9861, 0.9891 and 0.9893, Se for DRIVE, CHASE_DB1 and HRF is 0.8489, 0.8809 and 0.8476, and the Acc for DRIVE, CHASE_DB1 and HRF is 0.9697, 0.9826 and 0.9732, respectively. The total number of parameters is 1.70M, and for DRIVE, it takes 0.050s to segment an image.ConclusionOur method is statistically significantly different from the U-Net method, and the improved method shows superior performance with better accuracy and robustness of the model, which has good practical application in auxiliary diagnosis.     

A New Approach to Segment Both Main and Peripheral Retinal Vessels Based on Gray-Voting and Gaussian Mixture Model

Vessel segmentation in retinal fundus images is a preliminary step to clinical diagnosis for some systemic diseases and some eye diseases. The performances of existing methods for segmenting small vessels which are usually of more importance than the main vessels in a clinical diagnosis are not satisfactory in clinical use. In this paper, we present a method for both main and peripheral vessel segmentation. A local gray-level change enhancement algorithm called gray-voting is used to enhance the small vessels, while a two-dimensional Gabor wavelet is used to extract the main vessels. We fuse the gray-voting results with the 2D-Gabor filter results as pre-processing outcome. A Gaussian mixture model is then used to extract vessel clusters from the pre-processing outcome, while small vessels fragments are obtained using another gray-voting process, which complements the vessel cluster extraction already performed. At the last step, we eliminate the fragments that do not belong to the vessels based on the shape of the fragments. We evaluated the approach with two publicly available DRIVE (Staal et al., 2004) and STARE (Hoover et at., 2000) datasets with manually segmented results. For the STARE dataset, when using the second manually segmented results which include much more small vessels than the first manually segmented results as the “gold standard,” this approach achieved an average sensitivity, accuracy and specificity of 65.0%, 92.1% and 97.0%, respectively. The sensitivities of this approach were much higher than those of the other existing methods, with comparable specificities; these results thus demonstrated that this approach was sensitive to detection of small vessels.     

A Framework for White Blood Cell Segmentation in Microscopic Blood Images Using Digital Image Processing

Evaluation of blood smear is a commonly clinical test these days. Most of the time, the hematologists are interested on white blood cells (WBCs) only. Digital image processing techniques can help them in their analysis and diagnosis. For example, disease like acute leukemia is detected based on the amount and condition of the WBC. The main objective of this paper is to segment the WBC to its two dominant elements: nucleus and cytoplasm. The segmentation is conducted using a proposed segmentation framework that consists of an integration of several digital image processing algorithms. Twenty microscopic blood images were tested, and the proposed framework managed to obtain 92% accuracy for nucleus segmentation and 78% for cytoplasm segmentation. The results indicate that the proposed framework is able to extract the nucleus and cytoplasm region in a WBC image sample.     

兔先天性青光眼网络膜血管改变

目的　研究青光眼对视网膜脉络膜血液循环的影响。方法　选24月龄、体重3.5～4kg的先天性青光眼大耳白兔5只(7只眼),选10只同龄大耳白兔作为对照组。另选10只2月龄、体重2kg大耳白兔前房内灌注生理盐水制成急性高眼压模型。对三组兔进行眼底照像、闪光视诱发电位(FVEP)检查,观察视网膜脉络膜血管形态和FVEP的变化。对人工急性高眼压组还进行了闪光视网膜电流图(FERG)检查。结果　先天性青光眼组与同龄对照组相比视网膜脉络膜末梢血管网明显减少;人工急性高眼压组眼压升高后首先使视网膜脉络膜末梢血管网灌流不足,随着眼压的继续升高脉络膜大血管变细,末梢血管网灌流不足加重,眼压极度升高时脉络膜大血管血流中断。同龄正常对照组的FVEP的主波P100潜伏期是(83±9)ms,先天性青光眼组则为(112±14)ms,差异有非常显著意义(P＜0.01);人工急性高眼压组高眼压前为(69±5)ms,眼压60～80mm　Hg时延长为(81±7)ms,眼压在100～130mmHg时FVEP波形低平,近似直线;眼压恢复正常后2hFVEP的P100潜伏期为(82±8)ms。人工急性高眼压前后FERG变化显著。结论　青光眼可以影响视网膜脉络膜血液循环;可使FVEP、FERG发生变化。     

Retinal Vessel Segmentation: An Efficient Graph Cut Approach with Retinex and Local Phase

Our application concerns the automated detection of vessels in retinal images to improve understanding of the disease mechanism, diagnosis and treatment of retinal and a number of systemic diseases. We propose a new framework for segmenting retinal vasculatures with much improved accuracy and efficiency. The proposed framework consists of three technical components: Retinex-based image inhomogeneity correction, local phase-based vessel enhancement and graph cut-based active contour segmentation. These procedures are applied in the following order. Underpinned by the Retinex theory, the inhomogeneity correction step aims to address challenges presented by the image intensity inhomogeneities, and the relatively low contrast of thin vessels compared to the background. The local phase enhancement technique is employed to enhance vessels for its superiority in preserving the vessel edges. The graph cut-based active contour method is used for its efficiency and effectiveness in segmenting the vessels from the enhanced images using the local phase filter. We have demonstrated its performance by applying it to four public retinal image datasets (3 datasets of color fundus photography and 1 of fluorescein angiography). Statistical analysis demonstrates that each component of the framework can provide the level of performance expected. The proposed framework is compared with widely used unsupervised and supervised methods, showing that the overall framework outperforms its competitors. For example, the achieved sensitivity (0:744), specificity (0:978) and accuracy (0:953) for the DRIVE dataset are very close to those of the manual annotations obtained by the second observer.     

漫反射光谱测量皮窗模型中血管的血氧饱和度

血管中的血氧饱和度(Oxygen saturation,St O2)作为影响血管靶向光动力疗法(Vascular targeted photodynamic therapy,V-PDT)疗效的关键要素之一,实验测量了活体裸鼠背皮窗模型中血管的漫反射光谱(450-800nm),并通过拟合漫反射光谱数据定量获得了血管中的St O2。同时,研究了高氧、低氧和常氧等三种不同氧条件下V-PDT中血管的St O2和血管管径的变化情况。结果表明,高氧和常氧条件下的平均St O2和血管收缩较为显著,但低氧组的平均St O2和血管收缩不明显。在相同氧条件和不同光照功率密度条件下,V-PDT前后靶向血管的平均St O2与血管管径的变化之间没有显著相关性,但V-PDT前后平均St O2的变化量与光照功率密度之间呈正相关。     

CNV-CH: A Convex Hull Based Segmentation Approach to Detect Copy Number Variations (CNV) Using Next-Generation Sequencing Data

Copy number variation (CNV) is a form of structural alteration in the mammalian DNA sequence, which are associated with many complex neurological diseases as well as cancer. The development of next generation sequencing (NGS) technology provides us a new dimension towards detection of genomic locations with copy number variations. Here we develop an algorithm for detecting CNVs, which is based on depth of coverage data generated by NGS technology. In this work, we have used a novel way to represent the read count data as a two dimensional geometrical point. A key aspect of detecting the regions with CNVs, is to devise a proper segmentation algorithm that will distinguish the genomic locations having a significant difference in read count data. We have designed a new segmentation approach in this context, using convex hull algorithm on the geometrical representation of read count data. To our knowledge, most algorithms have used a single distribution model of read count data, but here in our approach, we have considered the read count data to follow two different distribution models independently, which adds to the robustness of detection of CNVs. In addition, our algorithm calls CNVs based on the multiple sample analysis approach resulting in a low false discovery rate with high precision.     

Segmentation of Uterus Using Laparoscopic Ultrasound by an Image-Based Active Contour Approach for Guiding Gynecological Diagnosis and Surgery

In laparoscopic gynecologic surgery, ultrasound has been typically implemented to diagnose urological and gynecological conditions. We applied laparoscopic ultrasonography (using Esaote 7.5~10MHz laparoscopic transducer) on the retrospective analyses of 42 women subjects during laparoscopic extirpation and excision of gynecological tumors in our hospital from August 2011 to August 2013. The objective of our research is to develop robust segmentation technique for isolation and identification of the uterus from the ultrasound images, so as to assess, locate and guide in removing the lesions during laparoscopic operations. Our method enables segmentation of the uterus by the active contour algorithm. We evaluated 42 in-vivo laparoscopic images acquired from the 42 patients (age 39.1 ± 7.2 years old) and selected images pertaining to 4 cases of congenital uterine malformations and 2 cases of pelvic adhesions masses. These cases (n = 6) were used for our uterus segmentation experiments. Based on them, the active contour method was compared with the manual segmentation method by a medical expert using linear regression and the Bland-Altman analysis (used to measure the correlation and the agreement). Then, the Dice and Jaccard indices are computed for measuring the similarity of uterus segmented between computational and manual methods. Good correlation was achieved whereby 84%–92% results fall within the 95% confidence interval in the Student t-test) and we demonstrate that the proposed segmentation method of uterus using laparoscopic images is effective.     

Evaluation of Non-Local Means Based Denoising Filters for Diffusion Kurtosis Imaging Using a New Phantom

Image denoising has a profound impact on the precision of estimated parameters in diffusion kurtosis imaging (DKI). This work first proposes an approach to constructing a DKI phantom that can be used to evaluate the performance of denoising algorithms in regard to their abilities of improving the reliability of DKI parameter estimation. The phantom was constructed from a real DKI dataset of a human brain, and the pipeline used to construct the phantom consists of diffusion-weighted (DW) image filtering, diffusion and kurtosis tensor regularization, and DW image reconstruction. The phantom preserves the image structure while minimizing image noise, and thus can be used as ground truth in the evaluation. Second, we used the phantom to evaluate three representative algorithms of non-local means (NLM). Results showed that one scheme of vector-based NLM, which uses DWI data with redundant information acquired at different b-values, produced the most reliable estimation of DKI parameters in terms of Mean Square Error (MSE), Bias and standard deviation (Std). The result of the comparison based on the phantom was consistent with those based on real datasets.     

Segmentation and Estimation for SNP Microarrays: A Bayesian Multiple Change‐Point Approach

Summary High‐density single‐nucleotide polymorphism (SNP) microarrays provide a useful tool for the detection of copy number variants (CNVs). The analysis of such large amounts of data is complicated, especially with regard to determining where copy numbers change and their corresponding values. In this article, we propose a Bayesian multiple change‐point model (BMCP) for segmentation and estimation of SNP microarray data. Segmentation concerns separating a chromosome into regions of equal copy number differences between the sample of interest and some reference, and involves the detection of locations of copy number difference changes. Estimation concerns determining true copy number for each segment. Our approach not only gives posterior estimates for the parameters of interest, namely locations for copy number difference changes and true copy number estimates, but also useful confidence measures. In addition, our algorithm can segment multiple samples simultaneously, and infer both common and rare CNVs across individuals. Finally, for studies of CNVs in tumors, we incorporate an adjustment factor for signal attenuation due to tumor heterogeneity or normal contamination that can improve copy number estimates.     

A Hybrid CNN-SVM Threshold Segmentation Approach for Tumor Detection and Classification of MRI Brain Images

ObjectiveIn this research paper, the brain MRI images are going to classify by considering the excellence of CNN on a public dataset to classify Benign and Malignant tumors.Materials and MethodsDeep learning (DL) methods due to good performance in the last few years have become more popular for Image classification. Convolution Neural Network (CNN), with several methods, can extract features without using handcrafted models, and eventually, show better accuracy of classification. The proposed hybrid model combined CNN and support vector machine (SVM) in terms of classification and with threshold-based segmentation in terms of detection.ResultThe findings of previous studies are based on different models with their accuracy as Rough Extreme Learning Machine (RELM)-94.233%, Deep CNN (DCNN)-95%, Deep Neural Network (DNN) and Discrete Wavelet Autoencoder (DWA)-96%, k-nearest neighbors (kNN)-96.6%, CNN-97.5%. The overall accuracy of the hybrid CNN-SVM is obtained as 98.4959%.ConclusionIn today's world, brain cancer is one of the most dangerous diseases with the highest death rate, detection and classification of brain tumors due to abnormal growth of cells, shapes, orientation, and the location is a challengeable task in medical imaging. Magnetic resonance imaging (MRI) is a typical method of medical imaging for brain tumor analysis. Conventional machine learning (ML) techniques categorize brain cancer based on some handicraft property with the radiologist specialist choice. That can lead to failure in the execution and also decrease the effectiveness of an Algorithm. With a brief look came to know that the proposed hybrid model provides more effective and improvement techniques for classification.     

A Confidence Region Approach for Assessing Equivalence in Variability of Bioavailability

The problem for assessment of equivalence in variability of bioavailability between two drug products is considered. Similar to the method for assessing bioequivalence in average bioavailability proposed by Chow and Shao (1990), an exact confidence region approach is derived when the intersubject variance is known. When the intersubject variance is unknown, a large sample approximation is considered. The proposed method for assessing equivalence of variability of bioavailability appears to be asymptotically uncorrelated with that of Chow and Shao (1990) for average bioavailability. As a result, the proposed method in conjunction with the method proposed by Chow and Shao (1990) constitutes a confidence region approach for assessing population bioequivalence. An example concerning a bioequivalence trial with 24 healthy volunteers is provided to illustrate the proposed method.     

Design of Epitope Based Vaccine for Dengue Virus Using Immunoinformatic Approach

International Journal of Peptide Research and Therapeutics - Dengue is an infectious disease caused by one of the four serotypes of the dengue virus. It is a mosquito borne disease and the female...     

A Combinational Clustering Based Method for cDNA Microarray Image Segmentation

Microarray technology plays an important role in drawing useful biological conclusions by analyzing thousands of gene expressions simultaneously. Especially, image analysis is a key step in microarray analysis and its accuracy strongly depends on segmentation. The pioneering works of clustering based segmentation have shown that k-means clustering algorithm and moving k-means clustering algorithm are two commonly used methods in microarray image processing. However, they usually face unsatisfactory results because the real microarray image contains noise, artifacts and spots that vary in size, shape and contrast. To improve the segmentation accuracy, in this article we present a combination clustering based segmentation approach that may be more reliable and able to segment spots automatically. First, this new method starts with a very simple but effective contrast enhancement operation to improve the image quality. Then, an automatic gridding based on the maximum between-class variance is applied to separate the spots into independent areas. Next, among each spot region, the moving k-means clustering is first conducted to separate the spot from background and then the k-means clustering algorithms are combined for those spots failing to obtain the entire boundary. Finally, a refinement step is used to replace the false segmentation and the inseparable ones of missing spots. In addition, quantitative comparisons between the improved method and the other four segmentation algorithms--edge detection, thresholding, k-means clustering and moving k-means clustering--are carried out on cDNA microarray images from six different data sets. Experiments on six different data sets, 1) Stanford Microarray Database (SMD), 2) Gene Expression Omnibus (GEO), 3) Baylor College of Medicine (BCM), 4) Swiss Institute of Bioinformatics (SIB), 5) Joe DeRisi’s individual tiff files (DeRisi), and 6) University of California, San Francisco (UCSF), indicate that the improved approach is more robust and sensitive to weak spots. More importantly, it can obtain higher segmentation accuracy in the presence of noise, artifacts and weakly expressed spots compared with the other four methods.     

Cerebral Blood Volume Analysis in Glioblastomas Using Dynamic Susceptibility Contrast-Enhanced Perfusion MRI: A Comparison of Manual and Semiautomatic Segmentation Methods

Purpose

To compare the reproducibilities of manual and semiautomatic segmentation method for the measurement of normalized cerebral blood volume (nCBV) using dynamic susceptibility contrast-enhanced (DSC) perfusion MR imaging in glioblastomas.

Materials and Methods

Twenty-two patients (11 male, 11 female; 27 tumors) with histologically confirmed glioblastoma (WHO grade IV) were examined with conventional MR imaging and DSC imaging at 3T before surgery or biopsy. Then nCBV (means and standard deviations) in each mass was measured using two DSC MR perfusion analysis methods including manual and semiautomatic segmentation method, in which contrast-enhanced (CE)-T1WI and T2WI were used as structural imaging. Intraobserver and interobserver reproducibility were assessed according to each perfusion analysis method or each structural imaging. Interclass correlation coefficient (ICC), Bland-Altman plot, and coefficient of variation (CV) were used to evaluate reproducibility.

Results

Intraobserver reproducibilities on CE-T1WI and T2WI were ICC of 0.74–0.89 and CV of 20.39–36.83% in manual segmentation method, and ICC of 0.95–0.99 and CV of 8.53–16.19% in semiautomatic segmentation method, repectively. Interobserver reproducibilites on CE-T1WI and T2WI were ICC of 0.86–0.94 and CV of 19.67–35.15% in manual segmentation method, and ICC of 0.74–1.0 and CV of 5.48–49.38% in semiautomatic segmentation method, respectively. Bland-Altman plots showed a good correlation with ICC or CV in each method. The semiautomatic segmentation method showed higher intraobserver and interobserver reproducibilities at CE-T1WI-based study than other methods.

Conclusion

The best reproducibility was found using the semiautomatic segmentation method based on CE-T1WI for structural imaging in the measurement of the nCBV of glioblastomas.     

A Novel Region-Growing Based Semi-Automatic Segmentation Protocol for Three-Dimensional Condylar Reconstruction Using Cone Beam Computed Tomography (CBCT)

Objective

To present and validate a semi-automatic segmentation protocol to enable an accurate 3D reconstruction of the mandibular condyles using cone beam computed tomography (CBCT).

Materials and Methods

Approval from the regional medical ethics review board was obtained for this study. Bilateral mandibular condyles in ten CBCT datasets of patients were segmented using the currently proposed semi-automatic segmentation protocol. This segmentation protocol combined 3D region-growing and local thresholding algorithms. The segmentation of a total of twenty condyles was performed by two observers. The Dice-coefficient and distance map calculations were used to evaluate the accuracy and reproducibility of the segmented and 3D rendered condyles.

Results

The mean inter-observer Dice-coefficient was 0.98 (range [0.95–0.99]). An average 90^th percentile distance of 0.32 mm was found, indicating an excellent inter-observer similarity of the segmented and 3D rendered condyles. No systematic errors were observed in the currently proposed segmentation protocol.

Conclusion

The novel semi-automated segmentation protocol is an accurate and reproducible tool to segment and render condyles in 3D. The implementation of this protocol in the clinical practice allows the CBCT to be used as an imaging modality for the quantitative analysis of condylar morphology.     

A New Distribution-Free Approach to Constructing the Confidence Region for Multiple Parameters

Construction of confidence intervals or regions is an important part of statistical inference. The usual approach to constructing a confidence interval for a single parameter or confidence region for two or more parameters requires that the distribution of estimated parameters is known or can be assumed. In reality, the sampling distributions of parameters of biological importance are often unknown or difficult to be characterized. Distribution-free nonparametric resampling methods such as bootstrapping and permutation have been widely used to construct the confidence interval for a single parameter. There are also several parametric (ellipse) and nonparametric (convex hull peeling, bagplot and HPDregionplot) methods available for constructing confidence regions for two or more parameters. However, these methods have some key deficiencies including biased estimation of the true coverage rate, failure to account for the shape of the distribution inherent in the data and difficulty to implement. The purpose of this paper is to develop a new distribution-free method for constructing the confidence region that is based only on a few basic geometrical principles and accounts for the actual shape of the distribution inherent in the real data. The new method is implemented in an R package, distfree.cr/R. The statistical properties of the new method are evaluated and compared with those of the other methods through Monte Carlo simulation. Our new method outperforms the other methods regardless of whether the samples are taken from normal or non-normal bivariate distributions. In addition, the superiority of our method is consistent across different sample sizes and different levels of correlation between the two variables. We also analyze three biological data sets to illustrate the use of our new method for genomics and other biological researches.