Symposium on Medical Information Processing and Analysis最新文献_第3页

Effects and methodology for grid subdivision of CT-based texture for unsupervised clustering 基于ct的纹理网格细分在无监督聚类中的效果与方法

Symposium on Medical Information Processing and Analysis Pub Date : 2023-03-06 DOI: 10.1117/12.2670140

K. M. Cunanan, B. Varghese, Yenlin Lee, Raghda Abouelnaga, Ramin Eghtesadi, D. Hwang, V. Duddalwar, S. Cen

{"title":"Effects and methodology for grid subdivision of CT-based texture for unsupervised clustering","authors":"K. M. Cunanan, B. Varghese, Yenlin Lee, Raghda Abouelnaga, Ramin Eghtesadi, D. Hwang, V. Duddalwar, S. Cen","doi":"10.1117/12.2670140","DOIUrl":"https://doi.org/10.1117/12.2670140","url":null,"abstract":"t-Distributed Stochastic Neighbor Embedding (t-SNE) and k-means have been increasingly utilized for dimension reduction and graphical illustration in medical imaging (e.g., CT) informatics. Mapping a grid network onto a slide is a prerequisite for implementing cluster analysis. Traditionally, the performance of cluster analysis is driven by hyperparameters, however, grid size which also affects performance is often set arbitrarily. In this study, we evaluated the effect of varying grid sizes, perplexity and learning rate hyperparameters for unsupervised clustering using CT images of renal masses. We investigated the impact of grid size to cluster analysis. The number of clusters was determined by Gap-statistics. The grid size selections were 2x2, 4x4, 5x5, and 8x8. The results showed that the number of output clusters increased with decreasing grid sizes from 8x8 to 4x4. However, when grid size reached 2x2, the model yielded the same cluster number as 8x8. This finding was consistent across different hyperparameter settings. Additional analyses were conducted to understand the nesting structure between the cluster membership (the mutually exclusive cluster number assigned to each grid in a cluster analysis) from large (8x8) grid and small (2x2) grid, although both grid size selections yielded the same number of clusters. We report that the cluster membership between large grid and small grid is only partially overlaid. This suggests that additional pattern/information is detected by using the small grid. In conclusion, the grid size should be treated as another hyperparameter when using unsupervised clustering methods for pattern recognition in medical imaging analysis.","PeriodicalId":147201,"journal":{"name":"Symposium on Medical Information Processing and Analysis","volume":"12567 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129872084","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Body location embedded 3D U-Net (BLE-U-Net) for ovarian cancer ascites segmentation on CT scans 身体位置嵌入3D U-Net (BLE-U-Net)用于卵巢癌CT扫描的腹水分割

Symposium on Medical Information Processing and Analysis Pub Date : 2023-03-06 DOI: 10.1117/12.2669783

M. Nag, Jianfei Liu, Liangchen Liu, Seung Yeon Shin, Sungwon Lee, Jung-Min Lee, R. Summers

{"title":"Body location embedded 3D U-Net (BLE-U-Net) for ovarian cancer ascites segmentation on CT scans","authors":"M. Nag, Jianfei Liu, Liangchen Liu, Seung Yeon Shin, Sungwon Lee, Jung-Min Lee, R. Summers","doi":"10.1117/12.2669783","DOIUrl":"https://doi.org/10.1117/12.2669783","url":null,"abstract":"Ascites is often regarded as the hallmark of advanced ovarian cancer, which is the most lethal gynecologic malignancy. Ascites segmentation contributes to track the progress of ovarian cancer development by providing accurate ascites measurement, which can effectively guide subsequent treatment and potentially reduce the mortality. Segmentation of ascites is challenging due to the presence of iso-intense fluids such as bile, urine, etc., near the ascites region. In this work we propose a novel 3D U-Net segmentation method called body location embedded U-Net (BLE-U-Net) that integrates anatomical location information with the segmentation process. BLE-U-Net incorporates body part regression to predict the approximate anatomical location of each CT slice along the z- axis. The regression scores are discretized to indicate different body regions and embedded into a modified 3D U-Net to improve the ascites segmentation. Twenty contrast-enhanced body CT scans were used to evaluate the proposed method. Dice coefficients of 38 ±10 and 65 ±06 were achieved for a conventional 3D U-Net and BLE-U-Net, respectively (with t-test p <0.05). Volumes of segmented ascites were 0.51±0.74 and 0.57±0.85 liters for each method where the ground-truth volume was 0.58±0.84 liters. These results suggest that the embedded location information is the key factor to improve the ascites segmentation, which could potentially benefit ovarian cancer diagnosis and treatment.","PeriodicalId":147201,"journal":{"name":"Symposium on Medical Information Processing and Analysis","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125763007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Multi-platform fractal dimension analysis of renal masses from multiphase contrast-enhanced computed tomography as markers for tumor subtyping 多期增强计算机断层扫描肾肿块的多平台分形分析作为肿瘤亚型标记

Symposium on Medical Information Processing and Analysis Pub Date : 2023-03-06 DOI: 10.1117/12.2670379

T. Yin, T. Zhao, S. Cen, X. Lei, D. Hwang, S. Hajian, M. Desai, I. Gill, V. Duddalwar, B. Varghese

引用次数: 1

Source separation for single channel thoracic cardio-respiratory sounds applying Non-negative Matrix Factorization (NMF) using a focused strategy on heart sound positions 应用非负矩阵分解(NMF)分离心音位置的单通道胸椎心肺音源

Symposium on Medical Information Processing and Analysis Pub Date : 2023-03-06 DOI: 10.1117/12.2669781

C. M. Escobar Arce, Patricio de la Cuadra Banderas

{"title":"Source separation for single channel thoracic cardio-respiratory sounds applying Non-negative Matrix Factorization (NMF) using a focused strategy on heart sound positions","authors":"C. M. Escobar Arce, Patricio de la Cuadra Banderas","doi":"10.1117/12.2669781","DOIUrl":"https://doi.org/10.1117/12.2669781","url":null,"abstract":"Auscultation using stethoscopes allows the diagnosis of respiratory and cardiac diseases. However, these sounds interfere with each other both in time and frequency. In the case of recording heart sounds, it is possible to ask the patient to stop their breathing to perform auscultation and obtain a pure heart sound. But, in the case of lung sounds it is impossible to do the same. In this paper, a source separation method based on Non-negative Matrix Factorization (NMF) is used to decompose a signal into different components. The method proposed uses information from the estimated lung sound to reinsert the segments of interest into the original signal. The objective of this approximation is not to distort the segments of pure respiratory sound (free of heart sound). This method is compared to a base case of NMF decomposition on the raw signal. Three criteria for classifying the components based on the literature are also proposed, which will allow to indicate which component corresponds to each sound. The results were evaluated using temporal and spectral correlations, mean square error (MSE) and signal to distortion ratio (SDR) between the original respiratory signal and the respiratory signal estimated through the algorithm. It is shown that the best approximation is the NMF decomposition on the entire signal & replacing segments under different parameter variations.","PeriodicalId":147201,"journal":{"name":"Symposium on Medical Information Processing and Analysis","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115204140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CEM radiomics for distinguishing lesion from background parenchymal enhancement in patients with invasive breast cancer CEM放射组学用于区分浸润性乳腺癌患者的病灶与背景实质增强

Symposium on Medical Information Processing and Analysis Pub Date : 2023-03-06 DOI: 10.1117/12.2670371

B. Varghese, Melissa Perkins, S. Cen, X. Lei, Jacquelyn Fields, J. Jamie, B. Desai, Mariam Thomas, D. Hwang, Sandy C Lee, L. Larsen, Mary W. Yamashita

{"title":"CEM radiomics for distinguishing lesion from background parenchymal enhancement in patients with invasive breast cancer","authors":"B. Varghese, Melissa Perkins, S. Cen, X. Lei, Jacquelyn Fields, J. Jamie, B. Desai, Mariam Thomas, D. Hwang, Sandy C Lee, L. Larsen, Mary W. Yamashita","doi":"10.1117/12.2670371","DOIUrl":"https://doi.org/10.1117/12.2670371","url":null,"abstract":"In this IRB approved retrospective study 41 women with biopsy-proven invasive breast cancers (IBC) were imaged using contrast-enhanced mammography (CEM), prior to any treatment. Size-matched regions of interest (ROIs) were manually contoured by an experienced breast radiologist on the CEM capturing the breast lesion and breast parenchymal enhancement (BPE), respectively. Radiomics analysis was performed using LifEx software and 109 radiomics metrics spanning 6 different texture families were extracted from each ROI. Predictive models of lesion malignancy were developed using multiple classifiers and used to subclassify breast cancers based on their hormone receptor status. The 10- fold cross validation was used to construct the decision classifier and performance was assessed. CEM radiomics models based on Random Forest, Real Adaboost, and ElasticNet classifiers achieved an AUC of 0.83, 0.82 and 0.74, respectively in discriminating malignant breast lesions from varying amounts of BPE. Accounting for the varying levels of BPE, revealed a reduction in AUC-based prediction of lesion vs. BPE as the qualitative assessment of BPE increased from minimal to moderate (AUCs of 0.89 vs 0.74). Further analyses of the IBC based on their hormone receptor status showed that triple negative breast lesions showed statistically significant differences in multiple radiomics metrics compared to ER+ PR+ HER2- and HER2+. The predicted probability of the radiomics model was significantly different across three receptor-based subtypes and between high and low nuclear grade breast cancers. CEM Radiomics demonstrated good discrimination (AUC>0.8) of malignant breast lesions despite varying BPE levels and supports breast lesion subtyping.","PeriodicalId":147201,"journal":{"name":"Symposium on Medical Information Processing and Analysis","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127241783","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Early processing of unattended emotional faces increases the brain response to attended emotional expressions: an SSVEP study 一项SSVEP研究表明，早期处理未被关注的情绪面孔增加了大脑对被关注的情绪表达的反应

Symposium on Medical Information Processing and Analysis Pub Date : 2023-03-06 DOI: 10.1117/12.2669739

Vanessa Hadid, Michèle W. MacLean, Caroline Grand-Maître, Julie Dandrimont, Marie-Charlotte Higgins, Simon Faghel-Soubeyrand, F. Lepore

{"title":"Early processing of unattended emotional faces increases the brain response to attended emotional expressions: an SSVEP study","authors":"Vanessa Hadid, Michèle W. MacLean, Caroline Grand-Maître, Julie Dandrimont, Marie-Charlotte Higgins, Simon Faghel-Soubeyrand, F. Lepore","doi":"10.1117/12.2669739","DOIUrl":"https://doi.org/10.1117/12.2669739","url":null,"abstract":"The brain has the ability to evaluate unattended social information, such as facial expressions, and reassign attentional resources to specific relevant features. Two neuronal mechanisms could account for such facial emotional processing: one slow and accurate system that can be measured around 170 ms and one fast and imprecise system that is triggered around 90 ms which could support early negative emotional processing for automatic/unattended and peripheral stimulation. Evidence that these mechanisms exist for positive affective processing is scarce. The present study investigated the neural correlates of unattended negative and positive emotional processing using the rapid presentation of unilateral and bilateral peripheral facial expressions. Hence, we measured the electrophysiological correlates of unattended fear, happy and neutral faces presented in the left and right hemifields of neurotypical individuals using a frequency tagging paradigm and electroencephalography. Frequency stimulations of 5.8 Hz and 11 Hz were chosen to induce Steady-State Visual Evoked Potential (SSVEP) occurring at 170 ms and 90 ms, respectively. The SSVEP amplitudes showed that unattended positive and negative information in the periphery was processed at early stages and increased the brain's response to attended salient emotional stimuli in posterior visual regions. These results suggest that emotional stimuli presented outside the attentional focus elicit increased brain activity, particularly in posterior regions which could be altered in disorders of social recognition.","PeriodicalId":147201,"journal":{"name":"Symposium on Medical Information Processing and Analysis","volume":"153 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124271911","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Building brain tumor segmentation networks with user-assisted filter estimation and selection 基于用户辅助滤波估计和选择的脑肿瘤分割网络构建

Symposium on Medical Information Processing and Analysis Pub Date : 2023-03-06 DOI: 10.1117/12.2669770

Matheus A. Cerqueira, F. Sprenger, B. C. Teixeira, A. Falcão

{"title":"Building brain tumor segmentation networks with user-assisted filter estimation and selection","authors":"Matheus A. Cerqueira, F. Sprenger, B. C. Teixeira, A. Falcão","doi":"10.1117/12.2669770","DOIUrl":"https://doi.org/10.1117/12.2669770","url":null,"abstract":"Brain tumor image segmentation is a challenging research topic in which deep-learning models have presented the best results. However, the traditional way of training those models from many pre-annotated images leaves several unanswered questions. Hence methodologies, such as Feature Learning from Image Markers (FLIM), have involved an expert in the learning loop to reduce human effort in data annotation and build models sufficiently deep for a given problem. FLIM has been successfully used to create encoders, estimating the filters of all convolutional layers from patches centered at marker voxels. In this work, we present Multi-Step (MS) FLIM – a user-assisted approach to estimating and selecting the most relevant filters from multiple FLIM executions. MS-FLIM is used only for the first convolutional layer, and the results already indicate improvement over FLIM. For evaluation, we build a simple U-shaped encoder-decoder network, named sU-Net, for glioblastoma segmentation using T1Gd and FLAIR MRI scans, varying the encoder’s training method, using FLIM, MS-FLIM, and backpropagation algorithm. Also, we compared these sU-Nets with two State-Of-The-Art (SOTA) deep-learning models using two datasets. The results show that the sU-Net based on MS-FLIM outperforms the other training methods and achieves effectiveness within the standard deviations of the SOTA models.","PeriodicalId":147201,"journal":{"name":"Symposium on Medical Information Processing and Analysis","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124280173","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

VENTSEG: efficient open source framework for ventricular segmentation VENTSEG:高效的心室分割开源框架

Symposium on Medical Information Processing and Analysis Pub Date : 2023-03-06 DOI: 10.1117/12.2669932

Alejandro León, Rodrigo Herrera, Jesús Urbina, R. Salas, S. Uribe, J. Sotelo

{"title":"VENTSEG: efficient open source framework for ventricular segmentation","authors":"Alejandro León, Rodrigo Herrera, Jesús Urbina, R. Salas, S. Uribe, J. Sotelo","doi":"10.1117/12.2669932","DOIUrl":"https://doi.org/10.1117/12.2669932","url":null,"abstract":"Despite advances in deep learning methods aimed at cardiac ventricular segmentation, most algorithms have drawbacks due to low prediction accuracy with images from different MR scans to those trained. It leads to a process that requires time-consuming correction by technicians or specialists. The time in this process is significant mainly due to the large number of image sets to be processed. The lack of description of the algorithms has not allowed repeatability, while commercial software is difficult to access for clinical use or research. However, in cardiac segmentation research, several solutions have already been proposed. This paper presents an opensource cardiac functionality segmentation and evaluation framework, which contemplates a diverse database for network training, a multi domain network architecture that allows model generalization, and pre-and postprocessing algorithms that improve prediction results. The prediction evaluation of the framework shows that Ventseg is 3.66% superior to the trained model and the similarity percentages in the tested MR scores are over 84%. On the other hand, the inter-observer variability analysis, with anonymized data, shows in the different metrics that Ventseg is on par with cardiac segmentation specialists. Finally, the efficiency calculated in an intra-observer test indicates that our framework reduces manual segmentation time by approximately 80%.","PeriodicalId":147201,"journal":{"name":"Symposium on Medical Information Processing and Analysis","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115799176","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An automatic method for detection of evoked events in the brain during spinal cord stimulation 一种自动检测脊髓刺激期间大脑中诱发事件的方法

Symposium on Medical Information Processing and Analysis Pub Date : 2023-03-06 DOI: 10.1117/12.2669787

Nicolás Vásquez-Tobar, Marion Caceres, Rómulo Fuentes, Leonel E. Medina

{"title":"An automatic method for detection of evoked events in the brain during spinal cord stimulation","authors":"Nicolás Vásquez-Tobar, Marion Caceres, Rómulo Fuentes, Leonel E. Medina","doi":"10.1117/12.2669787","DOIUrl":"https://doi.org/10.1117/12.2669787","url":null,"abstract":"Parkinson’s disease (PD) is a neurodegenerative brain disorder that mainly affects the elderly population and currently has no cure, only palliative treatments. Recently, spinal cord stimulation (SCS) showed great promise in the treatment of certain PD symptoms. In this semi-invasive technique, pulses of current are delivered to the spinal cord via electrodes implanted in the epidural space. However, the effects of SCS in the brain are poorly understood. In this work, we developed a method for detection of electrophysiological events in the brain evoked by SCS pulses, and analyzed the latency of such events under different experimental conditions. We performed in vivo recordings of local field potentials using 64 microelectrodes implanted in different brain areas of a rat model of PD, and during active and inactive states, and with and without administration of L-dopa. The signals were pre-processed and divided into windows centered around the stimulation pulses, in which we detected the evoked events using two approaches: the Hampel identifier and the wavelet transform. Next, we measured the latency of the response evoked in the brain with respect to the applied pulse. The Hampel method detected events in about 31% of trials, and the wavelet method in about 42% of trials. In addition, we found that movement had a statistical significant effect on the measured latency but only when L-dopa was administered to the animal. The differences in latency suggest that there may be a trajectory of neuronal activation in the brain in response of SCS. Our results may have implications in the design of more effective SCS strategies for the treatment of PD.","PeriodicalId":147201,"journal":{"name":"Symposium on Medical Information Processing and Analysis","volume":"310 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124403413","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Automatic detection of lung ultrasound artifacts using a deep neural networks approach 基于深度神经网络的肺超声伪影自动检测

Symposium on Medical Information Processing and Analysis Pub Date : 2023-03-06 DOI: 10.1117/12.2670456

C. Vásquez, Stefano Enrique Romero, Jose Zapana, Jesus Paucar, T. Marini, B. Castañeda

{"title":"Automatic detection of lung ultrasound artifacts using a deep neural networks approach","authors":"C. Vásquez, Stefano Enrique Romero, Jose Zapana, Jesus Paucar, T. Marini, B. Castañeda","doi":"10.1117/12.2670456","DOIUrl":"https://doi.org/10.1117/12.2670456","url":null,"abstract":"The COVID-19 pandemic has challenged many of the healthcare systems around the world. Many patients who have been hospitalized due to this disease develop lung damage. In low and middle-income countries, people living in rural and remote areas have very limited access to adequate health care. Ultrasound is a safe, portable and accessible alternative; however, it has limitations such as being operator-dependent and requiring a trained professional. The use of lung ultrasound volume sweep imaging is a potential solution for this lack of physicians. In order to support this protocol, image processing together with machine learning is a potential methodology for an automatic lung damage screening system. In this paper we present an automatic detection of lung ultrasound artifacts using a Deep Neural Network, identifying clinical relevant artifacts such as pleural and A-lines contained in the ultrasound examination taken as part of the clinical screening in patients with suspected lung damage. The model achieved encouraging preliminary results such as sensitivity of 94% , specificity of 81%, and accuracy of 89% to identify the presence of A-lines. Finally, the present study could result in an alternative solution for an operator-independent lung damage screening in rural areas, leading to the integration of AI-based technology as a complementary tool for healthcare professionals.","PeriodicalId":147201,"journal":{"name":"Symposium on Medical Information Processing and Analysis","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133512680","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1