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2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops)最新文献

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Multi-Channel Deep Neural Network For Temporal Lobe Epilepsy Classification Using Multimodal Mri Data 基于多模态Mri数据的多通道深度神经网络颞叶癫痫分类
2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops) Pub Date : 2020-04-01 DOI: 10.1109/ISBIWorkshops50223.2020.9153359
Maribel Torres-Velázquez, G. Hwang, C. Cook, B. Hermann, V. Prabhakaran, M. Meyerand, A. McMillan
{"title":"Multi-Channel Deep Neural Network For Temporal Lobe Epilepsy Classification Using Multimodal Mri Data","authors":"Maribel Torres-Velázquez, G. Hwang, C. Cook, B. Hermann, V. Prabhakaran, M. Meyerand, A. McMillan","doi":"10.1109/ISBIWorkshops50223.2020.9153359","DOIUrl":"https://doi.org/10.1109/ISBIWorkshops50223.2020.9153359","url":null,"abstract":"Multiple magnetic resonance imaging (MRI) modalities are currently used for the diagnosis and characterization of temporal lobe epilepsy (TLE). The objective of this study is to assess the performance of individual and combination of multimodal MRI datasets to provide an accurate classification of TLE by employing a multi-channel deep neural network. Several multi-channel deep neural network models were trained, validated, and tested using brain structure metrics from structural MRI, MRI-based region of interest correlation features, and personal demographic and cognitive data (PDC). The results show that PDC individually offered the most accurate classification of TLE followed by the combination of PDC with MRI-based brain structure metrics. These findings demonstrate the potential of deep learning approaches such as mDNN models to combine multiple datasets for TLE classification.","PeriodicalId":329356,"journal":{"name":"2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115497691","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
Pneumothorax Segmentation with Effective Conditioned Post-Processing in Chest X-Ray 胸片中有效条件后处理的气胸分割
2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops) Pub Date : 2020-04-01 DOI: 10.1109/ISBIWorkshops50223.2020.9153444
V. Groza, A. Kuzin
{"title":"Pneumothorax Segmentation with Effective Conditioned Post-Processing in Chest X-Ray","authors":"V. Groza, A. Kuzin","doi":"10.1109/ISBIWorkshops50223.2020.9153444","DOIUrl":"https://doi.org/10.1109/ISBIWorkshops50223.2020.9153444","url":null,"abstract":"The automatic detection of abnormal elements in chest Xrays (CXR), such as pneumothorax, is important and challenging problem. Screening for unexpected findings or any surveys in the complicated conditions are the common scenarios for the radiologists in their clinical workflow, where the automated solutions are required. The pneumothorax can be caused by a blunt chest injury, damage from underlying lung disease or it may occur for no obvious reason at all [1]. This is one of the complex problems for the experts manual detection, which can be solved automatically and simplify the clinical workflow. Proposed method presents new segmentation pipeline for the CXR images with the multi step conditi oned post-processing. This approach leads to the significant improvement compare with any ”baseline” by the reduction of the totally missed and false positive detections of the pneumothorax collapse regions. Obtained results demonstrate very high accuracy and strong robustness due to very similar performance on the double-stage test dataset. Final Dice scores are 0.8821 and 0.8614 for ”stage 1” and ”stage 2” test datasets respectively, what is resulted in top 0.01% standing of the private leaderboard on the Kaggle competition platform. Code is available at https://github.com/n01z3/kaggle-pneumothoraxsegmentation.","PeriodicalId":329356,"journal":{"name":"2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125067868","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
Investigating Heritability Across Resting State Brain Networks Via Heat Kernel Smoothing on Persistence Diagrams 通过持续图的热核平滑研究静息状态脑网络的遗传性
2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops) Pub Date : 2020-04-01 DOI: 10.1109/ISBIWorkshops50223.2020.9153361
Arman P. Kulkarni, M. Chung, B. Bendlin, V. Prabhakaran
{"title":"Investigating Heritability Across Resting State Brain Networks Via Heat Kernel Smoothing on Persistence Diagrams","authors":"Arman P. Kulkarni, M. Chung, B. Bendlin, V. Prabhakaran","doi":"10.1109/ISBIWorkshops50223.2020.9153361","DOIUrl":"https://doi.org/10.1109/ISBIWorkshops50223.2020.9153361","url":null,"abstract":"The brain’s heritable topological differences in resting state functional connectivity (rsfc) measured via resting state fMRI (rsfMRI) provide important insight into brain function and dysfunction. Current techniques investigating heritability are limited by arbitrary rsfc threshold selection and reduction of otherwise detailed brain topological properties into summary measures. Topological Data Analysis (TDA) is a novel tool for addressing these limitations by analyzing how the topological properties of data vary without arbitrary threshold and summary metric construction. TDA applies a filtration to the data and constructs a persistence diagram (PD). Therefore, the purpose of this study was to compute PDs to determine TDAbased heritability of static brain network topological features. To this end, we calculated a robust heritability index map across smoothed PDs derived from twin rsfMRI data.","PeriodicalId":329356,"journal":{"name":"2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116826720","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: 4
ISBI Workshops 2020 Cover Page ISBI研讨会2020封面
2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops) Pub Date : 2020-04-01 DOI: 10.1109/isbiworkshops50223.2020.9153434
{"title":"ISBI Workshops 2020 Cover Page","authors":"","doi":"10.1109/isbiworkshops50223.2020.9153434","DOIUrl":"https://doi.org/10.1109/isbiworkshops50223.2020.9153434","url":null,"abstract":"","PeriodicalId":329356,"journal":{"name":"2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132115487","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
A Novel U-Like Network For The Segmentation Of Thoracic Organs 一种新型胸腔器官分割的u型网络
2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops) Pub Date : 2020-04-01 DOI: 10.1109/ISBIWorkshops50223.2020.9153358
Jun Shi, Ke Wen, Xiaoyu Hao, Xudong Xue, Hong An, Hongyan Zhang
{"title":"A Novel U-Like Network For The Segmentation Of Thoracic Organs","authors":"Jun Shi, Ke Wen, Xiaoyu Hao, Xudong Xue, Hong An, Hongyan Zhang","doi":"10.1109/ISBIWorkshops50223.2020.9153358","DOIUrl":"https://doi.org/10.1109/ISBIWorkshops50223.2020.9153358","url":null,"abstract":"Accurate segmentation of organs at risk (OARs) in computed tomography (CT) is an essential step in radiation therapy for thoracic cancer treatment. However, the manual segmentation of OARs is time-consuming and subject to inter-observer variation. In this paper, we propose a novel U-like deep convolutional neural network (CNN) architecture, which adopts the encoder-decoder design, to automatically segment the OARs in thoracic CT images. In our method, hybrid dilated convolution (HDC) is employed to enlarge the receptive field of the encoder part, and a pyramid backbone with lateral connections between encoder and decoder is utilized to capture contextual information at multiple scales. To reduce the false-positive segmentation results, we use the multi-task learning strategy to add an auxiliary classifier branch to the network. The experiments demonstrate that the proposed method outperforms other state-of-the-art models and the results have a good consistency with that of experienced radiologists.","PeriodicalId":329356,"journal":{"name":"2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124501415","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
ISBI Workshops 2020 Table of Contents 2020年ISBI研讨会目录
2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops) Pub Date : 2020-04-01 DOI: 10.1109/isbiworkshops50223.2020.9153447
{"title":"ISBI Workshops 2020 Table of Contents","authors":"","doi":"10.1109/isbiworkshops50223.2020.9153447","DOIUrl":"https://doi.org/10.1109/isbiworkshops50223.2020.9153447","url":null,"abstract":"","PeriodicalId":329356,"journal":{"name":"2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124029019","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
C-Algl Net: Pathological Images Generate Diagnostic Results C-Algl网:病理图像产生诊断结果
2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops) Pub Date : 2020-04-01 DOI: 10.1109/ISBIWorkshops50223.2020.9153419
Zongkai Lian, Haiqiong Yang, Fan Wu, Mingxin Li, Shancheng Jiang
{"title":"C-Algl Net: Pathological Images Generate Diagnostic Results","authors":"Zongkai Lian, Haiqiong Yang, Fan Wu, Mingxin Li, Shancheng Jiang","doi":"10.1109/ISBIWorkshops50223.2020.9153419","DOIUrl":"https://doi.org/10.1109/ISBIWorkshops50223.2020.9153419","url":null,"abstract":"The lack of a clear correspondence between feature of lesion areas and corresponding pathological characteristics and the scarcity of high-quality histopathological image sets pose a great challenge to the establishment of interpretable computer-aided diagnostic systems. Therefore, we propose a new deep learning-based model, named as C-ALGL model (CNN-AttendLSTM-GenerateLSTM), which is able to generate visual image results with diagnostic descriptions from input histopathological images in one pass. We use an improved recurrent neural network-based structure that incorporates attentional mechanisms in the LSTM interlayer with altered LSTM parameter delivery pathways. The structure generates visualization results at the attentional mechanism and diagnostic text at the end-connected full-connected layer. We conducted a large number of experiments on the PATHOLOGY-11 skin pathology image dataset and the experimental results proved that the C-ALGL model performed better than benchmark models on this task.","PeriodicalId":329356,"journal":{"name":"2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128302774","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
Deep Convolutional Neural Network for Parkinson’s Disease Based Handwriting Screening 基于深度卷积神经网络的帕金森病笔迹筛选
2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops) Pub Date : 2020-04-01 DOI: 10.1109/ISBIWorkshops50223.2020.9153407
M. Shaban
{"title":"Deep Convolutional Neural Network for Parkinson’s Disease Based Handwriting Screening","authors":"M. Shaban","doi":"10.1109/ISBIWorkshops50223.2020.9153407","DOIUrl":"https://doi.org/10.1109/ISBIWorkshops50223.2020.9153407","url":null,"abstract":"In this paper, the use of a fine-tuned VGG-19 for screening Parkinson’s Disease (PD) based on a Kaggle handwriting dataset is investigated and experimented. The dataset including 102 wave and 102 spiral handwriting patterns was pre-processed where images were resized and a data augmentation based on image rotation was adopted to minimize overfitting. The Convolutional Neural Network (CNN) model was then trained on the pre-processed dataset and validated using both 4-fold and 10-fold cross validation techniques. The CNN model achieved an accuracy of 88%, 89%, and a sensitivity of 89%, 87% on the wave and spiral patterns respectively when a 10-fold cross validation was used. The proposed approach offers a promising solution for assessing and screening PD based on handwriting drawings and achieves a comparable high performance on the two different handwriting patterns as compared with the-state-of-the-art architecture that adopted a fine-tuned AlexNet.","PeriodicalId":329356,"journal":{"name":"2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114174324","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
Lung CT Screening With 3D Convolutional Neural Network Architectures 基于三维卷积神经网络架构的肺部CT筛查
2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops) Pub Date : 2020-04-01 DOI: 10.1109/ISBIWorkshops50223.2020.9153384
T. Lima, D. Ushizima, Antônio Oséas de Carvalho Filho, Flávio H. D. Araújo
{"title":"Lung CT Screening With 3D Convolutional Neural Network Architectures","authors":"T. Lima, D. Ushizima, Antônio Oséas de Carvalho Filho, Flávio H. D. Araújo","doi":"10.1109/ISBIWorkshops50223.2020.9153384","DOIUrl":"https://doi.org/10.1109/ISBIWorkshops50223.2020.9153384","url":null,"abstract":"The standard tests for diagnosis of pulmonary cancer are imaging, sputum cytology and lung biopsy, with chest computed tomography (CT) playing a major role in the early detection of nodules, which increases the patients survival. The challenge is to analyze these images automatically, for example, the nodules density often resembles other pulmonary structures evidenced in CTs. This paper proposes an automated algorithm to classify pulmonary nodules into benign or malignant. Our contribution is to design and test 3D Convolutional Neural Networks using a public CT image collection, optimize the results of the proposed approach considering varying input sizes and numbers of convolutional layers, as well as compare with several previous approaches on CT analysis. Promising results show accuracy of 0.9040, kappa of 0.7624, sensitivity of 0.8630, specificity of 0.9191 and AUC of 0.8911 during malignant nodule detection.","PeriodicalId":329356,"journal":{"name":"2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops)","volume":"81 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133204252","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
Dynamic Topological Data Analysis for Functional Brain Signals 脑功能信号的动态拓扑数据分析
2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops) Pub Date : 2020-04-01 DOI: 10.1109/ISBIWorkshops50223.2020.9153431
Tananun Songdechakraiwut, M. Chung
{"title":"Dynamic Topological Data Analysis for Functional Brain Signals","authors":"Tananun Songdechakraiwut, M. Chung","doi":"10.1109/ISBIWorkshops50223.2020.9153431","DOIUrl":"https://doi.org/10.1109/ISBIWorkshops50223.2020.9153431","url":null,"abstract":"We propose a novel dynamic topological data analysis (TDA) framework that builds persistent homology over a time series of 3D functional brain images. The proposed method encodes the time series as a time-ordered sequence of Vietoris-Rips complexes and their corresponding barcodes in studying dynamically changing topological patterns. The method is applied to the resting-state functional magnetic resonance imaging (fMRI) of the human brain. We demonstrate that the dynamic-TDA can capture the topological patterns that are consistently observed across different time points in the resting-state fMRI.","PeriodicalId":329356,"journal":{"name":"2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123884282","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: 15
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