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Directional analysis of intensity changes for determining the existence of cyst in optical coherence tomography images.
Monemian, M, Rabbani, H
Scientific reports. 2022;(1):2105
Abstract
Diabetic retinopathy (DR) is an important cause of blindness in people with the long history of diabetes. DR is caused due to the damage to blood vessels in the retina. One of the most important manifestations of DR is the formation of fluid-filled regions between retinal layers. The evaluation of stage and transcribed drugs can be possible through the analysis of retinal Optical Coherence Tomography (OCT) images. Therefore, the detection of cysts in OCT images and the is of considerable importance. In this paper, a fast method is proposed to determine the status of OCT images as cystic or non-cystic. The method consists of three phases which are pre-processing, boundary pixel determination and post-processing. After applying a noise reduction method in the pre-processing step, the method finds the pixels which are the boundary pixels of cysts. This process is performed by finding the significant intensity changes in the vertical direction and considering rectangular patches around the candidate pixels. The patches are verified whether or not they contain enough pixels making considerable diagonal intensity changes. Then, a shadow omission method is proposed in the post-processing phase to extract the shadow regions which can be mistakenly considered as cystic areas. Then, the pixels extracted in the previous phase that are near the shadow regions are removed to prevent the production of false positive cases. The performance of the proposed method is evaluated in terms of sensitivity and specificity on real datasets. The experimental results show that the proposed method produces outstanding results from both accuracy and speed points of view.
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Image-derived mean velocity measurement for prediction of coronary flow reserve in a canonical stenosis phantom using magnetic particle imaging.
Siepmann, R, Nilius, H, Mueller, F, Mueller, K, Luisi, C, Dadfar, SM, Straub, M, Schulz, V, Reinartz, SD
PloS one. 2021;(4):e0249697
Abstract
INTRODUCTION Aim of this study is to evaluate whether magnetic particle imaging (MPI) is capable of measuring velocities occurring in the coronary arteries and to compute coronary flow reserve (CFR) in a canonical phantom as a preliminary study. METHODS For basic velocity measurements, a circulation phantom was designed containing replaceable glass tubes with three varying inner diameters, matching coronary-vessel diameters. Standardised boluses of superparamagnetic-iron-oxide-nanoparticles were injected and visualised by MPI. Two image-based techniques were competitively applied to calibrate the respective glass tube and to compute the mean velocity: full-duration-at-half-maximum (FDHM) and tracer dilution (TD) method. For CFR-calculation, four necessary settings of the circulation model of a virtual vessel with an inner diameter of 4 mm were generated using differently sized glass tubes and a stenosis model. The respective velocities in stenotic glass tubes were computed without recalibration. RESULTS On velocity level, comparison showed a good agreement (rFDHM = 0.869, rTD = 0.796) between techniques, preferably better for 4 mm and 6 mm inner diameter glass tubes. On CFR level MPI-derived CFR-prediction performed considerably inferior with a relative error of 20-44%. CONCLUSIONS MPI has the ability to reliably measure coronary blood velocities at rest as well as under hyperaemia and therefore may be suitable for CFR calculation. Calibration-associated accuracy of CFR-measurements has to be improved substantially in further studies.
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An optimized segmentation and quantification approach in microvascular imaging for OCTA-based neovascular regression monitoring.
Wu, S, Wu, S, Feng, H, Hu, Z, Xie, Y, Su, Y, Feng, T, Li, L
BMC medical imaging. 2021;(1):13
Abstract
BACKGROUND Quantification of neovascularization changes in terms of neovascular complex (NVC) acquired from the optical coherence tomography angiography (OCTA) imaging is extremely important for diagnosis and treatment monitoring of proliferative diabetic retinopathy (PDR). However, only few vessel extraction methods have so far been reported to quantify neovascular changes in NVC with proliferative diabetic retinopathy PDR based on OCTA images. METHODS Here we propose an optimized approach to segment blood vessels, which is based on an improved vascular connectivity analysis (VCA) algorithm and combined with morphological characterization and elimination of noise and artifacts. The length and width of vessels are obtained in the quantitative assessment of microvascular network. The feasibility of the proposed method is further studied by a treatment monitoring and statistical analysis process, as we have monitored and statistically analyzed the changes of NVC based on sampled OCTA images of PDR patients (N = 14) after treatment by intravitreal injection of conbercept. RESULTS The proposed method has demonstrated better performance in accuracy compared with existing algorithms and can thus be used for PRD treatment monitoring. Following the PDR treatment monitoring study, our data has shown that from the 1st day to 7th day of treatment, the averaged (arithmetic mean) length of NVC has been substantially shortened by 36.8% (P < 0.01), indicating significant effects of treatment. Meanwhile, the averaged (arithmetic mean) width of NVC from the 1st day to 7th day of treatment has been increased by 10.2% (P < 0.05), indicating that most of the narrow neovascularization has been reduced. CONCLUSION The results and analysis have confirmed that the proposed optimization process by the improved VCA method is both effective and feasible to segment and quantify the NVC with lower noise and fewer artifacts. Thus, it can be potentially applied to monitor the fibrovascular regression during the treatment period. Clinical Trial Registration This trial is registered with the Chinese Clinical Trial Registry (Registered 27 December 2017, http://www.chictr.org.cn , registration number ChiCTR-IPR-17014160).
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MHANet: A hybrid attention mechanism for retinal diseases classification.
Xu, L, Wang, L, Cheng, S, Li, Y
PloS one. 2021;(12):e0261285
Abstract
With the increase of patients with retinopathy, retinopathy recognition has become a research hotspot. In this article, we describe the etiology and symptoms of three kinds of retinal diseases, including drusen(DRUSEN), choroidal neovascularization(CNV) and diabetic macular edema(DME). In addition, we also propose a hybrid attention mechanism to classify and recognize different types of retinopathy images. In particular, the hybrid attention mechanism proposed in this paper includes parallel spatial attention mechanism and channel attention mechanism. It can extract the key features in the channel dimension and spatial dimension of retinopathy images, and reduce the negative impact of background information on classification results. The experimental results show that the hybrid attention mechanism proposed in this paper can better assist the network to focus on extracting thr fetures of the retinopathy area and enhance the adaptability to the differences of different data sets. Finally, the hybrid attention mechanism achieved 96.5% and 99.76% classification accuracy on two public OCT data sets of retinopathy, respectively.
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Raman Cryomicroscopic Imaging and Sample Holder for Spectroscopic Subzero Temperature Measurements.
Yu, G, Li, R, Hubel, A
Methods in molecular biology (Clifton, N.J.). 2021;:351-361
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Abstract
Raman spectroscopy has been gaining in popularity for noninvasive analysis of single cells. Raman spectra and images deliver meaningful information regarding the biochemical, biophysical, and structural properties of cells in various states. Low-temperature Raman spectroscopy has been applied to verify the presence of ice inside a frozen cell and to illustrate the distribution of both penetrating and non-penetrating cryoprotectants. This chapter delineates Raman cryomicroscopic imaging of single cells as well as sample handling for spectroscopic measurements at subzero temperature. The experimental setup is depicted with a special emphasis on a custom-built temperature-controlled cooling stage. The use of Raman cryomicroscopic imaging is demonstrated using Jurkat cells cryopreserved in a sucrose solution. Moreover, strategies for determining intracellular ice formation (IIF) and analysis of sucrose partitioning across the cell membrane are presented.
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Validation of a Deep Learning System for the Full Automation of Bite and Meal Duration Analysis of Experimental Meal Videos.
Konstantinidis, D, Dimitropoulos, K, Langlet, B, Daras, P, Ioakimidis, I
Nutrients. 2020;(1)
Abstract
Eating behavior can have an important effect on, and be correlated with, obesity and eating disorders. Eating behavior is usually estimated through self-reporting measures, despite their limitations in reliability, based on ease of collection and analysis. A better and widely used alternative is the objective analysis of eating during meals based on human annotations of in-meal behavioral events (e.g., bites). However, this methodology is time-consuming and often affected by human error, limiting its scalability and cost-effectiveness for large-scale research. To remedy the latter, a novel "Rapid Automatic Bite Detection" (RABiD) algorithm that extracts and processes skeletal features from videos was trained in a video meal dataset (59 individuals; 85 meals; three different foods) to automatically measure meal duration and bites. In these settings, RABiD achieved near perfect agreement between algorithmic and human annotations (Cohen's kappa κ = 0.894; F1-score: 0.948). Moreover, RABiD was used to analyze an independent eating behavior experiment (18 female participants; 45 meals; three different foods) and results showed excellent correlation between algorithmic and human annotations. The analyses revealed that, despite the changes in food (hash vs. meatballs), the total meal duration remained the same, while the number of bites were significantly reduced. Finally, a descriptive meal-progress analysis revealed that different types of food affect bite frequency, although overall bite patterns remain similar (the outcomes were the same for RABiD and manual). Subjects took bites more frequently at the beginning and the end of meals but were slower in-between. On a methodological level, RABiD offers a valid, fully automatic alternative to human meal-video annotations for the experimental analysis of human eating behavior, at a fraction of the cost and the required time, without any loss of information and data fidelity.
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Applications of radiomics and machine learning for radiotherapy of malignant brain tumors.
Kocher, M, Ruge, MI, Galldiks, N, Lohmann, P
Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al]. 2020;(10):856-867
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Abstract
BACKGROUND Magnetic resonance imaging (MRI) and amino acid positron-emission tomography (PET) of the brain contain a vast amount of structural and functional information that can be analyzed by machine learning algorithms and radiomics for the use of radiotherapy in patients with malignant brain tumors. METHODS This study is based on comprehensive literature research on machine learning and radiomics analyses in neuroimaging and their potential application for radiotherapy in patients with malignant glioma or brain metastases. RESULTS Feature-based radiomics and deep learning-based machine learning methods can be used to improve brain tumor diagnostics and automate various steps of radiotherapy planning. In glioma patients, important applications are the determination of WHO grade and molecular markers for integrated diagnosis in patients not eligible for biopsy or resection, automatic image segmentation for target volume planning, prediction of the location of tumor recurrence, and differentiation of pseudoprogression from actual tumor progression. In patients with brain metastases, radiomics is applied for additional detection of smaller brain metastases, accurate segmentation of multiple larger metastases, prediction of local response after radiosurgery, and differentiation of radiation injury from local brain metastasis relapse. Importantly, high diagnostic accuracies of 80-90% can be achieved by most approaches, despite a large variety in terms of applied imaging techniques and computational methods. CONCLUSION Clinical application of automated image analyses based on radiomics and artificial intelligence has a great potential for improving radiotherapy in patients with malignant brain tumors. However, a common problem associated with these techniques is the large variability and the lack of standardization of the methods applied.
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Multiclass retinal disease classification and lesion segmentation in OCT B-scan images using cascaded convolutional networks.
Zhong, P, Wang, J, Guo, Y, Fu, X, Wang, R
Applied optics. 2020;(33):10312-10320
Abstract
Disease classification and lesion segmentation of retinal optical coherence tomography images play important roles in ophthalmic computer-aided diagnosis. However, existing methods achieve the two tasks separately, which is insufficient for clinical application and ignores the internal relation of disease and lesion features. In this paper, a framework of cascaded convolutional networks is proposed to jointly classify retinal diseases and segment lesions. First, we adopt an auxiliary binary classification network to identify normal and abnormal images. Then a novel, to the best of our knowledge, U-shaped multi-task network, BDA-Net, combined with a bidirectional decoder and self-attention mechanism, is used to further analyze abnormal images. Experimental results show that the proposed method reaches an accuracy of 0.9913 in classification and achieves an improvement of around 3% in Dice compared to the baseline U-shaped model in segmentation.
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Multi-label classification of retinal lesions in diabetic retinopathy for automatic analysis of fundus fluorescein angiography based on deep learning.
Pan, X, Jin, K, Cao, J, Liu, Z, Wu, J, You, K, Lu, Y, Xu, Y, Su, Z, Jiang, J, et al
Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie. 2020;(4):779-785
Abstract
PURPOSE To automatically detect and classify the lesions of diabetic retinopathy (DR) in fundus fluorescein angiography (FFA) images using deep learning algorithm through comparing 3 convolutional neural networks (CNNs). METHODS A total of 4067 FFA images from Eye Center at the Second Affiliated Hospital of Zhejiang University School of Medicine were annotated with 4 kinds of lesions of DR, including non-perfusion regions (NP), microaneurysms, leakages, and laser scars. Three CNNs including DenseNet, ResNet50, and VGG16 were trained to achieve multi-label classification, which means the algorithms could identify 4 retinal lesions above at the same time. RESULTS The area under the curve (AUC) of DenseNet reached 0.8703, 0.9435, 0.9647, and 0.9653 for detecting NP, microaneurysms, leakages, and laser scars, respectively. For ResNet50, AUC was 0.8140 for NP, 0.9097 for microaneurysms, 0.9585 for leakages, and 0.9115 for laser scars. And for VGG16, AUC was 0.7125 for NP, 0.5569 for microaneurysms, 0.9177 for leakages, and 0.8537 for laser scars. CONCLUSIONS Experimental results demonstrate that DenseNet is a suitable model to automatically detect and distinguish retinal lesions in the FFA images with multi-label classification, which lies the foundation of automatic analysis for FFA images and comprehensive diagnosis and treatment decision-making for DR.
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CEST MR-Fingerprinting: Practical considerations and insights for acquisition schedule design and improved reconstruction.
Perlman, O, Herz, K, Zaiss, M, Cohen, O, Rosen, MS, Farrar, CT
Magnetic resonance in medicine. 2020;(2):462-478
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Abstract
PURPOSE To understand the influence of various acquisition parameters on the ability of CEST MR-Fingerprinting (MRF) to discriminate different chemical exchange parameters and to provide tools for optimal acquisition schedule design and parameter map reconstruction. METHODS Numerical simulations were conducted using a parallel computing implementation of the Bloch-McConnell equations, examining the effect of TR, TE, flip-angle, water T1 and T2 , saturation-pulse duration, power, and frequency on the discrimination ability of CEST-MRF. A modified Euclidean distance matching metric was evaluated and compared to traditional dot product matching. L-Arginine phantoms of various concentrations and pH were scanned at 4.7T and the results compared to numerical findings. RESULTS Simulations for dot product matching demonstrated that the optimal flip-angle and saturation times are 30∘ and 1100 ms, respectively. The optimal maximal saturation power was 3.4 μT for concentrated solutes with a slow exchange rate, and 5.2 μT for dilute solutes with medium-to-fast exchange rates. Using the Euclidean distance matching metric, much lower maximum saturation powers were required (1.6 and 2.4 μT, respectively), with a slightly longer saturation time (1500 ms) and 90∘ flip-angle. For both matching metrics, the discrimination ability increased with the repetition time. The experimental results were in agreement with simulations, demonstrating that more than a 50% reduction in scan-time can be achieved by Euclidean distance-based matching. CONCLUSIONS Optimization of the CEST-MRF acquisition schedule is critical for obtaining the best exchange parameter accuracy. The use of Euclidean distance-based matching of signal trajectories simultaneously improved the discrimination ability and reduced the scan time and maximal saturation power required.