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1.
Sensors (Basel) ; 21(17)2021 Aug 26.
Artigo em Inglês | MEDLINE | ID: mdl-34502643

RESUMO

Homography mapping is often exploited to remove perspective distortion in images and can be estimated using point correspondences of a known object (marker). We focus on scenarios with multiple markers placed on the same plane if their relative positions in the world are unknown, causing an indeterminate point correspondence. Existing approaches may only estimate an isolated homography for each marker and cannot determine which homography achieves the best reprojection over the entire image. We thus propose a method to rank isolated homographies obtained from multiple distinct markers to select the best homography. This method extends existing approaches in the post-processing stage, provided that the point correspondences are available and that the markers differ only by similarity transformation after rectification. We demonstrate the robustness of our method using a synthetic dataset and show an approximately 60% relative improvement over the random selection strategy based on the homography estimation from the OpenCV library.


Assuntos
Algoritmos , Interpretação de Imagem Assistida por Computador
2.
Int J Mol Sci ; 22(16)2021 Aug 16.
Artigo em Inglês | MEDLINE | ID: mdl-34445517

RESUMO

We introduce here a novel machine learning (ML) framework to address the issue of the quantitative assessment of the immune content in neuroblastoma (NB) specimens. First, the EUNet, a U-Net with an EfficientNet encoder, is trained to detect lymphocytes on tissue digital slides stained with the CD3 T-cell marker. The training set consists of 3782 images extracted from an original collection of 54 whole slide images (WSIs), manually annotated for a total of 73,751 lymphocytes. Resampling strategies, data augmentation, and transfer learning approaches are adopted to warrant reproducibility and to reduce the risk of overfitting and selection bias. Topological data analysis (TDA) is then used to define activation maps from different layers of the neural network at different stages of the training process, described by persistence diagrams (PD) and Betti curves. TDA is further integrated with the uniform manifold approximation and projection (UMAP) dimensionality reduction and the hierarchical density-based spatial clustering of applications with noise (HDBSCAN) algorithm for clustering, by the deep features, the relevant subgroups and structures, across different levels of the neural network. Finally, the recent TwoNN approach is leveraged to study the variation of the intrinsic dimensionality of the U-Net model. As the main task, the proposed pipeline is employed to evaluate the density of lymphocytes over the whole tissue area of the WSIs. The model achieves good results with mean absolute error 3.1 on test set, showing significant agreement between densities estimated by our EUNet model and by trained pathologists, thus indicating the potentialities of a promising new strategy in the quantification of the immune content in NB specimens. Moreover, the UMAP algorithm unveiled interesting patterns compatible with pathological characteristics, also highlighting novel insights into the dynamics of the intrinsic dataset dimensionality at different stages of the training process. All the experiments were run on the Microsoft Azure cloud platform.


Assuntos
Interpretação de Imagem Assistida por Computador/métodos , Neuroblastoma/imunologia , Computação em Nuvem , Aprendizado Profundo , Feminino , Humanos , Linfócitos/metabolismo , Masculino , Redes Neurais de Computação , Neuroblastoma/diagnóstico por imagem
3.
Acta Cytol ; 65(4): 354-357, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34350848

RESUMO

Morphological analysis of the bone marrow is an essential step in the diagnosis of hematological disease. The conventional analysis of bone marrow smears is performed under a manual microscope, which is labor-intensive and subject to interobserver variability. The morphological differential diagnosis of abnormal lymphocytes from normal lymphocytes is still challenging. The digital pathology methods integrated with advances in machine learning enable new diagnostic features/algorithms from digital bone marrow cell images in order to optimize classification, thus providing a robust and faster screening diagnostic tool. We have developed a machine learning system, Morphogo, based on algorithms to discriminate abnormal lymphocytes from normal lymphocytes using digital imaging analysis. We retrospectively reviewed 347 cases of bone marrow digital images. Among them, 53 cases had a clinical history and the diagnosis of marrow involvement with lymphoma was confirmed either by morphology or flow cytometry. We split the 53 cases into two groups for training and testing with 43 and 10 cases, respectively. The selected 15,353 cell images were reviewed by pathologists, based on morphological visual appearance, from 43 patients whose diagnosis was confirmed by complementary tests. To expand the range and the precision of recognizing the lymphoid cells in the marrow by automated digital microscopy systems, we developed an algorithm that incorporated color and texture in addition to geometrical cytological features of the variable lymphocyte images which were applied as the training data set. The selected images from the 10 patients were analyzed by the trained artificial intelligence-based recognition system and compared with the final diagnosis rendered by pathologists. The positive predictive value for the identification of the categories of reactive/normal lymphocytes and abnormal lymphoid cells was 99.04%. It seems likely that further training and improvement of the algorithms will facilitate further subclassification of specific lineage subset pathology, e.g., diffuse large B-cell lymphoma from chronic lymphocytic leukemia/small lymphocytic lymphoma, follicular lymphoma, mantle cell lymphoma or even hairy cell leukemia in cases of abnormal malignant lymphocyte classes in the future. This research demonstrated the feasibility of digital pathology and emerging machine learning approaches to automatically diagnose lymphoma cells in the bone marrow based on cytological-histological analyses.


Assuntos
Células da Medula Óssea/patologia , Exame de Medula Óssea , Diagnóstico por Computador , Interpretação de Imagem Assistida por Computador , Linfócitos/patologia , Linfoma/patologia , Aprendizado de Máquina , Microscopia , Humanos , Valor Preditivo dos Testes , Reprodutibilidade dos Testes , Estudos Retrospectivos
4.
Radiol Clin North Am ; 59(5): 693-703, 2021 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-34392913

RESUMO

Precision medicine integrates molecular pathobiology, genetic make-up, and clinical manifestations of disease in order to classify patients into subgroups for the purposes of predicting treatment response and suggesting outcome. By identifying those patients who are most likely to benefit from a given therapy, interventions can be tailored to avoid the expense and toxicity of futile treatment. Ultimately, the goal is to offer the right treatment, to the right patient, at the right time. Lung cancer is a heterogeneous disease both functionally and morphologically. Further, over time, clonal proliferations of cells may evolve, becoming resistant to specific therapies. PET is a sensitive imaging technique with an important role in the precision medicine algorithm of lung cancer patients. It provides anatomo-functional insight during diagnosis, staging, and restaging of the disease. It is a prognostic biomarker in lung cancer patients that characterizes tumoral heterogeneity, helps predict early response to therapy, and may direct the selection of appropriate treatment.


Assuntos
Neoplasias Pulmonares/diagnóstico por imagem , Neoplasias Pulmonares/patologia , Imagem Molecular/tendências , Tomografia por Emissão de Pósitrons/tendências , Medicina de Precisão/tendências , Fluordesoxiglucose F18 , Humanos , Interpretação de Imagem Assistida por Computador , Estadiamento de Neoplasias , Tomografia por Emissão de Pósitrons combinada à Tomografia Computadorizada/tendências , Compostos Radiofarmacêuticos
5.
Medicine (Baltimore) ; 100(31): e26823, 2021 Aug 06.
Artigo em Inglês | MEDLINE | ID: mdl-34397844

RESUMO

ABSTRACT: Low specificity and operator dependency are the main problems of breast ultrasound (US) screening. We investigated the added value of deep learning-based computer-aided diagnosis (S-Detect) and shear wave elastography (SWE) to B-mode US for evaluation of breast masses detected by screening US.Between February 2018 and June 2019, B-mode US, S-Detect, and SWE were prospectively obtained for 156 screening US-detected breast masses in 146 women before undergoing US-guided biopsy. S-Detect was applied for the representative B-mode US image, and quantitative elasticity was measured for SWE. Breast Imaging Reporting and Data System final assessment category was assigned for the datasets of B-mode US alone, B-mode US plus S-Detect, and B-mode US plus SWE by 3 radiologists with varied experience in breast imaging. Area under the receiver operator characteristics curve (AUC), sensitivity, and specificity for the 3 datasets were compared using Delong's method and McNemar test.Of 156 masses, 10 (6%) were malignant and 146 (94%) were benign. Compared to B-mode US alone, the addition of S-Detect increased the specificity from 8%-9% to 31%-71% and the AUC from 0.541-0.545 to 0.658-0.803 in all radiologists (All P < .001). The addition of SWE to B-mode US also increased the specificity from 8%-9% to 41%-75% and the AUC from 0.541-0.545 to 0.709-0.823 in all radiologists (All P < .001). There was no significant loss in sensitivity when either S-Detect or SWE were added to B-mode US.Adding S-Detect or SWE to B-mode US improved the specificity and AUC without loss of sensitivity.


Assuntos
Neoplasias da Mama , Mama , Aprendizado Profundo , Diagnóstico por Computador/métodos , Técnicas de Imagem por Elasticidade/métodos , Ultrassonografia Mamária/métodos , Mama/diagnóstico por imagem , Mama/patologia , Neoplasias da Mama/diagnóstico por imagem , Neoplasias da Mama/epidemiologia , Neoplasias da Mama/patologia , Diagnóstico Diferencial , Detecção Precoce de Câncer/métodos , Feminino , Humanos , Interpretação de Imagem Assistida por Computador , Pessoa de Meia-Idade , Estudos Prospectivos , República da Coreia/epidemiologia , Sensibilidade e Especificidade
6.
IEEE Trans Neural Netw Learn Syst ; 32(9): 3786-3797, 2021 09.
Artigo em Inglês | MEDLINE | ID: mdl-34370672

RESUMO

Medical imaging technologies, including computed tomography (CT) or chest X-Ray (CXR), are largely employed to facilitate the diagnosis of the COVID-19. Since manual report writing is usually too time-consuming, a more intelligent auxiliary medical system that could generate medical reports automatically and immediately is urgently needed. In this article, we propose to use the medical visual language BERT (Medical-VLBERT) model to identify the abnormality on the COVID-19 scans and generate the medical report automatically based on the detected lesion regions. To produce more accurate medical reports and minimize the visual-and-linguistic differences, this model adopts an alternate learning strategy with two procedures that are knowledge pretraining and transferring. To be more precise, the knowledge pretraining procedure is to memorize the knowledge from medical texts, while the transferring procedure is to utilize the acquired knowledge for professional medical sentences generations through observations of medical images. In practice, for automatic medical report generation on the COVID-19 cases, we constructed a dataset of 368 medical findings in Chinese and 1104 chest CT scans from The First Affiliated Hospital of Jinan University, Guangzhou, China, and The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China. Besides, to alleviate the insufficiency of the COVID-19 training samples, our model was first trained on the large-scale Chinese CX-CHR dataset and then transferred to the COVID-19 CT dataset for further fine-tuning. The experimental results showed that Medical-VLBERT achieved state-of-the-art performances on terminology prediction and report generation with the Chinese COVID-19 CT dataset and the CX-CHR dataset. The Chinese COVID-19 CT dataset is available at https://covid19ct.github.io/.


Assuntos
COVID-19/diagnóstico por imagem , Aprendizado de Máquina , Relatório de Pesquisa/normas , Algoritmos , Inteligência Artificial , China , Humanos , Interpretação de Imagem Assistida por Computador , Terminologia como Assunto , Tomografia Computadorizada por Raios X , Transferência de Experiência , Redação
7.
Nutrients ; 13(7)2021 Jul 11.
Artigo em Inglês | MEDLINE | ID: mdl-34371881

RESUMO

Researchers and practitioners in sports nutrition would greatly benefit from a rapid, portable, and non-invasive technique to measure muscle glycogen, both in the laboratory and field. This explains the interest in MuscleSound®, the first commercial system to use high-frequency ultrasound technology and image analysis from patented cloud-based software to estimate muscle glycogen content from the echogenicity of the ultrasound image. This technique is based largely on muscle water content, which is presumed to act as a proxy for glycogen. Despite the promise of early validation studies, newer studies from independent groups reported discrepant results, with MuscleSound® scores failing to correlate with the glycogen content of biopsy-derived mixed muscle samples or to show the expected changes in muscle glycogen associated with various diet and exercise strategies. The explanation of issues related to the site of assessment do not account for these discrepancies, and there are substantial problems with the premise that the ratio of glycogen to water in the muscle is constant. Although further studies investigating this technique are warranted, current evidence that MuscleSound® technology can provide valid and actionable information around muscle glycogen stores is at best equivocal.


Assuntos
Glicogênio/análise , Interpretação de Imagem Assistida por Computador/métodos , Músculo Esquelético/diagnóstico por imagem , Avaliação Nutricional , Ciências da Nutrição e do Esporte/métodos , Ultrassonografia/métodos , Humanos , Estado de Hidratação do Organismo , Reprodutibilidade dos Testes , Software
8.
Sensors (Basel) ; 21(15)2021 Jul 22.
Artigo em Inglês | MEDLINE | ID: mdl-34372214

RESUMO

This paper proposes a method to embed and extract a watermark on a digital hologram using a deep neural network. The entire algorithm for watermarking digital holograms consists of three sub-networks. For the robustness of watermarking, an attack simulation is inserted inside the deep neural network. By including attack simulation and holographic reconstruction in the network, the deep neural network for watermarking can simultaneously train invisibility and robustness. We propose a network training method using hologram and reconstruction. After training the proposed network, we analyze the robustness of each attack and perform re-training according to this result to propose a method to improve the robustness. We quantitatively evaluate the results of robustness against various attacks and show the reliability of the proposed technique.


Assuntos
Segurança Computacional , Interpretação de Imagem Assistida por Computador , Algoritmos , Redes Neurais de Computação , Reprodutibilidade dos Testes
9.
Am J Pathol ; 191(8): 1431-1441, 2021 08.
Artigo em Inglês | MEDLINE | ID: mdl-34294192

RESUMO

Glomeruli instance segmentation from pathologic images is a fundamental step in the automatic analysis of renal biopsies. Glomerular histologic manifestations vary widely among diseases and cases, and several special staining methods are necessary for pathologic diagnosis. A robust model is needed to segment and classify glomeruli with different staining methods and apply in cases with various glomerular pathologic changes. Herein, pathologic images from renal biopsy slides stained with three basic special staining methods were used to build the data sets. The snapshot group included 1970 glomeruli from 516 patients, and the whole-slide image group included 8665 glomeruli from 148 patients. Cascade Mask region-based convolutional neural net architecture was trained to detect, classify, and segment glomeruli into three categories: i) GN, structural normal; ii) global sclerosis; and iii) glomerular with other lesions. In the snapshot group, total glomeruli, GN, global sclerosis, and glomerular with other lesions achieved an F1 score of 0.914, 0.896, 0.681, and 0.756, respectively, which were comparable with those in the whole-slide image group (0.940, 0.839, 0.806, and 0.753, respectively). Among the three categories, GN achieved the best instance segmentation effect in both groups, as determined by average precision, average recall, F1 score, and Mask mean Intersection over Union. The present model segments and classifies multistained glomeruli with efficiency and robustness. It can be applied as the first step for more detailed glomerular histologic analysis.


Assuntos
Aprendizado Profundo , Interpretação de Imagem Assistida por Computador/métodos , Nefropatias/diagnóstico , Glomérulos Renais/patologia , Biópsia , Humanos , Nefropatias/patologia , Coloração e Rotulagem
10.
Am J Pathol ; 191(9): 1520-1525, 2021 09.
Artigo em Inglês | MEDLINE | ID: mdl-34197776

RESUMO

The u-serrated immunodeposition pattern in direct immunofluorescence (DIF) microscopy is a recognizable feature and confirmative for the diagnosis of epidermolysis bullosa acquisita (EBA). Due to unfamiliarity with serrated patterns, serration pattern recognition is still of limited use in routine DIF microscopy. The objective of this study was to investigate the feasibility of using convolutional neural networks (CNNs) for the recognition of u-serrated patterns that can assist in the diagnosis of EBA. The nine most commonly used CNNs were trained and validated by using 220,800 manually delineated DIF image patches from 106 images of 46 different patients. The data set was split into 10 subsets: nine training subsets from 42 patients to train CNNs and the last subset from the remaining four patients for a validation data set of diagnostic accuracy. This process was repeated 10 times with a different subset used for validation. The best-performing CNN achieved a specificity of 89.3% and a corresponding sensitivity of 89.3% in the classification of u-serrated DIF image patches, an expert level of diagnostic accuracy. Experiments and results show the effectiveness of CNN approaches for u-serrated pattern recognition with a high accuracy. The proposed approach can assist clinicians and pathologists in recognition of u-serrated patterns in DIF images and facilitate the diagnosis of EBA.


Assuntos
Epidermólise Bolhosa Adquirida/diagnóstico , Interpretação de Imagem Assistida por Computador/métodos , Redes Neurais de Computação , Epidermólise Bolhosa Adquirida/patologia , Técnica Direta de Fluorescência para Anticorpo , Humanos , Microscopia de Fluorescência/métodos , Sensibilidade e Especificidade
11.
AJR Am J Roentgenol ; 217(3): 664-675, 2021 09.
Artigo em Inglês | MEDLINE | ID: mdl-34259544

RESUMO

OBJECTIVE. The purpose of our study was to develop a radiomics model based on preoperative MRI and clinical information for predicting recurrence-free survival (RFS) in patients with advanced high-grade serous ovarian carcinoma (HGSOC). MATERIALS AND METHODS. This retrospective study enrolled 117 patients with HGSOC, including 90 patients with recurrence and 27 without recurrence; 1046 radiomics features were extracted from T2-weighted images and contrast-enhanced T1-weighted images using a manual segmentation method. L1 regularization-based least absolute shrinkage and selection operator (LASSO) regression was performed to select features, and the synthetic minority oversampling technique (SMOTE) was used to balance our dataset. A support vector machine (SVM) classifier was used to build the classification model. To validate the performance of the proposed models, we applied a leave-one-out cross-validation method to train and test the classifier. Cox proportional hazards regression, Harrell concordance index (C-index), and Kaplan-Meier plots analysis were used to evaluate the associations between radiomics signatures and RFS. RESULTS. The fusion radiomics-based model yielded a significantly higher AUC value of 0.85 in evaluating RFS than the model using contrast-enhanced T1-weighted imaging features alone or T2-weighted imaging features alone (AUC = 0.79 and 0.74 and p = .02 and .01, respectively). Kaplan-Meier survival curves showed significant differences between high and low recurrence risk in patients with HGSOC by different models. The fusion model combining radiomics features and clinical information showed higher performance than the clinical model (C-index = 0.62 and 0.60, respectively). CONCLUSION. The proposed MRI-based radiomics signatures may provide a potential way to develop a prediction model and can help identify patients with advanced HGSOC who have a high risk of recurrence.


Assuntos
Interpretação de Imagem Assistida por Computador/métodos , Imageamento por Ressonância Magnética/métodos , Recidiva Local de Neoplasia/diagnóstico por imagem , Recidiva Local de Neoplasia/patologia , Neoplasias Ovarianas/diagnóstico por imagem , Neoplasias Ovarianas/patologia , Máquina de Vetores de Suporte , Feminino , Humanos , Pessoa de Meia-Idade , Ovário/diagnóstico por imagem , Ovário/patologia , Estudos Retrospectivos , Análise de Sobrevida
12.
Br J Radiol ; 94(1125): 20201314, 2021 Sep 01.
Artigo em Inglês | MEDLINE | ID: mdl-34233456

RESUMO

Radiomics is an emerging field of research that aims to find associations between quantitative information extracted from imaging examinations and clinical data to support the best clinical decision. In the last few years, some papers have been evaluating the role of radiomics in gynecological malignancies, mainly focusing on ovarian cancer. Nonetheless, cervical cancer is the most frequent gynecological malignancy in developing countries and endometrial cancer is the most common in western countries. The purpose of this narrative review is to give an overview of the latest published papers evaluating the role of radiomics in cervical and endometrial cancer, mostly evaluating association with tumor prognostic factors, with response to therapy and with prediction of recurrence and distant metastasis.


Assuntos
Diagnóstico por Imagem/métodos , Neoplasias do Endométrio/diagnóstico por imagem , Interpretação de Imagem Assistida por Computador/métodos , Neoplasias do Colo do Útero/diagnóstico por imagem , Colo do Útero/diagnóstico por imagem , Endométrio/diagnóstico por imagem , Feminino , Humanos
13.
Sensors (Basel) ; 21(13)2021 Jun 29.
Artigo em Inglês | MEDLINE | ID: mdl-34210041

RESUMO

The phase-to-height imaging model, as a three-dimensional (3D) measurement technology, has been commonly applied in fringe projection to assist surface profile measurement, where the efficient and accurate calculation of phase plays a critical role in precise imaging. To deal with multiple extra coded patterns and 2π jump error caused to the existing absolute phase demodulation methods, a novel method of phase demodulation is proposed based on dual variable-frequency (VF) coded patterns. In this paper, the frequency of coded fringe is defined as the number of coded fringes within a single sinusoidal fringe period. First, the effective wrapped phase (EWP) as calculated using the four-step phase shifting method was split into the wrapped phase region with complete period and the wrapped phase region without complete period. Second, the fringe orders in wrapped phase region with complete period were decoded according to the frequency of the VF coded fringes and the continuous characteristic of the fringe order. Notably, the sampling frequency of fast Fourier transform (FFT) was determined by the length of the decoding interval and can be adjusted automatically with the variation in height of the object. Third, the fringe orders in wrapped phase region without complete period were decoded depending on the consistency of fringe orders in the connected region of wrapped phase. Last, phase demodulation was performed. The experimental results were obtained to confirm the effectiveness of the proposed method in the phase demodulation of both discontinuous objects and highly abrupt objects.


Assuntos
Algoritmos , Interpretação de Imagem Assistida por Computador , Análise de Fourier , Imageamento Tridimensional
14.
BMC Res Notes ; 14(1): 289, 2021 Jul 27.
Artigo em Inglês | MEDLINE | ID: mdl-34315510

RESUMO

OBJECTIVES: Four-dimensional flow CMR allows for a comprehensive assessment of the blood flow kinetic energy of the ventricles of the heart. In comparison to standard two-dimensional image acquisition, 4D flow CMR is felt to offer superior reproducibility, which is important when repeated examinations may be required. The objective was to evaluate the inter-observer and intra-observer reproducibility of blood flow kinetic energy assessment using 4D flow of the left ventricle in 20 healthy volunteers across two centres in the United Kingdom and the Netherlands. DATA DESCRIPTION: This dataset contains 4D flow CMR blood flow kinetic energy data for 20 healthy volunteers with no known cardiovascular disease. Presented is kinetic energy data for the entire cardiac cycle (global), the systolic and diastolic components, in addition to blood flow kinetic energy for both early and late diastolic filling. This data is available for reuse and would be valuable in supporting other research, such as allowing for larger sample sizes with more statistical power for further analysis of these variables.


Assuntos
Interpretação de Imagem Assistida por Computador , Velocidade do Fluxo Sanguíneo , Humanos , Países Baixos , Valor Preditivo dos Testes , Reprodutibilidade dos Testes , Reino Unido
15.
IEEE J Transl Eng Health Med ; 9: 1800209, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34235005

RESUMO

Background: Accurate and fast diagnosis of COVID-19 is very important to manage the medical conditions of affected persons. The task is challenging owing to shortage and ineffectiveness of clinical testing kits. However, the existing problems can be improved by employing computational intelligent techniques on radiological images like CT-Scans (Computed Tomography) of lungs. Extensive research has been reported using deep learning models to diagnose the severity of COVID-19 from CT images. This has undoubtedly minimized the manual involvement in abnormality identification but reported detection accuracy is limited. Methods: The present work proposes an expert model based on deep features and Parameter Free BAT (PF-BAT) optimized Fuzzy K-nearest neighbor (PF-FKNN) classifier to diagnose novel coronavirus. In this proposed model, features are extracted from the fully connected layer of transfer learned MobileNetv2 followed by FKNN training. The hyperparameters of FKNN are fine-tuned using PF-BAT. Results: The experimental results on the benchmark COVID CT scan data reveal that the proposed algorithm attains a validation accuracy of 99.38% which is better than the existing state-of-the-art methods proposed in past. Conclusion: The proposed model will help in timely and accurate identification of the coronavirus at the various phases. Such kind of rapid diagnosis will assist clinicians to manage the healthcare condition of patients well and will help in speedy recovery from the diseases. Clinical and Translational Impact Statement - The proposed automated system can provide accurate and fast detection of COVID-19 signature from lung radiographs. Also, the usage of lighter MobileNetv2 architecture makes it practical for deployment in real-time.


Assuntos
COVID-19/diagnóstico por imagem , Aprendizado Profundo , Interpretação de Imagem Assistida por Computador/métodos , Algoritmos , Feminino , Humanos , Pulmão/diagnóstico por imagem , Masculino , SARS-CoV-2 , Tomografia Computadorizada por Raios X
17.
Sensors (Basel) ; 21(12)2021 Jun 14.
Artigo em Inglês | MEDLINE | ID: mdl-34198486

RESUMO

Digital cameras obtain color information of the scene using a chromatic filter, usually a Bayer filter, overlaid on a pixelated detector. However, the periodic arrangement of both the filter array and the detector array introduces frequency aliasing in sampling and color misregistration during demosaicking process which causes degradation of image quality. Inspired by the biological structure of the avian retinas, we developed a chromatic LED array which has a geometric arrangement of multi-hyperuniformity, which exhibits an irregularity on small-length scales but a quasi-uniformity on large scales, to suppress frequency aliasing and color misregistration in full color image retrieval. Experiments were performed with a single-pixel imaging system using the multi-hyperuniform chromatic LED array to provide structured illumination, and 208 fps frame rate was achieved at 32 × 32 pixel resolution. By comparing the experimental results with the images captured with a conventional digital camera, it has been demonstrated that the proposed imaging system forms images with less chromatic moiré patterns and color misregistration artifacts. The concept proposed verified here could provide insights for the design and the manufacturing of future bionic imaging sensors.


Assuntos
Aumento da Imagem , Interpretação de Imagem Assistida por Computador , Algoritmos , Biônica , Cor , Colorimetria
18.
Comput Methods Programs Biomed ; 208: 106286, 2021 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-34311412

RESUMO

BACKGROUND AND OBJECTIVE: Previous studies have indicated that brain morphological measures change in patients with amnestic mild cognitive impairment (aMCI). However, most existing classification methods cannot take full advantage of these measures. In this study, we improve traditional multitask learning framework by fully considering the relevance among related tasks and supplementary information from other unrelated tasks at the same time. METHODS: We propose a feature level-based group lasso (FL-GL) method in which a feature represents the average value of each ROI for each measure. First, we design a correlation matrix in which each row represents the relationship among different measures for each ROI. And this matrix is used to guide the feature selection based on a group lasso framework. Then, we train specific support vector machine (SVM) classifiers with the selected features for each measure. Finally, a weighted voting strategy is applied to combine these classifiers for a final prediction of aMCI from normal control (NC). RESULTS: We use the leave-one-out cross-validation strategy to verify our method on two datasets, the Xuan Wu Hospital dataset and the ADNI dataset. Compared with the traditional method, the results show that the classification accuracies can be improved by 6.12 and 4.92% with the FL-GL method on the two datasets. CONCLUSIONS: The results of an ablation study indicated that feature level-based group sparsity term was the core of our method. So, considering correlation at the feature level could improve the traditional multitask learning framework and our FL-GL method obtained better classification performance of patients with MCI and NCs.


Assuntos
Doença de Alzheimer , Disfunção Cognitiva , Encéfalo , Disfunção Cognitiva/diagnóstico , Humanos , Interpretação de Imagem Assistida por Computador , Imageamento por Ressonância Magnética
19.
J Clin Neurosci ; 90: 165-170, 2021 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-34275544

RESUMO

The purposes of this study were (1) to investigate postoperative changes in cross-sectional area (CSA) and signal intensity (SI) of the psoas muscle (PS) using magnetic resonance imaging (MRI) and (2) to compare the CSA and SI of the PS between patients with and without motor weakness after single-level lateral lumbar interbody fusion (LLIF) at level L4-L5. Sixty patients were divided into two groups-those with postoperative motor weakness and those without-and the two groups were compared. Baseline demographics and clinical characteristics, such as operation time and blood loss, length of hospital stay, and postoperative complications, were recorded. The CSA and SI of the PS were obtained from the MRI regions of interest defined by manual tracing. Patients who developed motor weakness after surgery were significantly older (p = 0.040). The operation time (p = 0.868), LLIF operative time (p = 0.476), and estimated bleeding loss (p = 0.168) did not differ significantly between groups. In both groups, the CSA and SI of the left and right PS increased after surgery. The change in the CSA of the left PS was significantly higher in patients with weakness (247.6 ± 155.2 mm2) than without weakness (152.2 ± 133.1 mm2) (p = 0.036). The change in SI of the left PS did not differ between the two groups (p = 0.530). To prevent postoperative motor weakness regardless of the operation time, surgeons should be aware of the potential for surgical invasive of the PS during LLIF in older people.


Assuntos
Debilidade Muscular/diagnóstico por imagem , Complicações Pós-Operatórias/diagnóstico por imagem , Músculos Psoas/diagnóstico por imagem , Músculos Psoas/fisiopatologia , Fusão Vertebral/efeitos adversos , Adulto , Idoso , Feminino , Humanos , Interpretação de Imagem Assistida por Computador/métodos , Vértebras Lombares/cirurgia , Imageamento por Ressonância Magnética/métodos , Masculino , Pessoa de Meia-Idade , Debilidade Muscular/epidemiologia , Debilidade Muscular/etiologia , Complicações Pós-Operatórias/etiologia , Músculos Psoas/cirurgia , Estudos Retrospectivos , Fusão Vertebral/métodos
20.
J Clin Neurosci ; 90: 244-250, 2021 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-34275557

RESUMO

Although T2-weighted axial magnetic resonance imaging (MRI) has strength in demonstrating morphologic characteristics of the spinal cord in cervical spondylotic myelopathy (CSM), no study has investigated postoperative changes. We aimed to assess postoperative changes on T2-weighted axial MRI using the classification system based on axial imaging in cervical compressive myelopathy (Ax-CCM) and associated impact on outcome in CSM. In total, 250 patients with CSM who underwent decompressive surgery with preoperative and postoperative MRI were included. At first, we investigated the presence of increased signal intensity (SI) in cervical spinal cord on T2-weighted sagittal images. Next, the increased SI was assessed using Ax-CCM on T2weighted axial images. The classifications were type 0, no-signal abnormality; single-level type 1, diffuse; single-level type 2, fuzzy focal; single-level type 3, discrete focal; and two-level. The recovery rates (RRs) of modified Japanese Orthopaedic Association (mJOA) score were evaluated from 5 to 10 months postoperatively. Eighty-seven patients (34.8%) exhibited postoperative changes. Most of postoperative changes were in single-level type 1 and 2. Patterns of changes were resolution, reduced extent, or transition to discrete margin. The most common pattern was resolution in type 1 (23.9%) and transition to discrete margin in type 2 (46.5%). In each group, resolution showed the best RR, but insignificantly (p > 0.05).


Assuntos
Imageamento por Ressonância Magnética/métodos , Doenças da Medula Espinal/diagnóstico por imagem , Doenças da Medula Espinal/cirurgia , Espondilose/diagnóstico por imagem , Espondilose/cirurgia , Adulto , Idoso , Idoso de 80 Anos ou mais , Medula Cervical/diagnóstico por imagem , Medula Cervical/patologia , Medula Cervical/cirurgia , Vértebras Cervicais/cirurgia , Descompressão Cirúrgica/métodos , Feminino , Humanos , Interpretação de Imagem Assistida por Computador , Masculino , Pessoa de Meia-Idade , Estudos Retrospectivos , Doenças da Medula Espinal/patologia , Espondilose/patologia , Resultado do Tratamento
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