2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)最新文献_第9页

SAVGAN: Self-Attention Based Generation of Tumour on Chip Videos SAVGAN:基于自我关注的肿瘤芯片视频生成

2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) Pub Date : 2022-03-28 DOI: 10.1109/ISBI52829.2022.9761518

Sandeep Manandhar, I. Veith, M. Parrini, Auguste Genovesio

{"title":"SAVGAN: Self-Attention Based Generation of Tumour on Chip Videos","authors":"Sandeep Manandhar, I. Veith, M. Parrini, Auguste Genovesio","doi":"10.1109/ISBI52829.2022.9761518","DOIUrl":"https://doi.org/10.1109/ISBI52829.2022.9761518","url":null,"abstract":"Generation of videomicroscopy sequences will become increasingly important in order to train and evaluate dynamic image analysis methods. The latter are crucial to the study of biological dynamic processes such as tumour-immune cell interactions. However, current generative models developed in the context of natural image sequences employ either a single 3D (2D+time) convolutional neural network (CNN) based generator, which fails to capture long range interactions, or two separate (spatial and temporal) generators, which are unable to faithfully reproduce the morphology of moving objects. Here, we propose a self-attention based generative model for videomicroscopy sequences that aims to take into account for the full range of interactions within a spatio-temporal volume of 32 frames. To reduce the computational burden of such a strategy, we consider the Nyström approximation of the attention matrix. This approach leads to significant improvements in reproducing the structures and the proper motion of videomicroscopy sequences as assessed by a range of existing and proposed quantitative metrics.","PeriodicalId":6827,"journal":{"name":"2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)","volume":"30 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":"83381991","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

TriageNet: A Multi-Agent Diagnosis Network for Imbalanced Data TriageNet:一种多智能体的不平衡数据诊断网络

2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) Pub Date : 2022-03-28 DOI: 10.1109/ISBI52829.2022.9761420

Weixiang Chen, Jianjiang Feng, Jie Zhou

引用次数: 0

Perfusion Imaging in Deep Prostate Cancer Detection from MP-MRI: Can We Take Advantage of it? 灌注成像在MP-MRI深部前列腺癌检测中的应用:我们能利用它吗?

2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) Pub Date : 2022-03-28 DOI: 10.1109/ISBI52829.2022.9761616

Audrey Duran, Gaspard Dussert, C. Lartizien

{"title":"Perfusion Imaging in Deep Prostate Cancer Detection from MP-MRI: Can We Take Advantage of it?","authors":"Audrey Duran, Gaspard Dussert, C. Lartizien","doi":"10.1109/ISBI52829.2022.9761616","DOIUrl":"https://doi.org/10.1109/ISBI52829.2022.9761616","url":null,"abstract":"To our knowledge, all deep computer-aided detection and diagnosis (CAD) systems for prostate cancer (PCa) detection consider bi-parametric magnetic resonance imaging (bp-MRI) only, including T2w and ADC sequences while excluding the 4D perfusion sequence,which is however part of standard clinical protocols for this diagnostic task. In this paper, we question strategies to integrate information from perfusion imaging in deep neural architectures. To do so, we evaluate several ways to encode the perfusion information in a U-Net like architecture, also considering early versus mid fusion strategies. We compare performance of multiparametric MRI (mp-MRI) models with the baseline bp-MRI model based on a private dataset of 219 mp-MRI exams. Perfusion maps derived from dynamic contrast enhanced MR exams are shown to positively impact segmentation and grading performance of PCa lesions, especially the 3D MR volume corresponding to the maximum slope of the wash-in curve as well as Tmax perfusion maps. The latter mp-MRI models indeed outperform the bp-MRI one whatever the fusion strategy, with Co-hen’s kappa score of 0.318±0.019 for the bp-MRI model and 0.378 ± 0.033 for the model including the maximum slope with a mid fusion strategy, also achieving competitive Co-hen’s kappa score compared to state of the art.","PeriodicalId":6827,"journal":{"name":"2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)","volume":"86 1 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":"89348167","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

Segmentation of Multiple Myeloma Cells Using Feature Selection Pyramid Network and Semantic Cascade Mask RCNN 基于特征选择金字塔网络和语义级联掩模的多发性骨髓瘤细胞分割

2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) Pub Date : 2022-03-28 DOI: 10.1109/ISBI52829.2022.9761460

Xinyun Qiu, Haijun Lei, Hai Xie, Baiying Lei

引用次数: 2

Extending the Capability of Linear Array Ultrasound Probe to Concave Array Using Low-Cost Acoustic Lens for High Frame Rate Focused Imaging 利用低成本声透镜将线阵超声探头扩展到凹阵，实现高帧率聚焦成像

2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) Pub Date : 2022-03-28 DOI: 10.1109/ISBI52829.2022.9761480

Pisharody Harikrishnan Gopalakrishnan, Mahesh Raveendranatha Panicker

引用次数: 0

Spatial Analysis For Histopathology: A Statistical Approach 组织病理学的空间分析:统计学方法

2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) Pub Date : 2022-03-28 DOI: 10.1109/ISBI52829.2022.9761498

Suvadip Mukherjee, V. Meas-Yedid, M. Bokobza, T. Lagache, A. Corthay, J. Olivo-Marin

引用次数: 0

CEUSegNet: A Cross-Modality Lesion Segmentation Network for Contrast-Enhanced Ultrasound CEUSegNet:用于对比增强超声的跨模态病变分割网络

2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) Pub Date : 2022-03-28 DOI: 10.1109/ISBI52829.2022.9761594

Zheling Meng, Yangyang Zhu, Xiao Fan, Jie Tian, F. Nie, Kun Wang

引用次数: 4

CapNeXt: Unifying Capsule And Resnext For Medical Image Segmentation CapNeXt:统一Capsule和Resnext用于医学图像分割

2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) Pub Date : 2022-03-28 DOI: 10.1109/ISBI52829.2022.9761649

Thanh M. Huynh, C. Nguyen, Khoa N. A. Nguyen, Trung Bui, S. Q. Truong

引用次数: 0

Simultaneous Imaging of Ultrasonic Backscatter and Attenuation Coefficients for Liver Steatosis Detection in a Murine Animal Model 超声后向散射和衰减系数同时成像检测小鼠肝脏脂肪变性

2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) Pub Date : 2022-03-28 DOI: 10.1109/ISBI52829.2022.9761657

José Timaná, Hector Chahuara, Lokesh Basavarajappa, A. Basarab, K. Hoyt, R. Lavarello

{"title":"Simultaneous Imaging of Ultrasonic Backscatter and Attenuation Coefficients for Liver Steatosis Detection in a Murine Animal Model","authors":"José Timaná, Hector Chahuara, Lokesh Basavarajappa, A. Basarab, K. Hoyt, R. Lavarello","doi":"10.1109/ISBI52829.2022.9761657","DOIUrl":"https://doi.org/10.1109/ISBI52829.2022.9761657","url":null,"abstract":"Non-alcoholic fatty liver disease (NAFLD) is one of the most prevalent chronic liver diseases. While early diagnosis is the most effective course of action, NAFLD diagnosis procedures are still limited since they are invasive and have a heavy component of subjectivity. In this paper, we present an approach based on Quantitative ultrasound (QUS) and Support Vector Machines (SVM) to detect liver steatosis based on the estimation of backscatter (BSC) and attenuation coefficients (AC) in a murine animal model. We tested our proposed method with data acquired from a population of 21 rats that were randomly divided into two groups subjected to two different diets. The results yielded by the estimation method at 15 MHz show a clear difference in the estimated QUS modalities in healthy liver, where BSC and AC mean and standard deviation values were found to be 0.22 ± 0.28 cm−1• sr−1 and 0.54 ± 0.03 dB MHz−1• cm−1, respectively, with respect to fatty liver, where BSC• and AC mean values were found to be 0.74 ± 0.80 cm−1 • sr−1 and 0.64 ± 0.06 dB • MHz−1• cm−1, respectively. Furthermore, the SVM achieved an accuracy of 97.6% when discriminating between healthy and steatotic liver, thus constituting a promising alternative for non-invasive NAFLD diagnosis.","PeriodicalId":6827,"journal":{"name":"2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)","volume":"137 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":"79575753","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: 3

Addformer: Alzheimer’s Disease Detection from Structural Mri Using Fusion Transformer adformer:利用融合变压器从结构Mri检测阿尔茨海默病

2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) Pub Date : 2022-03-28 DOI: 10.1109/ISBI52829.2022.9761421

Rafsanjany Kushol, Abbas Masoumzadeh, Dong Huo, S. Kalra, Yee-Hong Yang

{"title":"Addformer: Alzheimer’s Disease Detection from Structural Mri Using Fusion Transformer","authors":"Rafsanjany Kushol, Abbas Masoumzadeh, Dong Huo, S. Kalra, Yee-Hong Yang","doi":"10.1109/ISBI52829.2022.9761421","DOIUrl":"https://doi.org/10.1109/ISBI52829.2022.9761421","url":null,"abstract":"Alzheimer’s disease is the most prevalent neurodegenerative disorder characterized by degeneration of the brain. It is classified as a brain disease causing dementia that presents with memory loss and cognitive impairment. Experts primarily use brain imaging and other tests to rule out the disease. To automatically detect Alzheimer’s patients from healthy controls, this study adopts the vision transformer architecture, which can effectively capture the global or long-range relationship of image features. To further enhance the network’s performance, frequency and image domain features are fused together since MRI data is acquired in the frequency domain before being transformed to images. We train the model with selected coronal 2D slices to leverage the transfer learning property of pre-training the network using ImageNet. Finally, the majority voting of the coronal slices of an individual subject is used to generate the final classification score. Our proposed method has been evaluated on the publicly available benchmark dataset ADNI. The experimental results demonstrate the advantage of our proposed approach in terms of classification accuracy compared with that of the state-of-the-art methods. Our code is available at https://github.com/rkushol/ADDFormer.","PeriodicalId":6827,"journal":{"name":"2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)","volume":"27 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":"74762713","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