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2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)最新文献

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Non-Invasive Locating Of Premature Ventricular Contraction Origin With Low Rank/Tv Regularization 低秩/Tv正则化无创定位室性早搏起源
2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019) Pub Date : 2019-04-01 DOI: 10.1109/ISBI.2019.8759220
Lin Fang, Huafeng Liu
{"title":"Non-Invasive Locating Of Premature Ventricular Contraction Origin With Low Rank/Tv Regularization","authors":"Lin Fang, Huafeng Liu","doi":"10.1109/ISBI.2019.8759220","DOIUrl":"https://doi.org/10.1109/ISBI.2019.8759220","url":null,"abstract":"Most of the calculation methods for the electrocardiograph (ECG) inverse problem are based on priori assumptions of the instantaneous characteristics of the cardiac electrophysiology. In this paper, we have proposed a novel algorithm based on low rank and sparse decomposition (LSD) + total variation (TV) to solve the illposedness of dynamic ECG-inverse problem. The TV constraint filters out the disturbance of the noise and maintains the local smoothness of the potential. The LSD separates the sparse details from the potential background to prevent the potential details from being lost under the effect of smoothing constraint, thereby improving the accuracy of cardiac potential recovery.","PeriodicalId":119935,"journal":{"name":"2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116589813","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
Automated Segmentation Of Pulmonary Lobes Using Coordination-Guided Deep Neural Networks 基于坐标引导的深度神经网络的肺叶自动分割
2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019) Pub Date : 2019-04-01 DOI: 10.1109/ISBI.2019.8759492
Wenjia Wang, Junxuan Chen, Jie Zhao, Ying Chi, Xuansong Xie, Li Zhang, Xiansheng Hua
{"title":"Automated Segmentation Of Pulmonary Lobes Using Coordination-Guided Deep Neural Networks","authors":"Wenjia Wang, Junxuan Chen, Jie Zhao, Ying Chi, Xuansong Xie, Li Zhang, Xiansheng Hua","doi":"10.1109/ISBI.2019.8759492","DOIUrl":"https://doi.org/10.1109/ISBI.2019.8759492","url":null,"abstract":"The identification of pulmonary lobes is of great importance in disease diagnosis and treatment. A few lung diseases have regional disorders at lobar level. Thus, an accurate segmentation of pulmonary lobes is necessary. In this work, we propose an automated segmentation of pulmonary lobes using coordination-guided deep neural networks from chest CT images. We first employ an automated lung segmentation to extract the lung area from CT image, then exploit volumetric convolutional neural network (V-net) for segmenting the pulmonary lobes. To reduce the misclassification of different lobes, we therefore adopt coordination-guided convolutional layers (CoordConvs) that generate additional feature maps of the positional information of pulmonary lobes. The proposed model is trained and evaluated on a few publicly available datasets and has achieved the state-of-the-art accuracy with a mean Dice coefficient index of $0.947 pm 0.044$.","PeriodicalId":119935,"journal":{"name":"2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128227001","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: 20
Can Single Shell Diffusion MRI Detect Synaptic Plasticity in Mice? 单壳扩散MRI能检测小鼠突触可塑性吗?
2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019) Pub Date : 2019-04-01 DOI: 10.1109/ISBI.2019.8759467
L. Brusini, F. Cruciani, I. Galazzo, A. Galbusera, M. Borin, G. Paolone, Giovanni Diana, M. Buffelli, A. Gozzi, G. Menegaz
{"title":"Can Single Shell Diffusion MRI Detect Synaptic Plasticity in Mice?","authors":"L. Brusini, F. Cruciani, I. Galazzo, A. Galbusera, M. Borin, G. Paolone, Giovanni Diana, M. Buffelli, A. Gozzi, G. Menegaz","doi":"10.1109/ISBI.2019.8759467","DOIUrl":"https://doi.org/10.1109/ISBI.2019.8759467","url":null,"abstract":"Changes in the structure of synaptic connections underlie various physiological and neurological processes such as the development of new synapses and neuronal circuitry related to learning and memory processes or neural plasticity after injury and recovery. Recent technological advances, including two-photon microscopy and transgenic mice overexpressing fluorescent proteins have made possible to image individual dendritic arbors and spines in cortex in living animals. The aim of this work is to assess the detectability of such fine structural changes induced by Cytotoxic necrotizing factor 1 (CNF1) also via diffusion weighted Magnetic Resonance Imaging (dMRI). In this preliminary work, classical Diffusion Tensor Imaging (DTI)-based indices were derived for two groups of mice (twelve controls and fifteen CNF1-treated) and group differences were assessed by statistical analysis. T2-based Voxel Based (VBM) and Tensor Based Morphometry (TBM) were used for benchmarking. Results highlight an increment of both Fractional Anisotropy (FA) and Axial Diffusivity (AD) and a decrement of both Mean Diffusivity (MD) and Return To Plane Probability (RTPP) mainly in the visual and hippocampal areas. Our data suggest that mouse morphoanatomical imaging is sensitive to changes in neural plasticity.1","PeriodicalId":119935,"journal":{"name":"2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130367964","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
Analysis of Cedbt and CESM Performance Using a Realistic X-Ray Simulation Platform 基于真实x射线仿真平台的Cedbt和CESM性能分析
2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019) Pub Date : 2019-04-01 DOI: 10.1109/ISBI.2019.8759527
Ruben Sánchez de la Rosa, A. Carton, Pablo Milioni de Carvalho, I. Bloch, S. Muller
{"title":"Analysis of Cedbt and CESM Performance Using a Realistic X-Ray Simulation Platform","authors":"Ruben Sánchez de la Rosa, A. Carton, Pablo Milioni de Carvalho, I. Bloch, S. Muller","doi":"10.1109/ISBI.2019.8759527","DOIUrl":"https://doi.org/10.1109/ISBI.2019.8759527","url":null,"abstract":"Contrast Enhanced Spectral Mammography (CESM) and Contrast Enhanced Digital Breast Tomosynthesis (CEDBT) are multi-energy X-ray imaging techniques involving the injection of a vascular contrast agent. Both techniques provide information on hypervascularization of lesions through contrast uptake. CESM has proved to deliver a better diagnosis of breast cancer than diagnostic mammography. CEDBT is a promising technique which provides 3D information on the contrast uptake distribution. In this paper, new steps in the image acquisition process of a previously presented image acquisition simulation platform are described, including models of scatter, image lag and electronic noise. Using this simulation platform, 290 CESM and CEDBT images were generated. A human observer experiment was then performed to compare lesion detectability and characterization. The results indicate a similar detectability and an improved characterization of shape and contrast enhancement distribution using CEDBT.","PeriodicalId":119935,"journal":{"name":"2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)","volume":"173 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113983507","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: 7
Facilitating Data Association In Particle Tracking Using Autoencoding And Score Matching 利用自动编码和分数匹配促进粒子追踪中的数据关联
2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019) Pub Date : 2019-04-01 DOI: 10.1109/ISBI.2019.8759418
Ihor Smal, Yao Yao, N. Galjart, E. Meijering
{"title":"Facilitating Data Association In Particle Tracking Using Autoencoding And Score Matching","authors":"Ihor Smal, Yao Yao, N. Galjart, E. Meijering","doi":"10.1109/ISBI.2019.8759418","DOIUrl":"https://doi.org/10.1109/ISBI.2019.8759418","url":null,"abstract":"A crucial aspect of automated particle tracking in time-lapse fluorescence microscopy images is the linking or association of detected objects between frames. Recent evaluation studies have shown that the best results are achieved by making use of accurate motion models of the underlying particle dynamics. However, existing approaches often employ rather simple motion models which may be inappropriate for a given application, and even if complex models are used they all require careful user-parameter tuning. To alleviate these problems we propose a novel method based on autoencoding and score matching which can learn the dynamics from the data. Results on both synthetic and real data show the method performs comparable to state-of-the-art linking methods.","PeriodicalId":119935,"journal":{"name":"2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125999869","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: 5
Efficient Mitosis Detection in Breast Cancer Histology Images by RCNN RCNN在乳腺癌组织学图像中有效检测有丝分裂
2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019) Pub Date : 2019-04-01 DOI: 10.1109/ISBI.2019.8759461
De Cai, Xianhe Sun, Niyun Zhou, Xiao Han, Jianhua Yao
{"title":"Efficient Mitosis Detection in Breast Cancer Histology Images by RCNN","authors":"De Cai, Xianhe Sun, Niyun Zhou, Xiao Han, Jianhua Yao","doi":"10.1109/ISBI.2019.8759461","DOIUrl":"https://doi.org/10.1109/ISBI.2019.8759461","url":null,"abstract":"Mitotic cell detection and counting per tissue area is an important aggressiveness indicator for the invasive breast cancer. However, manual mitosis counting by pathologists is extremely labor-intensive. Several automatic mitosis detection methods have been proposed in recent years. Traditional methods using hand-crafted features suffer from large mitotic cell shape variation and the existence of many mimics with similar appearance. Pixel-wise classification working in a sliding window manner is time-consuming which hinders it from clinical application. In this work, we propose an efficient mitosis detection method in breast cancer histology images by applying modified regional convolutional neural network (RCNN). Our method achieves 0.76 in precision, 0.72 recall and 0.736 F1 score on MICCAI TUPAC 2016 datasets, outperforming all the previously published results as far as we know. F1 score of 0.585 is also achieved on ICPR 2014 mitosis dataset. TUPAC 2016 and ICPR 2014 datasets are cross validated without and with color normalization to study the generalization performance. The inference time for a 2000×2000 image is ~ 0.8 s, making our method a promising tool for clinical deployment.","PeriodicalId":119935,"journal":{"name":"2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134015997","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: 30
Deep Learning For Skin Cancer Diagnosis With Hierarchical Architectures 基于层次结构的皮肤癌诊断深度学习
2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019) Pub Date : 2019-04-01 DOI: 10.1109/ISBI.2019.8759561
Catarina Barata, J. Marques
{"title":"Deep Learning For Skin Cancer Diagnosis With Hierarchical Architectures","authors":"Catarina Barata, J. Marques","doi":"10.1109/ISBI.2019.8759561","DOIUrl":"https://doi.org/10.1109/ISBI.2019.8759561","url":null,"abstract":"Skin lesions are organized in a hierarchical way, which is taken into account by dermatologists when diagnosing them. However, automatic systems do not make use of this information, performing the diagnosis in a one-vs-all approach, where all types of lesions are considered. In this paper we propose to mimic the medical strategy and train a deep-learning architecture to perform a hierarchical diagnosis. Our results highlight the benefits of addressing the classification of dermoscopy images in a structured way. Additionally, we provide an extensive evaluation of criteria that must be taken into account in the development of diagnostic systems based on deep learning.","PeriodicalId":119935,"journal":{"name":"2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131088616","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: 38
ADHD Classification Within and Cross Cohort Using an Ensembled Feature Selection Framework 使用集成特征选择框架的ADHD分类和交叉队列
2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019) Pub Date : 2019-04-01 DOI: 10.1109/ISBI.2019.8759533
Dongren Yao, Hailun Sun, Xiaojie Guo, V. Calhoun, Li Sun, J. Sui
{"title":"ADHD Classification Within and Cross Cohort Using an Ensembled Feature Selection Framework","authors":"Dongren Yao, Hailun Sun, Xiaojie Guo, V. Calhoun, Li Sun, J. Sui","doi":"10.1109/ISBI.2019.8759533","DOIUrl":"https://doi.org/10.1109/ISBI.2019.8759533","url":null,"abstract":"Attention-deficit/hyperactivity disorder (ADHD) is a childhood-onset neurodevelopmental disorder that often persists into adulthood. However, as lacking objective measures, several studies have questioned the stability in diagnosing of ADHD from childhood to adulthood. In this study, we propose a novel feature selection framework based on functional connectivity (FCs) pattern, the so-called ‘FS_RIWEL,’ which could classify ADHD from age-matched healthy controls (HCs) with $sim 80$% accuracy (both for children and adults). More importantly, the feature space learned from child ADHD dataset can discriminate adult ADHD from HCs at $sim 70$% accuracy. To the best of our knowledge, this is the first attempt to perform a cross-cohort prediction between the adult and child ADHD using FC features. In addition, the most frequently selected FCs indicate that ADHD exhibit widely-impaired FC patterns in frontoparietal, basal ganglia, cerebellum network and so on suggesting that FCs may serve as potential biomarkers for ADHD diagnosis.","PeriodicalId":119935,"journal":{"name":"2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133034589","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: 5
Simultaneous Super-Resolution and Segmentation Using a Generative Adversarial Network: Application to Neonatal Brain MRI 同时使用生成对抗网络的超分辨率和分割:在新生儿脑MRI中的应用
2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019) Pub Date : 2019-04-01 DOI: 10.1109/ISBI.2019.8759255
Chi-Hieu Pham, Carlos Tor-Díez, H. Meunier, N. Bednarek, R. Fablet, Nicolas Passat, F. Rousseau
{"title":"Simultaneous Super-Resolution and Segmentation Using a Generative Adversarial Network: Application to Neonatal Brain MRI","authors":"Chi-Hieu Pham, Carlos Tor-Díez, H. Meunier, N. Bednarek, R. Fablet, Nicolas Passat, F. Rousseau","doi":"10.1109/ISBI.2019.8759255","DOIUrl":"https://doi.org/10.1109/ISBI.2019.8759255","url":null,"abstract":"Brest, France The analysis of clinical neonatal brain MRI remains challenging due to low anisotropic resolution of the data. In most pipelines, images are first re-sampled using interpolation or single image super-resolution techniques and then segmented using (semi-)automated approaches. Image reconstruction and segmentation are then performed separately. In this paper, we propose an end-to-end generative adversarial network for simultaneous high-resolution reconstruction and segmentation of brain MRI data. This joint approach is first assessed on the simulated low-resolution images of the high-resolution neonatal dHCP dataset. Then, the learned model is used to enhance and segment real clinical low-resolution images. Results demonstrate the potential of our proposed method with respect to practical medical applications.","PeriodicalId":119935,"journal":{"name":"2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133326391","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: 11
Generative Aging Of Brain MRI For Early Prediction Of MCI-AD Conversion 早期预测MCI-AD转换的脑MRI生成老化
2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019) Pub Date : 2019-04-01 DOI: 10.1109/ISBI.2019.8759394
Viktor Wegmayr, Maurice Hörold, J. Buhmann
{"title":"Generative Aging Of Brain MRI For Early Prediction Of MCI-AD Conversion","authors":"Viktor Wegmayr, Maurice Hörold, J. Buhmann","doi":"10.1109/ISBI.2019.8759394","DOIUrl":"https://doi.org/10.1109/ISBI.2019.8759394","url":null,"abstract":"Automatic diagnosis of Alzheimer’s disease (AD) from MR images of the brain promises to yield important information of a patient’s disease status or even early prediction of disease onset. This work investigates deep learning based methods to predict conversion of Mild Cognitive Impairment (MCI) to AD based on widely available T1-weighted MR brain images. We present a novel approach breaking up the conversion prediction into a generative and a discriminative step. Using the recently proposed Wasserstein-GAN model, we generate a synthetically aged brain image given a baseline image. The aged image is passed to an MCI/AD discriminator deciding the future disease status. Using only one coronal slice of a patient’s baseline T1image, our approach achieves 73% accuracy, 68% precision, and 75% recall on MCI-to-AD conversion prediction at a 48months follow-up.","PeriodicalId":119935,"journal":{"name":"2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)","volume":"253 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132385736","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: 17
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