Intravascular Optical Coherence Tomography Image Segmentation Based on Support Vector Machine Algorithm

Intravascular optical coherence tomography (IVOCT) is becoming more and more popular in clinical diagnosis of coronary atherosclerotic. However, reading IVOCT images is of large amount of work. This article describes a method based on image feature extraction and support vector machine (SVM) to achieve semi-automatic segmentation of IVOCT images. The image features utilized in this work including light attenuation coefficients and image textures based on gray level co-occurrence matrix. Different sets of hyper-parameters and image features were tested. This method achieved an accuracy of 83% on the test images. Single class accuracy of 89% for fibrous, 79.3% for calcification and 86.5% lipid tissue. The results show that this method can be a considerable way for semi-automatic segmentation of atherosclerotic plaque components in clinical IVOCT images.     

Real-Time Automatic Segmentation of Optical Coherence Tomography Volume Data of the Macular Region

Optical coherence tomography (OCT) is a high speed, high resolution and non-invasive imaging modality that enables the capturing of the 3D structure of the retina. The fast and automatic analysis of 3D volume OCT data is crucial taking into account the increased amount of patient-specific 3D imaging data. In this work, we have developed an automatic algorithm, OCTRIMA 3D (OCT Retinal IMage Analysis 3D), that could segment OCT volume data in the macular region fast and accurately. The proposed method is implemented using the shortest-path based graph search, which detects the retinal boundaries by searching the shortest-path between two end nodes using Dijkstra’s algorithm. Additional techniques, such as inter-frame flattening, inter-frame search region refinement, masking and biasing were introduced to exploit the spatial dependency between adjacent frames for the reduction of the processing time. Our segmentation algorithm was evaluated by comparing with the manual labelings and three state of the art graph-based segmentation methods. The processing time for the whole OCT volume of 496×644×51 voxels (captured by Spectralis SD-OCT) was 26.15 seconds which is at least a 2-8-fold increase in speed compared to other, similar reference algorithms used in the comparisons. The average unsigned error was about 1 pixel (∼ 4 microns), which was also lower compared to the reference algorithms. We believe that OCTRIMA 3D is a leap forward towards achieving reliable, real-time analysis of 3D OCT retinal data.     

A Multi-Branch Convolutional Neural Network for Screening and Staging of Diabetic Retinopathy Based on Wide-Field Optical Coherence Tomography Angiography

BackgroundDiabetic retinopathy (DR) is one of the major causes of blindness in adults suffering from diabetes. With the development of wide-field optical coherence tomography angiography (WF-OCTA), it is to become a gold standard for diagnosing DR. The demand for automated DR diagnosis system based on OCTA images have been fostered due to large diabetic population and pervasiveness of retinopathy cases.Materials and methodsIn this study, 288 diabetic patients and 97 healthy people were imaged by the swept-source optical coherence tomography (SS-OCT) with 12 mm × 12 mm single scan centered on the fovea. A multi-branch convolutional neural network (CNN) was proposed to classify WF-OCTA images into four grades: no DR, mild non-proliferative diabetic retinopathy (NPDR), moderate to severe NPDR, and proliferative diabetic retinopathy (PDR).ResultsThe proposed model achieved a classification accuracy of 96.11%, sensitivity of 98.08% and specificity of 89.43% in detecting DR. The accuracy of the model for DR staging is 90.56%, which is higher than that of other mainstream convolution neural network models.ConclusionThis technology enables early diagnosis and objective tracking of disease progression, which may be useful for optimal treatment to reduce vision loss.     

基于光学相干层析成像技术的皮肤组织非消融性光热损伤过程

使用OCT技术观察活体小鼠皮肤组织在非消融性激光作用后光热损伤及其修复过程,得出皮肤组织在此过程中形态卜的变化规律.利用OCT散射模型得出皮肤在激光作用前后光学参数中衰减系数的变化,根据所获得的光学参数的变化量来判断热损伤程度,并分析引起散射参数变化的原因,作为临床上无损的判断光热作用下皮肤内部损伤情况的依据.结果表明OCT是一种很有潜力的活体监测光热损伤及其修复过程的工具.     

Thinned-skull Cortical Window Technique for In Vivo Optical Coherence Tomography Imaging

Optical coherence tomography (OCT) is a biomedical imaging technique with high spatial-temporal resolution. With its minimally invasive approach OCT has been used extensively in ophthalmology, dermatology, and gastroenterology^1-3. Using a thinned-skull cortical window (TSCW), we employ spectral-domain OCT (SD-OCT) modality as a tool to image the cortex in vivo. Commonly, an opened-skull has been used for neuro-imaging as it provides more versatility, however, a TSCW approach is less invasive and is an effective mean for long term imaging in neuropathology studies. Here, we present a method of creating a TSCW in a mouse model for in vivo OCT imaging of the cerebral cortex.     

GPU-Accelerated Framework for Intracoronary Optical Coherence Tomography Imaging at the Push of a Button

Frequency domain optical coherence tomography (FD-OCT) has become one of the important clinical tools for intracoronary imaging to diagnose and monitor coronary artery disease, which has been one of the leading causes of death. To help more accurate diagnosis and monitoring of the disease, many researchers have recently worked on visualization of various coronary microscopic features including stent struts by constructing three-dimensional (3D) volumetric rendering from series of cross-sectional intracoronary FD-OCT images. In this paper, we present the first, to our knowledge, "push-of-a-button" graphics processing unit (GPU)-accelerated framework for intracoronary OCT imaging. Our framework visualizes 3D microstructures of the vessel wall with stent struts from raw binary OCT data acquired by the system digitizer as one seamless process. The framework reports the state-of-the-art performance; from raw OCT data, it takes 4.7 seconds to provide 3D visualization of a 5-cm-long coronary artery (of size 1600 samples x 1024 A-lines x 260 frames) with stent struts and detection of malapposition automatically at the single push of a button.     

光学相干层析成像技术用于裸鼠皮肤霉菌感染研究

利用中心波长为850 nm的宽带光源SLD实现了纵向分辨率为8μm的光学相干层析成像系统。系统采用傅里叶域光学延迟线实现了深度扫描速度为160 mm/s,成像深度为3 mm。获得了裸鼠皮肤霉菌感染部位和健康皮肤的光学相干层析(optical coherence tom ography,OCT)图像,皮肤病变前后的内部结构信息清晰可见。     

位相分辨偏振灵敏光学相干层析术:组织双折射特性的成像与定量分析

我们研制了一种基于光纤的位相分辨偏振灵敏光学相干层析成像系统。该系统中的偏振状态控制设量在参考臂而非光源臂上,因而使得光抵达样品的传输效率大大提高。鉴于光源的部分偏振性,入射于样品上的光含有任意偏振状态的分量,通过对参考光偏振状态的调制,就可相干地提取对应于入射光四种正交偏振状态并经样品后向散射的光信号。基于斯托克斯矢量夹角在无损光纤系统传输的变换不变性,我们能利用测量臂中光信号的斯托克斯参数来确定双折射样品深度分辨的位相延迟信息。利用所研制的偏振灵敏光学相干层析成像系统,不仅确认了韧带和软骨的双折射性质,而且定量分析了不同条件下韧带的双折射变化．研究结果表明：韧带松弛可使其双折射特性明显减弱,而韧带经拉伸后,其双折射特性的变化却不明显。     

OCT于大鼠脱水组织中光散射特性的分析研究

目的:探讨活体组织液体含量的改变对OCT成像的影响,以期提高OCT在诊断组织病理性质方面的能力。方法:实验中复制脱水大鼠病理模型,应用光学相干层析成像设备,进行大鼠舌浅表组织在体显微成像检测,并对图像中组织的信号衰减特性进行量化分析。结果:正常对照组大鼠体重明显增加,病理模型组显著下降,病理模型组于脱水3天和5天后组织的平均OCT信号衰减系数明显高于正常对照组(P<0.01),且5天较3天的病理模型组组织的信号衰减系数变化尤其显著(P<0.01)。结论:改变组织含液量,可显著改变OCT成像效果,且通过对OCT图像中信号的衰减系数分析,可获得组织细微的散射变化,从而有望提高OCT技术在组织性质方面的诊断能力。     

Comparing Artificial Neural Networks,General Linear Models and Support Vector Machines in Building Predictive Models for Small Interfering RNAs

Background

Exogenous short interfering RNAs (siRNAs) induce a gene knockdown effect in cells by interacting with naturally occurring RNA processing machinery. However not all siRNAs induce this effect equally. Several heterogeneous kinds of machine learning techniques and feature sets have been applied to modeling siRNAs and their abilities to induce knockdown. There is some growing agreement to which techniques produce maximally predictive models and yet there is little consensus for methods to compare among predictive models. Also, there are few comparative studies that address what the effect of choosing learning technique, feature set or cross validation approach has on finding and discriminating among predictive models.

Principal Findings

Three learning techniques were used to develop predictive models for effective siRNA sequences including Artificial Neural Networks (ANNs), General Linear Models (GLMs) and Support Vector Machines (SVMs). Five feature mapping methods were also used to generate models of siRNA activities. The 2 factors of learning technique and feature mapping were evaluated by complete 3×5 factorial ANOVA. Overall, both learning techniques and feature mapping contributed significantly to the observed variance in predictive models, but to differing degrees for precision and accuracy as well as across different kinds and levels of model cross-validation.

Conclusions

The methods presented here provide a robust statistical framework to compare among models developed under distinct learning techniques and feature sets for siRNAs. Further comparisons among current or future modeling approaches should apply these or other suitable statistically equivalent methods to critically evaluate the performance of proposed models. ANN and GLM techniques tend to be more sensitive to the inclusion of noisy features, but the SVM technique is more robust under large numbers of features for measures of model precision and accuracy. Features found to result in maximally predictive models are not consistent across learning techniques, suggesting care should be taken in the interpretation of feature relevance. In the models developed here, there are statistically differentiable combinations of learning techniques and feature mapping methods where the SVM technique under a specific combination of features significantly outperforms all the best combinations of features within the ANN and GLM techniques.     

The Correlation Between Texture Features and Fibrous Cap Thickness of Lipid-Rich Atheroma Based on Optical Coherence Tomography Imaging

Fibrous cap thickness (FCT) is seen as critical to plaque vulnerability. Therefore, the development of automatic algorithms for the quantification of FCT is for estimating cardiovascular risk of patients. Intravascular optical coherence tomography (IVOCT) is currently the only in vivo imaging modality with which FCT, the critical component of plaque vulnerability, can be assessed accurately. This study was aimed to discussion the correlation between the texture features of OCT images and the FCT in lipid-rich atheroma. Methods: Firstly, a full automatic segmentation algorithm based on unsupervised fuzzy c means (FCM) clustering with geometric constrains was developed to segment the ROIs of IVOCT images. Then, 32 features, which are associated with the structural and biochemical changes of tissue, were carried out to describe the properties of ROIs. The FCT in grayscale IVOCT images were manually measured by two independent observers. In order to analysis the correlation between IVOCT image features and manual FCT measurements, linear regression approach was performed. Results: Inter-observer agreement of the twice manual FCT measurements was excellent with an intraclass correlation coefficient (ICC) of 0.99. The correlation coefficient between each individual feature set and mean FCT of OCT images were 0.68 for FOS, 0.80 for GLCM, 0.74 for NGTDM, 0.72 for FD, 0.62 for IM and 0.58 for SP. The fusion image features of automatic segmented ROIs and FCT measurements improved the results significantly with a high correlation coefficient (r= 0.91, p<0.001). Conclusion The OCT images features demonstrated the perfect performances and could be used for automatic qualitative analysis and the identification of high-risk plaques instead manual FCT measurements.     

基于OCT技术的鼠皮肤激光热损伤修复过程的监测

采用Er:YAG激光(波长为2 940 nm,能量密度为:2.5 J/cm2单光斑,扫描次数为4)照射活体小白鼠皮肤,利用光学相干层析成像(optical coherence tomography,OCT)技术在活体小鼠上观察其皮肤组织在激光作用之前及作用之后光热损伤修复的整个过程,得到了激光光热作用下引起损伤的皮肤组织在此过程中皮肤光学特性参数的变化情况,发现皮肤修复过程中光学参数有显著差异,并分析了这些差异引起的原因,以揭示激光美容中并发症主要因素。     

维生素C对光化学法诱导皮肤光损伤的光保护作用:OCT定量研究

8-甲氧补骨脂素(8-MOP)联合UVA辐射(即光化学疗法PUVA)因诱导皮肤光损伤的副作用,常被用于实验动物皮肤光损伤模型的构建。为研究维生素C(Vc)对皮肤光损伤的保护效应,本研究在Balb/c小鼠背部皮肤涂抹0.1%8-MOP溶液1h后进行UVA辐照(10 J/cn2)。然后分别涂抹7.5%、15%和30%浓度的Vc溶液的进行光保护治疗,利用光学相干层析成像分析皮肤厚度和光信号衰减的变化,从而评估Vc对皮肤的光保护作用。结果显示,相对于溶媒组,Vc治疗组皮肤厚度减小,光衰减系数增加,其中15%效果尤为明显结果表明,局部涂抹Vc溶液具有一定的光损伤保护效应。     

Study of the Morphological and Functional State of Higher Plant Tissues by Optical Coherence Microscopy and Optical Coherence Tomography

Comparative analysis of two optical methods—optical coherence tomography (OCT) and optical coherence microscopy (OCM)—was made for vital visualization of plant tissues in tomato (Lycopersicon esculentum Mill), spiderwort (Tradescantia pallida (Rose) D. Hunt), orach (Atriplex sp.), and leaves and seeds of medium starwort (Stellaria media L.). The obtained OCT- and OCM-images allowed the morphological and functional state of plant tissues to be assessed in vivo. A higher spatial resolution of the OCM method, as compared to OCT method, allowed plant morphological structures to be identified with greater confidence. The morphological and functional state of tissues can be monitored with a time resolution of 1–4 s in intact plants, without removing them from the habitat.__________Translated from Fiziologiya Rastenii, Vol. 52, No. 4, 2005, pp. 628–634.Original Russian Text Copyright © 2005 by Kutis, Sapozhnikova, Kuranov, Kamenskii.     

A Review on Convolutional Neural Networks for Brain Tumor Segmentation: Methods,Datasets, Libraries,and Future Directions

ObjectivesAccurate and reliable segmentation of brain tumors from MRI images helps in planning an enhanced treatment and increases the life expectancy of patients. However, the manual segmentation of brain tumors is subjective and more prone to errors. Nonetheless, the recent advances in convolutional neural network (CNN)-based methods have exhibited outstanding potential in robust segmentation of brain tumors. This article comprehensively investigates recent advances in CNN-based methods for automatic segmentation of brain tumors from MRI images. It examines popular deep learning (DL) libraries/tools for an expeditious and effortless implementation of CNN models. Furthermore, a critical assessment of current DL architectures is delineated along with the scope of improvement.MethodsIn this work, more than 50 scientific papers from 2014-2020 are selected using Google Scholar and PubMed. Also, the leading journals related to our work along with proceedings from major conferences such as MICCAI, MIUA and ECCV are retrieved. This research investigated various annual challenges too related to this work including Multimodal Brain Tumor Segmentation Challenge (MICCAI BRATS) and Ischemic Stroke Lesion Segmentation Challenge (ISLES).ResultAfter a systematic literature search pertinent to the theme, we found that principally there exist three variations of CNN architecture for brain tumor segmentation: single-path and multi-path, fully convolutional, and cascaded CNNs. The respective performances of most automated methods based on CNN are appraised on the BraTS dataset, provided as a part of the MICCAI Multimodal Brain Tumor Segmentation challenge held annually since 2012.ConclusionNotwithstanding the remarkable potential of CNN-based methods, reliable and robust segmentation of brain tumors continues to be an intractable challenge. This is due to the intricate anatomy of the brain, variability in its appearance, and imperfection in image acquisition. Moreover, owing to the small size of MRI datasets, CNN-based methods cannot operate with their full capacity, as demonstrated with large scale datasets, such as ImageNet.     

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.     

金纳米棒-聚苯胺核壳纳米材料的光学相干层析成像能力研究

对比试剂的使用能够显著提升光学相干层析(OCT)的成像效果。聚苯胺(PANI)是一种有机导电聚合物,在近红外(NIR)区有着很强的光吸收。本文采用PANI对常见的OCT成像对比试剂--金纳米棒(GNRs)进行修饰,合成了PANI/GNRs核壳粒子,并对其OCT成像对比能力进行了研究。PANI/GNRs展现出良好的NIR光吸收特性;同时,PANI对GNRs的包裹也显著提升了金纳米结构的稳定性、降低了GNRs原有的毒性。选用离体猪肝组织作为检测样本,发现纳米材料使用能够显著提升OCT的成像效果。与未修饰的GNRs及PANI粒子相比,PANI/GNRs的OCT成像对比效果明显更好。因此,PANI包裹的GNRs核壳纳米材料有望成为一种低毒性且效果良好的OCT对比试剂用于生物组织成像。     

Deep Convolution Neural Network Based System for Early Diagnosis of Alzheimer's Disease

ObjectivesAlzheimer's Disease (AD) is the most general type of dementia. In all leading countries, it is one of the primary reasons of death in senior citizens. Currently, it is diagnosed by calculating the MSME score and by the manual study of MRI Scan. Also, different machine learning methods are utilized for automatic diagnosis but existing has some limitations in terms of accuracy. So, main objective of this paper to include a preprocessing method before CNN model to increase the accuracy of classification.Materials and methodIn this paper, we present a deep learning-based approach for detection of Alzheimer's Disease from ADNI database of Alzheimer's disease patients, the dataset contains fMRI and PET images of Alzheimer's patients along with normal person's image. We have applied 3D to 2D conversion and resizing of images before applying VGG-16 architecture of Convolution neural network for feature extraction. Finally, for classification SVM, Linear Discriminate, K means clustering, and Decision tree classifiers are used.ResultsThe experimental result shows that the average accuracy of 99.95% is achieved for the classification of the fMRI dataset, while the average accuracy of 73.46% is achieved with the PET dataset. On comparing results on the basis of accuracy, specificity, sensitivity and on some other parameters we found that these results are better than existing methods.Conclusionsthis paper, suggested a unique way to increase the performance of CNN models by applying some preprocessing on image dataset before sending to CNN architecture for feature extraction. We applied this method on ADNI database and on comparing the accuracies with other similar approaches it shows better results.     

用于全眼段成像和视轴参数测量的四焦点开关光学相干断层扫描

本文演示了四焦点开关傅里叶域光学相干断层扫描系统用于全眼段成像和体内视轴参数(VAP)测量的可行性.使用包括不同厚度平行玻璃板的两个同步转盘来将探测光束的焦点位置从角膜以及晶状体的前部和后部切换到视网膜.该过程同时增加了参考光束的深度范围.这种多级聚焦的方法可以使探测光束完全聚焦在人眼的每个部分.初步实验表明,该方法可...     

Measurement of Myopia and Normal Human Choroidal Thickness Using Spectral Domain Optical Coherence Tomography

Myopia is a common ophthalmic deficiency. The structure and function of choroid layer is assumed to be associated with myopia. In this study, a laboratory developed spectral domain optical coherence tomography scanning system is used to image human eyes. The axial resolution of the system is about 7 μm, and the acquisition rate is 100 kHz. Firstly, a cross-sectional image was acquired by averaging 100 images from imaging posterior segment of each eye. The choroid thickness was measured by 11 discrete points. The average thickness of normal human eyes was (0.296 ± 0.126) mm, whereas the average choroid thickness of myopic eyes was (0.220 ± 0.095) mm. Afterwards, the T test is used to calculate the data statistically. The analysis of the final result is based on the average thickness measured and the thickness of each measuring point. There was a significant difference in choroid thickness between myopia and normal eyes (P value < 0.01), which indicates that the choroid thickness of myopia was significantly thinner than that of normal eyes. Besides, there are findings that the choroidal thickness in nasal side is thinner than that in the fovea and temporal side in each eye. The choroidal thickness on temporal side in myopia eye has the most significant difference comparing with that in normal eye. The comprehensive evaluation of myopia and normal choroidal thickness using spectral domain optical coherence tomography may provide an important reference for the development of medical methods for diagnosis and treatment of myopia.