2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)最新文献_第6页

Manifold Learning in Detecting the Transitions of Dynamic Functional Connectivities Boosts Brain State-Specific Recognition 检测动态功能连接转换的流形学习促进大脑状态特异性识别

2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) Pub Date : 2022-03-28 DOI: 10.1109/ISBI52829.2022.9761486

Tingting Dan, Zhuobin Huang, Hongmin Cai, Guorong Wu

{"title":"Manifold Learning in Detecting the Transitions of Dynamic Functional Connectivities Boosts Brain State-Specific Recognition","authors":"Tingting Dan, Zhuobin Huang, Hongmin Cai, Guorong Wu","doi":"10.1109/ISBI52829.2022.9761486","DOIUrl":"https://doi.org/10.1109/ISBI52829.2022.9761486","url":null,"abstract":"Exploring functional dynamics, especially with regard to the topology of functional networks, evolves into the forefront in neuroscience. Despite recent advances in identifying the transitions of functional connectivities (FCs), recognizing accurately the specific brain cognitive states along time series is few reported. Direct classification of time-varying brain data often produces sub-optimal recognition results that do not adhere to the principle of the quasi-stationary functional state. On account of the predicted brain states in such manner will be disorderly change along time. To overcome this challenge, we exploit a novel state recognition network (SR-Net) guided by the detection for transitions of dynamic FCs on Riemannian manifold. To do so, we regard the temporal evolution of functional brain networks as a set of landmarks residing on a Riemannian manifold. Accounting for high-dimensional properties of the brain networks, we elaborate a feature distillation network to capture low-dimensional FC signatures with symmetric positive definite (SPD) geometry properties. Stratifying the distribution of functional networks is devised to detect cognition state changes, which can be well solved by identifying latent modes through mean shift on the Riemannian manifold. Since functional dynamic recognition is implicated in cognitive state changes, we propose to classify these latent modes from the stratified time-varying data. Empirical results show that our SR-Net has achieved favorable state recognition results than other state-of-the-art methods on the simulated and task functional neuroimaging data from Human Connectome Project (HCP).","PeriodicalId":6827,"journal":{"name":"2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)","volume":"45 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2022-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82124137","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

Generation of 12-Lead Electrocardiogram with Subject-Specific, Image-Derived Characteristics Using a Conditional Variational Autoencoder 使用条件变分自编码器生成具有受试者特定图像衍生特征的12导联心电图

2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) Pub Date : 2022-03-28 DOI: 10.1109/ISBI52829.2022.9761431

Yuling Sang, M. Beetz, V. Grau

引用次数: 3

LONDN-MRI: Adaptive Local Neighborhood-Based Networks for MR Image Reconstruction from Undersampled Data 伦敦磁共振成像:基于自适应局部邻域的低采样数据磁共振图像重建网络

2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) Pub Date : 2022-03-28 DOI: 10.1109/ISBI52829.2022.9761587

S. Liang, Ashwin Sreevatsa, Anish Lahiri, S. Ravishankar

{"title":"LONDN-MRI: Adaptive Local Neighborhood-Based Networks for MR Image Reconstruction from Undersampled Data","authors":"S. Liang, Ashwin Sreevatsa, Anish Lahiri, S. Ravishankar","doi":"10.1109/ISBI52829.2022.9761587","DOIUrl":"https://doi.org/10.1109/ISBI52829.2022.9761587","url":null,"abstract":"There has been much interest in machine learning based methods for MR image reconstruction from undersampled k-space data. This paper presents a method for MR image reconstruction based on rapidly fitting neural networks to adaptively estimated neighbor-hoods within a larger training set. The weights of the network are learned only on training examples that are specified in the ‘vicinity’ or local neighborhood of a test reconstruction. The algorithm (dubbed LONDN-MRI) alternates between estimating the neighbors of the reconstructed test image and performing (local) network training and updating the test reconstruction. Rather than attempting to fit a model once to the entire dataset, our proposed method allows for learning models that are more tailored to the input test data, and therefore more flexible to the choice of undersampling patterns or anatomy. It also easily accommodates modifications to training sets. We used the recent MoDL (deep unrolled) network and the FastMRI dataset for testing our approach. We present reconstruction results for fourfold and eightfold undersampling of multi-coil data using 1D variable-density random phase-encode sampling masks. When trained locally, our method yields reconstructions of better quality compared to models learned globally on larger datasets.","PeriodicalId":6827,"journal":{"name":"2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)","volume":"31 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2022-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90370347","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

From Chairs To Brains: Customizing Optical Flow For Surgical Activity Localization 从椅子到大脑:定制手术活动定位的光流

2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) Pub Date : 2022-03-28 DOI: 10.1109/ISBI52829.2022.9761704

Markus Philipp, Neal Bacher, Stefan Saur, Franziska Mathis-Ullrich, Andrés Bruhn

引用次数: 0

LFANET: Transforming 3T Single-Shell to 7T Multi-Shell DMRI Using Deep Learning Based Leapfrog and Attention LFANET:利用基于深度学习的跳跃和注意力将3T单壳转换为7T多壳DMRI

2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) Pub Date : 2022-03-28 DOI: 10.1109/ISBI52829.2022.9761658

Ranjeet Ranjan Jha, S. Pathak, W. Schneider, B. V. R. Kumar, A. Bhavsar, A. Nigam

{"title":"LFANET: Transforming 3T Single-Shell to 7T Multi-Shell DMRI Using Deep Learning Based Leapfrog and Attention","authors":"Ranjeet Ranjan Jha, S. Pathak, W. Schneider, B. V. R. Kumar, A. Bhavsar, A. Nigam","doi":"10.1109/ISBI52829.2022.9761658","DOIUrl":"https://doi.org/10.1109/ISBI52829.2022.9761658","url":null,"abstract":"HARDI-based diffusion MRI acquisition technique is a relatively recent modality of interest as it can yield more accurate fiber tracts. Besides, HARDI at higher magnetic strength is more sensitive to tissue changes and accurately estimate anatomical details in the human brain. However, a higher magnetic strength scanner is costly and not available in most clinical settings. Furthermore, due to signal-to-noise ratio issues and severe imaging artefacts, most existing 3T dMRI scanners with low gradient-strengths generally acquire single-shell up to b = 1000s/mm2. Hence, in this work, we consider the task of transforming the 3T single-shell HARDI signal (at b = 1000s/mm2) to a 7T multi-shell HARDI signal utilizing the proposed deep learning model LF ANet. The proposed model consists of modules based on a Leapfrog method and an attention module. In addition, we have included suitable loss functions such as L1 and total variation loss. Several quantitative and qualitative results have been presented to show the effectiveness of the proposed method.","PeriodicalId":6827,"journal":{"name":"2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)","volume":"94 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2022-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91017693","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

Reconstruction of Resting State FMRI Using LSTM Variational Auto-Encoder on Subcortical Surface to Detect Epilepsy 皮层下表面LSTM变分自编码器重建静息状态FMRI检测癫痫

2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) Pub Date : 2022-03-28 DOI: 10.1109/ISBI52829.2022.9761430

Yunan Wu, P. Besson, Emanuel A. Azcona, S. Bandt, T. Parrish, A. Katsaggelos

{"title":"Reconstruction of Resting State FMRI Using LSTM Variational Auto-Encoder on Subcortical Surface to Detect Epilepsy","authors":"Yunan Wu, P. Besson, Emanuel A. Azcona, S. Bandt, T. Parrish, A. Katsaggelos","doi":"10.1109/ISBI52829.2022.9761430","DOIUrl":"https://doi.org/10.1109/ISBI52829.2022.9761430","url":null,"abstract":"Functional MRI offers unique insights for the characterization and presurgical evaluation of people with epilepsy (PWE). In this paper, we develop a graph-based variational auto-encoder (gVAEs) to 1) learn the patterns of resting state functional MRI (rsfMRI) within the brain’s subcortical structures in healthy subjects and 2) reconstruct it in PWE to identify findings unique to patients with epilepsy. The gVAE was enriched with Sequential Long Short Term Memory (LSTM) and perceptual loss to learn temporal rsfMRI features and smooth the reconstructed signals. Using a cross-validation approach on healthy controls, our best model yielded an average spatial correlation of 0.791 and an average temporal correlation of 0.793. When applied to PWE, the average and spatial correlation decreased to 0.752 and 0.750 respectively. Our findings pave the path to the development of a whole brain data-driven tool that may be valuable for the characterization of abnormalities within the epileptic brain. This may advance our understanding as to how these abnormalities are related to the location of seizure onset and can inform the care of patients with epilepsy. The code is available at: GitHub","PeriodicalId":6827,"journal":{"name":"2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)","volume":"10 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2022-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91105194","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

Lightseg: Efficient Yet Effective Medical Image Segmentation Lightseg:高效而有效的医学图像分割

2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) Pub Date : 2022-03-28 DOI: 10.1109/ISBI52829.2022.9761663

Most Husne Jahan, A. Imran

引用次数: 0

Automated Dcis Identification From Multiplex Immunohistochemistry Using Generative Adversarial Networks 利用生成对抗网络从多重免疫组化中自动识别Dcis

2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) Pub Date : 2022-03-28 DOI: 10.1109/ISBI52829.2022.9761413

F. Sobhani, A. Hamidinekoo, A. Hall, Lorraine M. King, J. Marks, C. Maley, H. Horlings, E. Hwang, Yinyin Yuan

{"title":"Automated Dcis Identification From Multiplex Immunohistochemistry Using Generative Adversarial Networks","authors":"F. Sobhani, A. Hamidinekoo, A. Hall, Lorraine M. King, J. Marks, C. Maley, H. Horlings, E. Hwang, Yinyin Yuan","doi":"10.1109/ISBI52829.2022.9761413","DOIUrl":"https://doi.org/10.1109/ISBI52829.2022.9761413","url":null,"abstract":"Ductal Carcinoma In Situ (DCIS) is a non-obligatory precursor of Invasive Breast Cancer. It is the most common mammographically detected breast cancer. Predicting DCIS progression to invasive ductal carcinoma is a major clinical challenge due to the lack of a uniform classification system in the diagnosis and prognostication of this disease. To characterise the tissue microecology of DCIS, we proposed and tested the model \"DCIS-Identification model\" based on Generative Adversarial Networks (GAN) for detection and segmentation of DCIS ducts from multiplex immunohistochemistry (IHC) staining samples. We also trained a Spatially Constrained Convolutional Neural Network (SC-CNN) to detect and classify single cells based on their CA9 and FOXP3 expression. The DCIS-Identification model was evaluated on 8 whole slide images, resulting in an average Dice score of 0.95 for the segmentation performance. The single cell identification framework was tested on 10 randomly selected whole slide sections, achieving the average accuracy of 88.6% in a 5 fold cross validation scheme. With the proposed pipeline, we efficiently integrated deep learning, computational pathology and spatial statistics to report distinct differences in the microenvironments of DCIS and IDC/DCIS samples. The proposed pipeline provides a tool for a better understanding of the mechanism of tumours in DCIS and IDC/DCIS cases.","PeriodicalId":6827,"journal":{"name":"2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)","volume":"3 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2022-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78360315","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

A Lung-Parenchyma-Contrast Hybrid Network For EGFR Gene Mutation Prediction In Lung Cancer 肺癌中EGFR基因突变预测的肺-实质-对比杂交网络

2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) Pub Date : 2022-03-28 DOI: 10.1109/ISBI52829.2022.9761614

Meili Liu, Shuo Wang, He Yu, Yongbei Zhu, Liusu Wang, Mingyu Zhang, Zhangjie Wu, Xiaohu Li, Wei-min Li, Jie Tian

{"title":"A Lung-Parenchyma-Contrast Hybrid Network For EGFR Gene Mutation Prediction In Lung Cancer","authors":"Meili Liu, Shuo Wang, He Yu, Yongbei Zhu, Liusu Wang, Mingyu Zhang, Zhangjie Wu, Xiaohu Li, Wei-min Li, Jie Tian","doi":"10.1109/ISBI52829.2022.9761614","DOIUrl":"https://doi.org/10.1109/ISBI52829.2022.9761614","url":null,"abstract":"Epidermal growth factor receptor (EGFR) mutation status is critical for lung cancer treatment planning. Current identification relies on invasive biopsy and expensive gene sequencing. Recent studies revealed that CT images combined with deep learning can be used to non-invasively predict EGFR mutation status. However, how to enable the network to focus on the lung parenchyma area and extract discriminative features needs further exploration. In this study, we proposed a lung-parenchyma-contrast (LPC) hybrid network that: 1) uses a fully automatic whole-lung analysis method and enables the model to focus on the lung parenchyma area; 2) extracts local and global lung parenchyma features by a contrastive learning strategy; and 3) jointly performs feature learning and classifier learning to improve predictive performance. We evaluated our network on a large multi-center dataset (2316 patients), which outperforms (AUC=0.827) the previous state-of-the-art methods. Extensive experiments also demonstrated the effectiveness of the contrastive learning modules.†","PeriodicalId":6827,"journal":{"name":"2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)","volume":"99 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2022-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76069733","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

Towards Efficient FMRI Data Re-Use: Can We Run Between-Group Analyses with Datasets Processed Differently with SPM? 迈向有效的FMRI数据重用:我们可以用SPM不同处理的数据集进行组间分析吗?

2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) Pub Date : 2022-03-28 DOI: 10.1109/ISBI52829.2022.9761471

Xavier Rolland, Pierre Maurel, Camille Maumet

引用次数: 0