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1.
Comput Biol Med ; 120: 103728, 2020 May.
Artigo em Inglês | MEDLINE | ID: mdl-32250857

RESUMO

Multiview based learning has generally returned dividends in performance because additional information can be extracted for the representation of the diversity of different views. The advantage of multiview based learning fits the purpose of segmenting cardiac anatomy from multiview echocardiography, which is a non-invasive, low-cost and low-risk imaging modality. Nevertheless, it is still challenging because of limited training data, a poor signal-to-noise ratio of the echocardiographic data, and large variances across views for a joint learning. In addition, for a better interpretation of pathophysiological processes, clinical decision-making and prognosis, such cardiac anatomy segmentation and quantitative analysis of various clinical indices should ideally be performed for the data covering the full cardiac cycle. To tackle these challenges, a multiview recurrent aggregation network (MV-RAN) has been developed for the echocardiographic sequences segmentation with the full cardiac cycle analysis. Experiments have been carried out on multicentre and multi-scanner clinical studies consisting of spatio-temporal (2D + t) datasets. Compared to other state-of-the-art deep learning based methods, the MV-RAN method has achieved significantly superior results (0.92 ± 0.04 Dice scores) for the segmentation of the left ventricle on the independent testing datasets. For the estimation of clinical indices, our MV-RAN method has also demonstrated great promise and will undoubtedly propel forward the understanding of pathophysiological processes, computer-aided diagnosis and personalised prognosis using echocardiography.

2.
Artigo em Inglês | MEDLINE | ID: mdl-32310804

RESUMO

The estimation of multitype cardiac indices from cardiac magnetic resonance imaging (MRI) and computed tomography (CT) images attracts great attention because of its clinical potential for comprehensive function assessment. However, the most exiting model can only work in one imaging modality (MRI or CT) without transferable capability. In this article, we propose the multitask learning method with the reverse inferring for estimating multitype cardiac indices in MRI and CT. Different from the existing forward inferring methods, our method builds a reverse mapping network that maps the multitype cardiac indices to cardiac images. The task dependencies are then learned and shared to multitask learning networks using an adversarial training approach. Finally, we transfer the parameters learned from MRI to CT. A series of experiments were conducted in which we first optimized the performance of our framework via ten-fold cross-validation of over 2900 cardiac MRI images. Then, the fine-tuned network was run on an independent data set with 2360 cardiac CT images. The results of all the experiments conducted on the proposed adversarial reverse mapping show excellent performance in estimating multitype cardiac indices.

3.
Neural Netw ; 123: 82-93, 2020 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-31835156

RESUMO

Humans perceive physical properties such as motion and elastic force by observing objects in visual scenes. Recent research has proven that computers are capable of inferring physical properties from camera images like humans. However, few studies perceive the physical properties in more complex environment, i.e. humans have difficulty estimating physical quantities directly from the visual observation, or encounter difficulty visualizing the physical process in mind according to their daily experiences. As an appropriate example, fractional flow reserve (FFR), which measures the blood pressure difference across the vessel stenosis, becomes an important physical quantitative value determining the likelihood of myocardial ischemia in clinical coronary intervention procedure. In this study, we propose a novel deep neural network solution (TreeVes-Net) that allows machines to perceive FFR values directly from static coronary CT angiography images. Our framework fully utilizes a tree-structured recurrent neural network (RNN) with a coronary representation encoder. The encoder captures coronary geometric information providing the blood fluid-related representation. The tree-structured RNN builds a long-distance spatial dependency of blood flow information inside the coronary tree. The experiments performed on 13000 synthetic coronary trees and 180 real coronary trees from clinical patients show that the values of the area under ROC curve (AUC) are 0.92 and 0.93 under two clinical criterions. These results can demonstrate the effectiveness of our framework and its superiority to seven FFR computation methods based on machine learning.

4.
Med Image Anal ; 59: 101568, 2020 01.
Artigo em Inglês | MEDLINE | ID: mdl-31622838

RESUMO

Accurate and simultaneous segmentation and full quantification (all indices are required in a clinical assessment) of the myocardial infarction (MI) area are crucial for early diagnosis and surgical planning. Current clinical methods remain subject to potential high-risk, nonreproducibility and time-consumption issues. In this study, a deep spatiotemporal adversarial network (DSTGAN) is proposed as a contrast-free, stable and automatic clinical tool to simultaneously segment and quantify MIs directly from the cine MR image. The DSTGAN is implemented using a conditional generative model, which conditions the distributions of the objective cine MR image to directly optimize the generalized error of the mapping between the input and the output. The method consists of the following: (1) A multi-level and multi-scale spatiotemporal variation encoder learns a coarse to fine hierarchical feature to effectively encode the MI-specific morphological and kinematic abnormality structures, which vary for different spatial locations and time periods. (2) The top-down and cross-task generators learn the shared representations between segmentation and quantification to use the commonalities and differences between the two related tasks and enhance the generator preference. (3) Three inter-/intra-tasks to label the relatedness discriminators are iteratively imposed on the encoder and generator to detect and correct the inconsistencies in the label relatedness between and within tasks via adversarial learning. Our proposed method yields a pixel classification accuracy of 96.98%, and the mean absolute error of the MI centroid is 0.96 mm from 165 clinical subjects. These results indicate the potential of our proposed method in aiding standardized MI assessments.

5.
Artigo em Inglês | MEDLINE | ID: mdl-31715563

RESUMO

Intracoronary imaging is a crucial imaging technology in coronary disease diagnosis as it visualizes the internal tissue morphologies of coronary arteries. Vessel border detection in intracoronary images (VBDI) is desired because it can help the succeeding procedures of computer-aided disease diagnosis. However, existing VDBI methods suffer from the challenge of vessel-environment variability (i.e. high intra-and inter-subject diversity of vessels and their surrounding tissues appeared in images). This challenge leads to the ineffectiveness in the vessel region representation for hand-crafted features, in the receptive field extraction for deeply-represented features, as well as performance suppression derived from clinical data limitation. To solve this challenge, we propose a novel privileged modality distillation (PMD) framework for VBDI. PMD transforms the single-input-single-task (SIST) learning problem in the single-mode VBDI to a multiple-input-multiple-task (MIMT) problem by using the privileged image modality to help the learning model in the target modality. This learns the enriched high-level knowledge with similar semantics and generalizes PMD on diversity-increased low-level image features for improving the model adaptation to diverse vessel environments. Moreover, PMD refines MIMT to SIST by distilling the learned knowledge from multiple to one modality. This eliminates the reliance on privileged modality in the test phase, and thus enables the applicability to each of different intracoronary modalities. A structure-deformable neural network is proposed as an elaborately-designed implementation of PMD. It expands a conventional SIST network structure to the MIMT structure, and then recovers it to the final SIST structure. The PMD is validated on intravascular ultrasound imaging and optical coherence tomography imaging. One modality is the target, and the other one can be considered as the privileged modality owing to their semantic relatedness. The experiments show that our PMD is effective in VBDI (e.g. the Dice index is larger than 0.95), as well as superior to six state-of-the-art VBDI methods.

6.
Comput Biol Med ; 115: 103485, 2019 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-31630029

RESUMO

Glaucoma is a chronic and widespread eye disease threatening humans' irreversible vision loss. The cup-to-disc ratio (CDR), one of the most important measurements used for glaucoma screening and diagnosis, requires accurate segmentation of optic disc and cup from fundus images. However, most existing techniques fail to obtain satisfactory segmentation performance because a significant number of pixel-level annotated data are often unavailable during training. To cope with this limitation, in this paper, we propose an effective joint optic disc and cup segmentation method based on semi-supervised conditional Generative Adversarial Nets (GANs). Our architecture consists of a segmentation net, a generator and a discriminator, to learn a mapping between the fundus images and the corresponding segmentation maps. Additionally, we employ both labeled and unlabeled data to improve the segmentation performance. The extensive experiments show that our method achieves state-of-the-art optic disc and cup segmentation results on both ORIGA and REFUGE datasets.

7.
Med Image Anal ; 58: 101554, 2019 12.
Artigo em Inglês | MEDLINE | ID: mdl-31546227

RESUMO

Accurate direct estimation of the left ventricle (LV) multitype indices from two-dimensional (2D) echocardiograms of paired apical views, i.e., paired apical four-chamber (A4C) and two-chamber (A2C), is of great significance to clinically evaluate cardiac function. It enables a comprehensive assessment from multiple dimensions and views. Yet it is extremely challenging and has never been attempted, due to significantly varied LV shape and appearance across subjects and along cardiac cycle, the complexity brought by the paired different views, unexploited inter-frame indices relatedness hampering working effect, and low image quality preventing segmentation. We propose a paired-views LV network (PV-LVNet) to automatically and directly estimate LV multitype indices from paired echo apical views. Based on a newly designed Res-circle Net, the PV-LVNet robustly locates LV and automatically crops LV region of interest from A4C and A2C sequence with location module and image resampling, then accurately and consistently estimates 7 different indices of multiple dimensions (1D, 2D & 3D) and views (A2C, A4C, and union of A2C+A4C) with indices module. The experiments show that our method achieves high performance with accuracy up to 2.85mm mean absolute error and internal consistency up to 0.974 Cronbach's α for the cardiac indices estimation. All of these indicate that our method enables an efficient, accurate and reliable cardiac function diagnosis in clinical.

8.
Med Image Anal ; 58: 101534, 2019 12.
Artigo em Inglês | MEDLINE | ID: mdl-31352179

RESUMO

Quasi-static ultrasound elastography is an importance imaging technology to assess the conditions of various diseases through reconstructing the tissue strain from radio frequency data. State-of-the-art strain reconstruction techniques suffer from the inexperienced user unfriendliness, high model bias, and low effectiveness-to-efficiency ratio. The three challenges result from the explicitness characteristic (i.e. explicit formulation of the reconstruction model) in these techniques. For these challenges, we are the first to develop an implicit strain reconstruction framework by a deep neural network architecture. However, the classic neural network methods are unsuitable to the strain reconstruction task because they are difficult to impose any direct influence on the intermediate state of the learning process. This may lead the map learned by the neural network to be biased with the desired map. In order to correct the intermediate state of the learning process, our framework proposes the learning-using-privileged-information (LUPI) paradigm with causality in the network. It provides the causal privileged information besides the training examples to help the network learning, while makes these privileged information unavailable at the test stage. This improvement can narrow the search region of the map learned by the network, and thus prompts the network to evolve towards the actual ultrasound elastography process. Moreover, in order to ensure the causality in LUPI, our framework proposes a physically-based data generation strategy to produce the triplets of privileged information, training examples and labels. This data generation process can approximately describes the actual ultrasound elastography process by the numerical simulation based on the tissue biomechanics and ultrasound physics. It thus can build the causal relationship between the privileged information and training examples/labels. It can also address the medical data insufficiency problem. The performance of our framework has been validated on 100 simulation data, 42 phantom data and 4 real clinical data by comparing with the ground truth performed by an ultrasound simulation system and four state-of-the-art methods. The experimental results show that our framework is well agreed (average bias is 0.065 for strain reconstruction) with the ground truth, as well as superior to these state-of-the-art methods. These results can demonstrate the effectiveness of our framework in the strain reconstruction.

9.
Eur Radiol ; 29(11): 6191-6201, 2019 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-31041565

RESUMO

OBJECTIVES: To evaluate the performance of a novel three-dimensional (3D) joint convolutional and recurrent neural network (CNN-RNN) for the detection of intracranial hemorrhage (ICH) and its five subtypes (cerebral parenchymal, intraventricular, subdural, epidural, and subarachnoid) in non-contrast head CT. METHODS: A total of 2836 subjects (ICH/normal, 1836/1000) from three institutions were included in this ethically approved retrospective study, with a total of 76,621 slices from non-contrast head CT scans. ICH and its five subtypes were annotated by three independent experienced radiologists, with majority voting as reference standard for both the subject level and the slice level. Ninety percent of data was used for training and validation, and the rest 10% for final evaluation. A joint CNN-RNN classification framework was proposed, with the flexibility to train when subject-level or slice-level labels are available. The predictions were compared with the interpretations from three junior radiology trainees and an additional senior radiologist. RESULTS: It took our algorithm less than 30 s on average to process a 3D CT scan. For the two-type classification task (predicting bleeding or not), our algorithm achieved excellent values (≥ 0.98) across all reporting metrics on the subject level. For the five-type classification task (predicting five subtypes), our algorithm achieved > 0.8 AUC across all subtypes. The performance of our algorithm was generally superior to the average performance of the junior radiology trainees for both two-type and five-type classification tasks. CONCLUSIONS: The proposed method was able to accurately detect ICH and its subtypes with fast speed, suggesting its potential for assisting radiologists and physicians in their clinical diagnosis workflow. KEY POINTS: • A 3D joint CNN-RNN deep learning framework was developed for ICH detection and subtype classification, which has the flexibility to train with either subject-level labels or slice-level labels. • This deep learning framework is fast and accurate at detecting ICH and its subtypes. • The performance of the automated algorithm was superior to the average performance of three junior radiology trainees in this work, suggesting its potential to reduce initial misinterpretations.


Assuntos
Algoritmos , Aprendizado Profundo , Imageamento Tridimensional/métodos , Hemorragias Intracranianas/diagnóstico , Tomografia Computadorizada por Raios X/métodos , Adolescente , Adulto , Idoso , Idoso de 80 Anos ou mais , Criança , Pré-Escolar , Feminino , Humanos , Lactente , Masculino , Pessoa de Meia-Idade , Reprodutibilidade dos Testes , Estudos Retrospectivos , Adulto Jovem
10.
Radiology ; 291(3): 606-617, 2019 06.
Artigo em Inglês | MEDLINE | ID: mdl-31038407

RESUMO

Background Renal impairment is common in patients with coronary artery disease and, if severe, late gadolinium enhancement (LGE) imaging for myocardial infarction (MI) evaluation cannot be performed. Purpose To develop a fully automatic framework for chronic MI delineation via deep learning on non-contrast material-enhanced cardiac cine MRI. Materials and Methods In this retrospective single-center study, a deep learning model was developed to extract motion features from the left ventricle and delineate MI regions on nonenhanced cardiac cine MRI collected between October 2015 and March 2017. Patients with chronic MI, as well as healthy control patients, had both nonenhanced cardiac cine (25 phases per cardiac cycle) and LGE MRI examinations. Eighty percent of MRI examinations were used for the training data set and 20% for the independent testing data set. Chronic MI regions on LGE MRI were defined as ground truth. Diagnostic performance was assessed by analysis of the area under the receiver operating characteristic curve (AUC). MI area and MI area percentage from nonenhanced cardiac cine and LGE MRI were compared by using the Pearson correlation, paired t test, and Bland-Altman analysis. Results Study participants included 212 patients with chronic MI (men, 171; age, 57.2 years ± 12.5) and 87 healthy control patients (men, 42; age, 43.3 years ± 15.5). Using the full cardiac cine MRI, the per-segment sensitivity and specificity for detecting chronic MI in the independent test set was 89.8% and 99.1%, respectively, with an AUC of 0.94. There were no differences between nonenhanced cardiac cine and LGE MRI analyses in number of MI segments (114 vs 127, respectively; P = .38), per-patient MI area (6.2 cm2 ± 2.8 vs 5.5 cm2 ± 2.3, respectively; P = .27; correlation coefficient, r = 0.88), and MI area percentage (21.5% ± 17.3 vs 18.5% ± 15.4; P = .17; correlation coefficient, r = 0.89). Conclusion The proposed deep learning framework on nonenhanced cardiac cine MRI enables the confirmation (presence), detection (position), and delineation (transmurality and size) of chronic myocardial infarction. However, future larger-scale multicenter studies are required for a full validation. Published under a CC BY 4.0 license. Online supplemental material is available for this article. See also the editorial by Leiner in this issue.


Assuntos
Aprendizado Profundo , Interpretação de Imagem Assistida por Computador/métodos , Imagem Cinética por Ressonância Magnética/métodos , Infarto do Miocárdio/diagnóstico por imagem , Adulto , Idoso , Doença Crônica , Bases de Dados Factuais , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Estudos Retrospectivos , Sensibilidade e Especificidade
11.
Eur Radiol ; 29(7): 3669-3677, 2019 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-30887203

RESUMO

BACKGROUND: We aimed to compare the performance of FFRCT and FFRQCA in assessing the functional significance of coronary artery stenosis in patients suffering from coronary artery disease with stable angina. METHOD: A total of 101 stable coronary heart disease (CAD) patients with 181 lesions were recruited. FFRCT and FFRQCA were compared using invasive fractional flow reserve (FFR) as a reference standard. Comparisons between FFRCT and FFRQCA were conducted based on strategies of the geometric reconstruction, boundary conditions, and geometric characteristics. The performance of FFRCT and FFRQCA in detecting hemodynamic significance was also investigated. RESULTS: The performance of FFRCT and FFRQCA in discriminating hemodynamically significant lesions was compared. Good correlation and agreement with invasive FFR was found using FFRCT and FFRQCA (r = 0.809, p < 0.001 and r = 0.755, p < 0.001). A significant difference was observed in the complex coronary artery tree, in which relatively better prediction was observed using FFRCT than FFRQCA when analyzing the stenosis distributed in the middle segment of a stenotic branch (p = 0.036). Moreover, FFRCT was found to be better at predicting hemodynamically insignificant stenosis than FFRQCA (p = 0.007), while the performance of the two parameters was similar in discriminating functional significant lesions using an FFR threshold of ≤ 0.8 as a reference standard. CONCLUSION: FFRCT and FFRQCA could both accurately rule out functional insignificant lesions in stable CAD patients. FFRCT was found to be better for the noninvasive screening of CAD patients with stable angina than FFRQCA. KEY POINTS: • FFR CT and FFR QCA were both in good correlation and agreement with invasive FFR measurements. • FFR CT is superior in accuracy and consistency compared to FFR QCA in patients with stenoses distributed in left coronary artery. • The noninvasive nature of FFR CT could provide potential benefit for stable CAD patients on disease management.


Assuntos
Angina Estável/diagnóstico , Angiografia por Tomografia Computadorizada/métodos , Angiografia Coronária/métodos , Reserva Fracionada de Fluxo Miocárdico/fisiologia , Idoso , Angina Estável/fisiopatologia , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Estudos Prospectivos , Índice de Gravidade de Doença
12.
Int J Comput Assist Radiol Surg ; 14(2): 271-280, 2019 Feb.
Artigo em Inglês | MEDLINE | ID: mdl-30484116

RESUMO

PURPOSE: Automated anatomical labeling facilitates the diagnostic process for physicians and radiologists. One of the challenges in automated anatomical labeling problems is the robustness to handle the large individual variability inherited in human anatomy. A novel deep neural network framework, referred to Tree Labeling Network (TreeLab-Net), is proposed to resolve this problem in this work. METHODS: A multi-layer perceptron (MLP) encoder network and a bidirectional tree-structural long short-term memory (Bi-TreeLSTM) are combined to construct the TreeLab-Net. Vessel spatial locations and directions are selected as features, where a spherical coordinate transform is utilized to normalize vessel spatial variations. The dataset includes 436 coronary computed tomography angiography images. Tenfold cross-validation is performed for evaluation. RESULTS: The precision-recall curve of TreeLab-Net shows that the four main branch classes, LM, LAD, LCX and RCA, have the area under the curve (AUC) higher than 97%. Other major side branch classes, D, OM, and R-PLB, also have AUC higher than 90%. Comparing with four other methods (i.e., AdaBoost, MLP, Up-to-Down and Down-to-Up TreeLSTM), the TreeLab-Net achieves higher F1 scores with less topological errors. CONCLUSION: The TreeLab-Net is able to capture the characteristics of tree structures by learning the spatial and topological dependencies of blood vessels effectively. The results demonstrate that TreeLab-Net is able to yield competitive performances on a large dataset with great variance among subjects.


Assuntos
Angiografia Coronária/métodos , Vasos Coronários/anatomia & histologia , Diagnóstico por Computador/métodos , Tomografia Computadorizada por Raios X/métodos , Adulto , Área Sob a Curva , Aprendizado Profundo , Feminino , Humanos , Masculino
13.
Phys Med Biol ; 64(2): 025005, 2019 01 08.
Artigo em Inglês | MEDLINE | ID: mdl-30524024

RESUMO

Multi-modality examinations have been extensively applied in current clinical cancer management. Leveraging multi-modality medical images can be highly beneficial for automated tumor segmentation as they provide complementary information that could make the segmentation of tumors more accurate. This paper investigates CNN-based methods for automated nasopharyngeal carcinoma (NPC) segmentation using computed tomography (CT) and magnetic resonance (MR) images. Specially, a multi-modality convolutional neural network (M-CNN) is designed to jointly learn a multi-modal similarity metric and segmentation of paired CT-MR images. By jointly optimizing the similarity learning error and the segmentation error, the feature learning processes of both modalities are mutually guided. In doing so, the segmentation sub-networks are able to take advantage of the other modality's information. Considering that each modality possesses certain distinctive characteristics, we combine the higher-layer features extracted by a single-modality CNN (S-CNN) and M-CNN to form a combined CNN (C-CNN) for each modality, which is able to further utilize the complementary information of different modalities and improve the segmentation performance. The proposed M-CNN and C-CNN were evaluated on 90 CT-MR images of NPC patients. Experimental results demonstrate that our methods achieve improved segmentation performance compared to their counterparts without multi-modal information fusion and the existing CNN-based multi-modality segmentation methods.


Assuntos
Processamento de Imagem Assistida por Computador/métodos , Imagem por Ressonância Magnética/métodos , Imagem Multimodal/métodos , Carcinoma Nasofaríngeo/diagnóstico por imagem , Carcinoma Nasofaríngeo/patologia , Tomografia Computadorizada por Raios X/métodos , Adulto , Idoso , Quimiorradioterapia , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Carcinoma Nasofaríngeo/terapia , Adulto Jovem
14.
J Magn Reson Imaging ; 49(3): 825-833, 2019 03.
Artigo em Inglês | MEDLINE | ID: mdl-30260592

RESUMO

BACKGROUND: Accurate classification of gliomas is crucial for prescribing therapy and assessing the prognosis of patients. PURPOSE: To develop a radiomics nomogram using multiparametric MRI for predicting glioma grading. STUDY TYPE: Retrospective. POPULATION: This study involved 85 patients (training cohort: n = 56; validation cohort: n = 29) with pathologically confirmed gliomas. FIELD STRENGTH/SEQUENCE: 1.5T MR, containing contrast-enhanced T1 -weighted (CET1 WI), axial T2 -weighted (T2 WI), and apparent diffusion coefficient (ADC) sequences. ASSESSMENT: A region of interest of the tumor was delineated. A total of 652 radiomics features were extracted and were reduced using least absolute shrinkage and selection operator regression. STATISTICAL TESTING: Radiomic signature, participant's age, and gender were analyzed as potential predictors to perform logistic regression analysis and develop a prediction model of glioma grading, and a radiomics nomogram was used to represent this model. The performance of the nomogram was assessed in terms of discrimination, calibration, and clinical value in glioma grading. RESULTS: The radiomic signature was significantly associated with glioma grade (P < 0.001) in both the training and validation cohorts. The performance of the radiomics nomogram derived from three MRI sequences (with C-index of 0.971 and 0.961 in the training and validation cohorts, respectively) was improved compared to those based on either CET1 WI, T2 WI, or ADC alone in glioma grading (with C-index of 0.914, 0.714, 0.842 in the training cohort, and 0.941, 0.500, 0.730 in the validation cohort). The nomogram derived from three sequences showed good calibration: the calibration curve showed good agreement between the estimated and the actual probability. The decision curve demonstrated that combining three sequences had more favorable clinical predictive value than single sequence imaging. DATA CONCLUSION: We created and assessed a multiparametric MRI-based radiomics nomogram that may help clinicians classify gliomas more accurately. LEVEL OF EVIDENCE: 4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:825-833.

15.
IEEE J Biomed Health Inform ; 23(2): 703-713, 2019 03.
Artigo em Inglês | MEDLINE | ID: mdl-29994054

RESUMO

Due to the variation in factors surrounding humans, the physiological impact of stress is reported to be different for each individual. Thus, an efficient stress monitoring system needs to assess both the physiological and psychological impact of stress on individual basis and translate these assessments into an accurate quantitative metric that is of value to the individual. Therefore, this study proposed a logistic regression based model that integrates data from psychological Stress Response Inventory, biochemical (salivary cortisol), and physiological (HRV measures) domains via a principle of triangulation for achieving high reliability and consistency during stress assessment. With the proposed model, a mental stress index (MSI) based on the correlation between salivary cortisol and HRV time-/frequency-domain features were established. A total of 30 college students were recruited to verify the feasibility of proposed method by identifying targeted stressful event. The obtained results reveal that MSI values were sensitive to acute stress, and could predict the association level of normal individual to a stress group with approximately 97% accuracy. Findings from this study could provide potential insight on self-tracking and training of individual's stress with adoption of wearable sensor system in a dynamic setting.


Assuntos
Monitorização Ambulatorial , Processamento de Sinais Assistido por Computador , Estresse Psicológico , Dispositivos Eletrônicos Vestíveis , Adulto , Análise por Conglomerados , Feminino , Frequência Cardíaca/fisiologia , Humanos , Hidrocortisona/análise , Masculino , Modelos Biológicos , Monitorização Ambulatorial/instrumentação , Monitorização Ambulatorial/métodos , Saliva/química , Autocuidado , Estresse Psicológico/classificação , Estresse Psicológico/diagnóstico , Estresse Psicológico/fisiopatologia , Adulto Jovem
16.
Conf Proc IEEE Eng Med Biol Soc ; 2018: 1123-1127, 2018 07.
Artigo em Inglês | MEDLINE | ID: mdl-30440587

RESUMO

Accurate delineation of heart substructures is a prerequisite for abnormality detection, for making quantitative and functional measurements, and for computer-aided diagnosis and treatment planning. Late Gadolinium-Enhanced Cardiac MRI (LGE-CMRI) is an emerging imaging technology for myocardial infarction or scar detection based on the differences in the volume of residual gadolinium distribution between scar and healthy tissues. While LGE-CMRI is a well-established non-invasive tool for detecting myocardial scar tissues in the ventricles, its application to left atrium (LA) imaging is more challenging due to its very thin wall of the LA and poor quality images, which may be produced because of motion artefacts and low signal-to-noise ratio. As the LGE-CMRI scan is designed to highlight scar tissues by altering the gadolinium kinetics, the anatomy among different heart substructures has less distinguishable boundaries. An accurate, robust and reproducible method for LA segmentation is highly in demand because it can not only provide valuable information of the heart function but also be helpful for the further delineation of scar tissue and measuring the scar percentage. In this study, we proposed a novel deep learning framework working on LGE-CMRI images directly by combining sequential learning and dilated residual learning to delineate LA and pulmonary veins fully automatically. The achieved results showed accurate segmentation results compared to the state-of-the-art methods. The proposed framework leads to an automatic generation of a patient-specific model that can potentially enable an objective atrial scarring assessment for the atrial fibrillation patients.


Assuntos
Fibrilação Atrial , Átrios do Coração , Veias Pulmonares , Meios de Contraste , Gadolínio , Humanos , Imagem por Ressonância Magnética
17.
Med Image Anal ; 50: 82-94, 2018 12.
Artigo em Inglês | MEDLINE | ID: mdl-30227385

RESUMO

Changes in mechanical properties of myocardium caused by a infarction can lead to kinematic abnormalities. This phenomenon has inspired us to develop this work for delineation of myocardial infarction area directly from non-contrast agents cardiac MR imaging sequences. The main contribution of this work is to develop a new joint motion feature learning architecture to efficiently establish direct correspondences between motion features and tissue properties. This architecture consists of three seamless connected function layers: the heart localization layers can automatically crop the region of interest (ROI) sequences involving the left ventricle from the cardiac MR imaging sequences; the motion feature extraction layers, using long short-term memory-recurrent neural networks, a) builds patch-based motion features through local intensity changes between fixed-size patch sequences (cropped from image sequences), and b) uses optical flow techniques to build image-based features through global intensity changes between adjacent images to describe the motion of each pixel; the fully connected discriminative layers can combine two types of motion features together in each pixel and then build the correspondences between motion features and tissue identities (that is, infarct or not) in each pixel. We validated the performance of our framework in 165 cine cardiac MR imaging datasets by comparing to the ground truths manually segmented from delayed Gadolinium-enhanced MR cardiac images by two radiologists with more than 10 years of experience. Our experimental results show that our proposed method has a high and stable accuracy (pixel-level: 95.03%) and consistency (Kappa statistic: 0.91; Dice: 89.87%; RMSE: 0.72  mm; Hausdorff distance: 5.91  mm) compared to manual delineation results. Overall, the advantage of our framework is that it can determine the tissue identity in each pixel from its motion pattern captured by normal cine cardiac MR images, which makes it an attractive tool for the clinical diagnosis of infarction.


Assuntos
Imagem por Ressonância Magnética/métodos , Infarto do Miocárdio/fisiopatologia , Ventrículos do Coração , Humanos , Movimento (Física)
18.
World Neurosurg ; 120: e1301-e1309, 2018 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-30240869

RESUMO

OBJECTIVE: To investigate hemodynamic changes in moyamoya disease (MMD) via two-dimensional cine phase-contrast magnetic resonance imaging and computational fluid dynamics. METHODS: In 18 patients with MMD and 10 healthy control subjects, phase-contrast magnetic resonance imaging was performed to quantify flow rate of main supplying arteries, including internal carotid arteries (ICAs) and vertebral arteries. Mean flow rate in these vessels was adopted as the patient-specific boundary condition for computational fluid dynamics simulation of the circle of Willis in MMD and control groups. Pressure drop in both ICAs and their difference, wall shear stress and secondary flow in the carotid siphon of ICAs, and flow rate and size of posterior communicating arteries (PComAs) were compared between MMD and control groups. Four patients with MMD underwent follow-up scans for longitudinal comparison. RESULTS: Phase-contrast magnetic resonance imaging data revealed significantly different flow rate in the left ICA and right vertebral arteries between MMD and control groups. Computational fluid dynamics simulation demonstrated similar wall shear stress and similar secondary flow of both ICAs but significantly higher pressure drop in left ICA, higher pressure drop difference between left ICA and right ICA, and higher flow rate in PComAs in patients with MMD compared with control subjects. Significantly increased size of left PComA in patients with MMD was also found. Follow-up results confirmed that the combination of pressure drop difference, flow rate, and size of PComAs can potentially assist long-term prognosis after surgery. CONCLUSIONS: Pressure drop difference, flow rate, and size of PComAs can be used to evaluate impairments in cerebrovascular reserve and indicate long-term prognosis in MMD.


Assuntos
Hemodinâmica , Imagem por Ressonância Magnética , Doença de Moyamoya/diagnóstico por imagem , Doença de Moyamoya/fisiopatologia , Adulto , Artérias/diagnóstico por imagem , Artérias/fisiopatologia , Simulação por Computador , Meios de Contraste , Feminino , Seguimentos , Humanos , Hidrodinâmica , Estudos Longitudinais , Masculino , Pessoa de Meia-Idade , Modelos Cardiovasculares , Modelos Neurológicos
19.
IEEE Trans Biomed Eng ; 65(12): 2751-2759, 2018 12.
Artigo em Inglês | MEDLINE | ID: mdl-29993429

RESUMO

Heartbeats based random binary sequences (RBSs) are the backbone for several security aspects in wireless body sensor networks (WBSNs). However, current heartbeats based methods require a lot of processing time (∼25-30 s) to generate 128-bit RBSs in real-time healthcare applications. In order to improve time efficiency, a biometric RBSs generation technique using interpulse intervals (IPIs) of heartbeats is developed in this study. The proposed technique incorporates a finite monotonic increasing sequences generation mechanism of IPIs and a cyclic block encoding procedure that extracts a high number of entropic bits from each IPI. To validate the proposed technique, 89 ECG recordings including 25 healthy individuals in a laboratory environment, 20 from MIT-BIH Arrhythmia Database, and 44 cardiac patients from the clinical environment are considered. By applying the proposed technique on the ECG signals, at most 16 random bits can be extracted from each heartbeat to generate 128-bit RBSs via concatenation of eight consecutive IPIs. And the randomness and distinctiveness of generated 128-bit RBSs are measured based on the National Institute of Standards and Technology statistical tests and hamming distance, respectively. From the experimental results, the generated 128-bit RBSs from both healthy subjects and patients can potentially be used as keys for encryption or entity identifiers to secure WBSNs. Moreover, the proposed approach is examined to be up to four times faster than the existing heartbeat-based RBSs generation schemes. Therefore, the developed technique necessitates less processing time (0-8 s) in real-time health monitoring scenarios to construct 128-bit RBSs in comparisons with current methods.


Assuntos
Eletrocardiografia/métodos , Frequência Cardíaca/fisiologia , Pulso Arterial/métodos , Processamento de Sinais Assistido por Computador , Tecnologia sem Fio , Adulto , Algoritmos , Humanos , Masculino
20.
Biomech Model Mechanobiol ; 17(6): 1581-1597, 2018 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-29982960

RESUMO

Computational fluid dynamics (CFD) is an increasingly used method for investigation of hemodynamic parameters and their alterations under pathological conditions, which are important indicators for diagnosis of cardiovascular disease. In hemodynamic simulation models, the employment of appropriate boundary conditions (BCs) determines the computational accuracy of the CFD simulation in comparison with pressure and velocity measurements. In this study, we have first assessed the influence of inlet boundary conditions on hemodynamic CFD simulations. We selected two typical patients suspected of carotid artery disease, with mild stenosis and severe stenosis. Both patients underwent digital subtraction angiography (DSA), magnetic resonance angiography, and the invasive pressure guide wire measured pressure profile. We have performed computational experiments to (1) study the hemodynamic simulation outcomes of distributions of wall shear stress, pressure, pressure gradient and (2) determine the differences in hemodynamic performances caused by inlet BCs derived from DSA and Womersley analytical solution. Our study has found that the difference is related to the severity of the stenosis; the greater the stenosis, the more the difference ensues. Further, in our study, the two typical subjects with invasively measured pressure profile and thirty subjects with ultrasound Doppler velocimeter (UDV) measurement served as the criteria to evaluate the hemodynamic outcomes of wall shear stress, pressure, pressure gradient and velocity due to different outlet BCs based on the Windkessel model, structured-tree model, and fully developed flow model. According to the pressure profiles, the fully developed model appeared to have more fluctuations compared with the other two models. The Windkessel model had more singularities before convergence. The three outlet BCs models also showed good correlation with the UDV measurement, while the Windkessel model appeared to be slightly better ([Formula: see text]). The structured-tree model was seen to have the best performance in terms of available computational cost and accuracy. The results of our numerical simulation and the good correlation with the computed pressure and velocity with their measurements have highlighted the effectiveness of CFD simulation in patient-specific human carotid artery with suspected stenosis.


Assuntos
Artérias Carótidas/fisiopatologia , Doenças das Artérias Carótidas/fisiopatologia , Adulto , Idoso , Angiografia , Aterosclerose/fisiopatologia , Velocidade do Fluxo Sanguíneo , Simulação por Computador , Constrição Patológica , Hemodinâmica , Humanos , Hidrodinâmica , Imageamento Tridimensional , Angiografia por Ressonância Magnética , Pessoa de Meia-Idade , Modelos Cardiovasculares , Pressão , Reprodutibilidade dos Testes , Estresse Mecânico
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