Computerized Medical Imaging and Graphics最新文献

{"title":"A Parkinson’s disease-related nuclei segmentation network based on CNN-Transformer interleaved encoder with feature fusion","authors":"Hongyi Chen ,&nbsp;Junyan Fu ,&nbsp;Xiao Liu ,&nbsp;Zhiji Zheng ,&nbsp;Xiao Luo ,&nbsp;Kun Zhou ,&nbsp;Zhijian Xu ,&nbsp;Daoying Geng","doi":"10.1016/j.compmedimag.2024.102465","DOIUrl":"10.1016/j.compmedimag.2024.102465","url":null,"abstract":"<div><div>Automatic segmentation of Parkinson’s disease (PD) related deep gray matter (DGM) nuclei based on brain magnetic resonance imaging (MRI) is significant in assisting the diagnosis of PD. However, due to the degenerative-induced changes in appearance, low tissue contrast, and tiny DGM nuclei size in elders’ brain MRI images, many existing segmentation models are limited in the application. To address these challenges, this paper proposes a PD-related DGM nuclei segmentation network to provide precise prior knowledge for aiding diagnosis PD. The encoder of network is designed as an alternating encoding structure where the convolutional neural network (CNN) captures spatial and depth texture features, while the Transformer complements global position information between DGM nuclei. Moreover, we propose a cascaded channel-spatial-wise block to fuse features extracted by the CNN and Transformer, thereby achieving more precise DGM nuclei segmentation. The decoder incorporates a symmetrical boundary attention module, leveraging the symmetrical structures of bilateral nuclei regions by constructing signed distance maps for symmetric differences, which optimizes segmentation boundaries. Furthermore, we employ a dynamic adaptive region of interests weighted Dice loss to enhance sensitivity towards smaller structures, thereby improving segmentation accuracy. In qualitative analysis, our method achieved optimal average values for PD-related DGM nuclei (DSC: 0.854, IOU: 0.750, HD95: 1.691 mm, ASD: 0.195 mm). Experiments conducted on multi-center clinical datasets and public datasets demonstrate the good generalizability of the proposed method. Furthermore, a volumetric analysis of segmentation results reveals significant differences between HCs and PDs. Our method holds promise for assisting clinicians in the rapid and accurate diagnosis of PD, offering a practical method for the imaging analysis of neurodegenerative diseases.</div></div>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"118 ","pages":"Article 102465"},"PeriodicalIF":5.4,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142700984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Retinal structure guidance-and-adaption network for early Parkinson’s disease recognition based on OCT images","authors":"Hanfeng Shi ,&nbsp;Jiaqi Wei ,&nbsp;Richu Jin ,&nbsp;Jiaxin Peng ,&nbsp;Xingyue Wang ,&nbsp;Yan Hu ,&nbsp;Xiaoqing Zhang ,&nbsp;Jiang Liu","doi":"10.1016/j.compmedimag.2024.102463","DOIUrl":"10.1016/j.compmedimag.2024.102463","url":null,"abstract":"<div><div>Parkinson’s disease (PD) is a leading neurodegenerative disease globally. Precise and objective PD diagnosis is significant for early intervention and treatment. Recent studies have shown significant correlations between retinal structure information and PD based on optical coherence tomography (OCT) images, providing another potential means for early PD recognition. However, how to exploit the retinal structure information (e.g., thickness and mean intensity) from different retinal layers to improve PD recognition performance has not been studied before. Motivated by the above observations, we first propose a structural prior knowledge extraction (SPKE) module to obtain the retinal structure feature maps; then, we develop a structure-guided-and-adaption attention (SGDA) module to fully leverage the potential of different retinal layers based on the extracted retinal structure feature maps. By embedding SPKE and SGDA modules at the low stage of deep neural networks (DNNs), a retinal structure-guided-and-adaption network (RSGA-Net) is constructed for early PD recognition based on OCT images. The extensive experiments on a clinical OCT-PD dataset demonstrate the superiority of RSGA-Net over state-of-the-art methods. Additionally, we provide a visual analysis to explain how retinal structure information affects the decision-making process of DNNs.</div></div>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"118 ","pages":"Article 102463"},"PeriodicalIF":5.4,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142722393","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploratory analysis of Type B Aortic Dissection (TBAD) segmentation in 2D CTA images using various kernels","authors":"Ayman Abaid ,&nbsp;Srinivas Ilancheran ,&nbsp;Talha Iqbal ,&nbsp;Niamh Hynes ,&nbsp;Ihsan Ullah","doi":"10.1016/j.compmedimag.2024.102460","DOIUrl":"10.1016/j.compmedimag.2024.102460","url":null,"abstract":"<div><div>Type-B Aortic Dissection is a rare but fatal cardiovascular disease characterized by a tear in the inner layer of the aorta, affecting 3.5 per 100,000 individuals annually. In this work, we explore the feasibility of leveraging two-dimensional Convolutional Neural Network (CNN) models to perform accurate slice-by-slice segmentation of true lumen, false lumen and false lumen thrombus in Computed Tomography Angiography images. The study performed an exploratory analysis of three 2D U-Net models: the baseline 2D U-Net, a variant of U-Net with atrous convolutions, and a U-Net with a custom layer featuring a position-oriented, partially shared weighting scheme kernel. These models were trained and benchmarked against a state-of-the-art baseline 3D U-Net model. Overall, our U-Net with the VGG19 encoder architecture achieved the best performance score among all other models, with a mean Dice score of 80.48% and an IoU score of 72.93%. The segmentation results were also compared with the Segment Anything Model (SAM) and the UniverSeg models. Our findings indicate that our 2D U-Net models excel in false lumen and true lumen segmentation accuracy while achieving lower false lumen thrombus segmentation accuracy compared to the state-of-the-art 3D U-Net model. The study findings highlight the complexities involved in developing segmentation models, especially for cardiovascular medical images, and emphasize the importance of developing lightweight models for real-time decision-making to improve overall patient care.</div></div>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"118 ","pages":"Article 102460"},"PeriodicalIF":5.4,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142693784","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring transformer reliability in clinically significant prostate cancer segmentation: A comprehensive in-depth investigation","authors":"Gustavo Andrade-Miranda ,&nbsp;Pedro Soto Vega ,&nbsp;Kamilia Taguelmimt ,&nbsp;Hong-Phuong Dang ,&nbsp;Dimitris Visvikis ,&nbsp;Julien Bert","doi":"10.1016/j.compmedimag.2024.102459","DOIUrl":"10.1016/j.compmedimag.2024.102459","url":null,"abstract":"<div><div>Despite the growing prominence of transformers in medical image segmentation, their application to clinically significant prostate cancer (csPCa) has been overlooked. Minimal attention has been paid to domain shift analysis and uncertainty assessment, critical for safely implementing computer-aided diagnosis (CAD) systems. Domain shift in medical imagery refers to differences between the data used to train a model and the data evaluated later, arising from variations in imaging equipment, protocols, patient populations, and acquisition noise. While recent models enhance in-domain performance, areas such as robustness and uncertainty estimation in out-of-domain distributions have received limited investigation, creating indecisiveness about model reliability. In contrast, our study addresses csPCa at voxel, lesion, and image levels, investigating models from traditional U-Net to cutting-edge transformers. We focus on four key points: robustness, calibration, out-of-distribution (OOD), and misclassification detection (MD). Findings show that transformer-based models exhibit enhanced robustness at image and lesion levels, both in and out of domain. However, this improvement is not fully translated to the voxel level, where Convolutional Neural Networks (CNNs) outperform in most robustness metrics. Regarding uncertainty, hybrid transformers and transformer encoders performed better, but this trend depends on misclassification or out-of-distribution tasks.</div></div>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"118 ","pages":"Article 102459"},"PeriodicalIF":5.4,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142683103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"NACNet: A histology context-aware transformer graph convolution network for predicting treatment response to neoadjuvant chemotherapy in Triple Negative Breast Cancer","authors":"Qiang Li ,&nbsp;George Teodoro ,&nbsp;Yi Jiang ,&nbsp;Jun Kong","doi":"10.1016/j.compmedimag.2024.102467","DOIUrl":"10.1016/j.compmedimag.2024.102467","url":null,"abstract":"<div><div>Neoadjuvant chemotherapy (NAC) response prediction for triple negative breast cancer (TNBC) patients is a challenging task clinically as it requires understanding complex histology interactions within the tumor microenvironment (TME). Digital whole slide images (WSIs) capture detailed tissue information, but their giga-pixel size necessitates computational methods based on multiple instance learning, which typically analyze small, isolated image tiles without the spatial context of the TME. To address this limitation and incorporate TME spatial histology interactions in predicting NAC response for TNBC patients, we developed a histology context-aware transformer graph convolution network (NACNet). Our deep learning method identifies the histopathological labels on individual image tiles from WSIs, constructs a spatial TME graph, and represents each node with features derived from tissue texture and social network analysis. It predicts NAC response using a transformer graph convolution network model enhanced with graph isomorphism network layers. We evaluate our method with WSIs of a cohort of TNBC patient (N=105) and compared its performance with multiple state-of-the-art machine learning and deep learning models, including both graph and non-graph approaches. Our NACNet achieves 90.0% accuracy, 96.0% sensitivity, 88.0% specificity, and an AUC of 0.82, through eight-fold cross-validation, outperforming baseline models. These comprehensive experimental results suggest that NACNet holds strong potential for stratifying TNBC patients by NAC response, thereby helping to prevent overtreatment, improve patient quality of life, reduce treatment cost, and enhance clinical outcomes, marking an important advancement toward personalized breast cancer treatment.</div></div>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"118 ","pages":"Article 102467"},"PeriodicalIF":5.4,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142700926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Self-supervised multi-modal feature fusion for predicting early recurrence of hepatocellular carcinoma","authors":"Sen Wang ,&nbsp;Ying Zhao ,&nbsp;Jiayi Li ,&nbsp;Zongmin Yi ,&nbsp;Jun Li ,&nbsp;Can Zuo ,&nbsp;Yu Yao ,&nbsp;Ailian Liu","doi":"10.1016/j.compmedimag.2024.102457","DOIUrl":"10.1016/j.compmedimag.2024.102457","url":null,"abstract":"<div><div>Surgical resection stands as the primary treatment option for early-stage hepatocellular carcinoma (HCC) patients. Postoperative early recurrence (ER) is a significant factor contributing to the mortality of HCC patients. Therefore, accurately predicting the risk of ER after curative resection is crucial for clinical decision-making and improving patient prognosis. This study leverages a self-supervised multi-modal feature fusion approach, combining multi-phase MRI and clinical features, to predict ER of HCC. Specifically, we utilized attention mechanisms to suppress redundant features, enabling efficient extraction and fusion of multi-phase features. Through self-supervised learning (SSL), we pretrained an encoder on our dataset to extract more generalizable feature representations. Finally, we achieved effective multi-modal information fusion via attention modules. To enhance explainability, we employed Score-CAM to visualize the key regions influencing the model’s predictions. We evaluated the effectiveness of the proposed method on our dataset and found that predictions based on multi-phase feature fusion outperformed those based on single-phase features. Additionally, predictions based on multi-modal feature fusion were superior to those based on single-modal features.</div></div>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"118 ","pages":"Article 102457"},"PeriodicalIF":5.4,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142689549","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DSIFNet: Implicit feature network for nasal cavity and vestibule segmentation from 3D head CT","authors":"Yi Lu ,&nbsp;Hongjian Gao ,&nbsp;Jikuan Qiu ,&nbsp;Zihan Qiu ,&nbsp;Junxiu Liu ,&nbsp;Xiangzhi Bai","doi":"10.1016/j.compmedimag.2024.102462","DOIUrl":"10.1016/j.compmedimag.2024.102462","url":null,"abstract":"<div><div>This study is dedicated to accurately segment the nasal cavity and its intricate internal anatomy from head CT images, which is critical for understanding nasal physiology, diagnosing diseases, and planning surgeries. Nasal cavity and it’s anatomical structures such as the sinuses, and vestibule exhibit significant scale differences, with complex shapes and variable microstructures. These features require the segmentation method to have strong cross-scale feature extraction capabilities. To effectively address this challenge, we propose an image segmentation network named the Deeply Supervised Implicit Feature Network (DSIFNet). This network uniquely incorporates an Implicit Feature Function Module Guided by Local and Global Positional Information (LGPI-IFF), enabling effective fusion of features across scales and enhancing the network's ability to recognize details and overall structures. Additionally, we introduce a deep supervision mechanism based on implicit feature functions in the network's decoding phase, optimizing the utilization of multi-scale feature information, thus improving segmentation precision and detail representation. Furthermore, we constructed a dataset comprising 7116 CT volumes (including 1,292,508 slices) and implemented PixPro-based self-supervised pretraining to utilize unlabeled data for enhanced feature extraction. Our tests on nasal cavity and vestibule segmentation, conducted on a dataset comprising 128 head CT volumes (including 34,006 slices), demonstrate the robustness and superior performance of proposed method, achieving leading results across multiple segmentation metrics.</div></div>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"118 ","pages":"Article 102462"},"PeriodicalIF":5.4,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142656920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AFSegNet: few-shot 3D ankle-foot bone segmentation via hierarchical feature distillation and multi-scale attention and fusion","authors":"Yuan Huang ,&nbsp;Sven A. Holcombe ,&nbsp;Stewart C. Wang ,&nbsp;Jisi Tang","doi":"10.1016/j.compmedimag.2024.102456","DOIUrl":"10.1016/j.compmedimag.2024.102456","url":null,"abstract":"<div><div>Accurate segmentation of ankle and foot bones from CT scans is essential for morphological analysis. Ankle and foot bone segmentation challenges due to the blurred bone boundaries, narrow inter-bone gaps, gaps in the cortical shell, and uneven spongy bone textures. Our study endeavors to create a deep learning framework that harnesses advantages of 3D deep learning and tackles the hurdles in accurately segmenting ankle and foot bones from clinical CT scans. A few-shot framework AFSegNet is proposed considering the computational cost, which comprises three 3D deep-learning networks adhering to the principles of progressing from simple to complex tasks and network structures. Specifically, a shallow network first over-segments the foreground, and along with the foreground ground truth are used to supervise a subsequent network to detect the over-segmented regions, which are overwhelmingly inter-bone gaps. The foreground and inter-bone gap probability map are then input into a network with multi-scale attentions and feature fusion, a loss function combining region-, boundary-, and topology-based terms to get the fine-level bone segmentation. AFSegNet is applied to the 16-class segmentation task utilizing 123 in-house CT scans, which only requires a GPU with 24 GB memory since the three sub-networks can be successively and individually trained. AFSegNet achieves a Dice of 0.953 and average surface distance of 0.207. The ablation study and comparison with two basic state-of-the-art networks indicates the effectiveness of the progressively distilled features, attention and feature fusion modules, and hybrid loss functions, with the mean surface distance error decreased up to 50 %.</div></div>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"118 ","pages":"Article 102456"},"PeriodicalIF":5.4,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142592954","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"VLFATRollout: Fully transformer-based classifier for retinal OCT volumes","authors":"Marzieh Oghbaie ,&nbsp;Teresa Araújo ,&nbsp;Ursula Schmidt-Erfurth ,&nbsp;Hrvoje Bogunović","doi":"10.1016/j.compmedimag.2024.102452","DOIUrl":"10.1016/j.compmedimag.2024.102452","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Despite the promising capabilities of 3D transformer architectures in video analysis, their application to high-resolution 3D medical volumes encounters several challenges. One major limitation is the high number of 3D patches, which reduces the efficiency of the global self-attention mechanisms of transformers. Additionally, background information can distract vision transformers from focusing on crucial areas of the input image, thereby introducing noise into the final representation. Moreover, the variability in the number of slices per volume complicates the development of models capable of processing input volumes of any resolution while simple solutions like subsampling may risk losing essential diagnostic details.</div></div><div><h3>Methods:</h3><div>To address these challenges, we introduce an end-to-end transformer-based framework, variable length feature aggregator transformer rollout (VLFATRollout), to classify volumetric data. The proposed VLFATRollout enjoys several merits. First, the proposed VLFATRollout can effectively mine slice-level fore-background information with the help of transformer’s attention matrices. Second, randomization of volume-wise resolution (i.e. the number of slices) during training enhances the learning capacity of the learnable positional embedding (PE) assigned to each volume slice. This technique allows the PEs to generalize across neighboring slices, facilitating the handling of high-resolution volumes at the test time.</div></div><div><h3>Results:</h3><div>VLFATRollout was thoroughly tested on the retinal optical coherence tomography (OCT) volume classification task, demonstrating a notable average improvement of 5.47% in balanced accuracy over the leading convolutional models for a 5-class diagnostic task. These results emphasize the effectiveness of our framework in enhancing slice-level representation and its adaptability across different volume resolutions, paving the way for advanced transformer applications in medical image analysis. The code is available at <span><span>https://github.com/marziehoghbaie/VLFATRollout/</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"118 ","pages":"Article 102452"},"PeriodicalIF":5.4,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142570320","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"WISE: Efficient WSI selection for active learning in histopathology","authors":"Hyeongu Kang ,&nbsp;Mujin Kim ,&nbsp;Young Sin Ko ,&nbsp;Yesung Cho ,&nbsp;Mun Yong Yi","doi":"10.1016/j.compmedimag.2024.102455","DOIUrl":"10.1016/j.compmedimag.2024.102455","url":null,"abstract":"<div><div>Deep neural network (DNN) models have been applied to a wide variety of medical image analysis tasks, often with the successful performance outcomes that match those of medical doctors. However, given that even minor errors in a model can impact patients’ life, it is critical that these models are continuously improved. Hence, active learning (AL) has garnered attention as an effective and sustainable strategy for enhancing DNN models for the medical domain. Extant AL research in histopathology has primarily focused on patch datasets derived from whole-slide images (WSIs), a standard form of cancer diagnostic images obtained from a high-resolution scanner. However, this approach has failed to address the selection of WSIs, which can impede the performance improvement of deep learning models and increase the number of WSIs needed to achieve the target performance. This study introduces a WSI-level AL method, termed WSI-informative selection (WISE). WISE is designed to select informative WSIs using a newly formulated WSI-level class distance metric. This method aims to identify diverse and uncertain cases of WSIs, thereby contributing to model performance enhancement. WISE demonstrates state-of-the-art performance across the Colon and Stomach datasets, collected in the real world, as well as the public DigestPath dataset, significantly reducing the required number of WSIs by more than threefold compared to the one-pool dataset setting, which has been dominantly used in the field.</div></div>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"118 ","pages":"Article 102455"},"PeriodicalIF":5.4,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142553819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}