2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)最新文献

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GORDA: Graph-Based Orientation Distribution Analysis of SLI Scatterometry Patterns of Nerve Fibres 神经纤维SLI散射测量模式的基于图的取向分布分析
2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) Pub Date : 2022-03-28 DOI: 10.48550/arXiv.2204.05776
Esteban Vaca, M. Menzel, K. Amunts, M. Axer, Timo Dickscheid
{"title":"GORDA: Graph-Based Orientation Distribution Analysis of SLI Scatterometry Patterns of Nerve Fibres","authors":"Esteban Vaca, M. Menzel, K. Amunts, M. Axer, Timo Dickscheid","doi":"10.48550/arXiv.2204.05776","DOIUrl":"https://doi.org/10.48550/arXiv.2204.05776","url":null,"abstract":"Scattered Light Imaging (SLI) is a novel approach for microscopically revealing the fibre architecture of unstained brain sections. The measurements are obtained by illuminating brain sections from different angles and measuring the transmitted (scattered) light under normal incidence. The evaluation of scattering profiles commonly relies on a peak picking technique and feature extraction from the peaks, which allows quantitative determination of parallel and crossing in-plane nerve fibre directions for each image pixel. However, the estimation of the 3D orientation of the fibres cannot be assessed with the traditional methodology. We propose an unsupervised learning approach using spherical convolutions for estimating the 3D orientation of neural fibres, resulting in a more detailed interpretation of the fibre orientation distributions in the brain.","PeriodicalId":6827,"journal":{"name":"2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)","volume":"19 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":"75548682","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
Identification of Diffusive States in Tracking Applications Using Unsupervised Deep Learning Methods 利用无监督深度学习方法识别跟踪应用中的扩散状态
2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) Pub Date : 2022-03-28 DOI: 10.1109/ISBI52829.2022.9761672
Hélène Kabbech, Ihor Smal
{"title":"Identification of Diffusive States in Tracking Applications Using Unsupervised Deep Learning Methods","authors":"Hélène Kabbech, Ihor Smal","doi":"10.1109/ISBI52829.2022.9761672","DOIUrl":"https://doi.org/10.1109/ISBI52829.2022.9761672","url":null,"abstract":"The most widely used method for analysis of diffusive motion in particle tracking is based on estimation of the mean squared displacement (MSD) and subsequently relevant motion parameters. This approach is only valid for a population of particles exhibiting a single type of motion (e.g., super or sub-diffusive). Thus, to deal with trajectories that describe dynamics with switching motion patterns, trajectory segmentation techniques are of major importance.Here, we propose an unsupervised trajectory segmentation technique, which employs the ideas of the state-of-the-art image denoising \"noise2noise\" approach. Using typical single-particle tracking data, our method is capable of unsupervised trajectory segmentation in the most difficult situations (e.g. unknown number of purely diffusive states), and computation of the relevant parameters. The applicability of the method is demonstrated using simulated and real experimental data, showing that its performance is comparable to similar top performing supervised methods.","PeriodicalId":6827,"journal":{"name":"2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)","volume":"142 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":"80204092","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
Bounding Box Based Weakly Supervised Deep Convolutional Neural Network for Medical Image Segmentation Using an Uncertainty Guided and Spatially Constrained Loss 基于边界盒的弱监督深度卷积神经网络的不确定性引导和空间约束损失医学图像分割
2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) Pub Date : 2022-03-28 DOI: 10.1109/ISBI52829.2022.9761558
Golnar K. Mahani, Ruizhe Li, N. Evangelou, Stamatios Sotiropolous, P. Morgan, A. French, Xin Chen
{"title":"Bounding Box Based Weakly Supervised Deep Convolutional Neural Network for Medical Image Segmentation Using an Uncertainty Guided and Spatially Constrained Loss","authors":"Golnar K. Mahani, Ruizhe Li, N. Evangelou, Stamatios Sotiropolous, P. Morgan, A. French, Xin Chen","doi":"10.1109/ISBI52829.2022.9761558","DOIUrl":"https://doi.org/10.1109/ISBI52829.2022.9761558","url":null,"abstract":"In this paper, we propose a weakly supervised deep convolutional neural network for medical image segmentation using an uncertainty guided and spatially constrained loss, which only requires bounding box annotations for model training. We utilise predictive uncertainty estimation during training to guide the model learning from the image region with high predictive confidence. Additionally, a conditional random field (CRF) based local spatial constraint is incorporated to the loss function, which regularises the predicted labels of a local region. This CRF loss term is independent to the training labels (bounding box annotation), which prevents the model over-fitted to the bounding box annotation. We evaluated our method on a public dermoscopic dataset containing different types of skin lesions. Our method achieved superior performance in comparison with the state-of-the-art learning based (DeepCut) and non-learning based (GrabCut) methods in terms of dice coefficient. The code is available on Github (https://github.com/golnarkmahani/Weakly-Supervised-Segmentation).","PeriodicalId":6827,"journal":{"name":"2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)","volume":"2 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":"80216619","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
Facile Prediction of Neutrophil Activation State from Microscopy Images: A New Dataset and Comparative Deep Learning Approaches 从显微镜图像中简单预测中性粒细胞激活状态:一个新的数据集和比较深度学习方法
2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) Pub Date : 2022-03-28 DOI: 10.1109/ISBI52829.2022.9761554
Wei-Duen Liao, Ching-Yun Ko, Tsui-Wei Weng, Lucani E. Daniel, J. Voldman
{"title":"Facile Prediction of Neutrophil Activation State from Microscopy Images: A New Dataset and Comparative Deep Learning Approaches","authors":"Wei-Duen Liao, Ching-Yun Ko, Tsui-Wei Weng, Lucani E. Daniel, J. Voldman","doi":"10.1109/ISBI52829.2022.9761554","DOIUrl":"https://doi.org/10.1109/ISBI52829.2022.9761554","url":null,"abstract":"The immune system protects its host from infection. Dysfunction of the immune system can cause autoimmune diseases and inflammatory diseases. Monitoring the immune system provides crucial information in informing treatment strategies and assessing the effect of therapies. While measures such as complete blood count to determine the leukocyte subsets are extensively used clinically, our ability to assess leukocyte function is limited, especially for the cells of the innate immune system, such as neutrophils. Here we introduce the idea of assessing neutrophil function from simple-to-obtain phase microscopy images. We developed an experimental pipeline using measurement of reactive oxygen species generation as a label of neutrophil function. We generated a large neutrophil imaging dataset and explored different deep learning approaches to predict neutrophil activation state. Our work demonstrates the potential of using deep learning models to evaluate functional aspects of the immune system, which could provide significant insight into immune disease prognostic monitoring that can be easily adapted to clinical settings.","PeriodicalId":6827,"journal":{"name":"2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)","volume":"79 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":"80314395","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
Symmetric Contrastive Loss for Out-of-Distribution Skin Lesion Detection 非分布皮肤病变检测的对称对比损失
2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) Pub Date : 2022-03-28 DOI: 10.1109/ISBI52829.2022.9761434
Xuan Li, Christian Desrosiers, Xue Liu
{"title":"Symmetric Contrastive Loss for Out-of-Distribution Skin Lesion Detection","authors":"Xuan Li, Christian Desrosiers, Xue Liu","doi":"10.1109/ISBI52829.2022.9761434","DOIUrl":"https://doi.org/10.1109/ISBI52829.2022.9761434","url":null,"abstract":"Detecting out-of-distribution (OOD) data has been a challenging task for deep learning models trained with real-life datasets. This work studies OOD detection in medical images where inter-class difference (e.g., variations in visual appearance across separate diseases) outweighs intra-class difference (e.g., same disease but on different locations or people). To improve OOD detection performance, we propose a self-supervised learning approach that can better capture inter-/intra-class variance using a novel symmetric contrastive loss. Two large-scale, publicly-available skin lesion datasets, HAM10000 and DermNet, are adopted in our study. Comprehensive experiments, including three different distributional shifts, disease-specific OOD detection, as well as an adversarial attack, are conducted to validate the effectiveness of our approach.","PeriodicalId":6827,"journal":{"name":"2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)","volume":"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":"77211250","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
Trans-ResNet: Integrating Transformers and CNNs for Alzheimer’s disease classification Trans-ResNet:整合变压器和cnn用于阿尔茨海默病分类
2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) Pub Date : 2022-03-28 DOI: 10.1109/ISBI52829.2022.9761549
Chao Li, Yue Cui, Na Luo, Yong Liu, P. Bourgeat, J. Fripp, Tianzi Jiang
{"title":"Trans-ResNet: Integrating Transformers and CNNs for Alzheimer’s disease classification","authors":"Chao Li, Yue Cui, Na Luo, Yong Liu, P. Bourgeat, J. Fripp, Tianzi Jiang","doi":"10.1109/ISBI52829.2022.9761549","DOIUrl":"https://doi.org/10.1109/ISBI52829.2022.9761549","url":null,"abstract":"Convolutional neural networks (CNNs) have demonstrated excellent performance for brain disease classification from MRI data. However, CNNs lack the ability to capture global dependencies. The recently proposed architecture called Transformer uses attention mechanisms to match or even outperform CNNs on various vision tasks. Transformer’s performance is dependent on access to large training datasets, but sample sizes for most brain MRI datasets are relatively small. To overcome this limitation, we propose Trans-ResNet, a novel architecture which integrates the advantages of both CNNs and Transformers. In addition, we pre-trained our Trans-ResNet on a large-scale dataset on the task of brain age estimation for higher performance. Using three neuroimaging cohorts (UK Biobank, AIBL, ADNI), we demonstrated that our Trans-ResNet achieved higher classification accuracy on Alzheimer disease prediction compared to other state-of-the-art CNN-based methods.","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":"73253663","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
Omni-Supervised Domain Adversarial Training for White Matter Hyperintensity Segmentation in the UK Biobank 英国生物银行白质高强度分割的全监督域对抗训练
2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) Pub Date : 2022-03-28 DOI: 10.1109/ISBI52829.2022.9761539
V. Sundaresan, N. Dinsdale, M. Jenkinson, L. Griffanti
{"title":"Omni-Supervised Domain Adversarial Training for White Matter Hyperintensity Segmentation in the UK Biobank","authors":"V. Sundaresan, N. Dinsdale, M. Jenkinson, L. Griffanti","doi":"10.1109/ISBI52829.2022.9761539","DOIUrl":"https://doi.org/10.1109/ISBI52829.2022.9761539","url":null,"abstract":"White matter hyperintensities (WMHs, or lesions) appear as hyperintense, localized regions on T2-weighted and FLAIR brain MR images. The heterogeneity in lesion characteristics due to subject-level (e.g., local intensity/contrast) and population-level (e.g., demographic, scanner-related) variations make their segmentation highly challenging. Here, we propose a framework for adapting a state-of-the-art WMH segmentation method with high accuracy from a small, labeled source data (MICCAI WMH segmentation challenge 2017 training data) to a larger dataset such as the UK Biobank without the need of additional manual training labels, using domain adversarial training with omni-supervised learning. Given the well-known association of WMHs with age, the proposed method uses a multi-tasking model for learning lesion segmentation, domain adaptation and age prediction simultaneously. On a subset of the UK Biobank dataset, the proposed method achieves a lesion-level recall, lesion-level F1-measure and Dice overlap value of 0.95, 0.65 and 0.84 respectively, when compared to values of 0.75, 0.49 and 0.80 obtained from the pretrained state-of-the-art baseline method. The code for the method is available at https://github.com/v-sundaresan/omnisup_agepred_semidann.","PeriodicalId":6827,"journal":{"name":"2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)","volume":"11 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":"74406398","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
GAN-Based Realistic Gastrointestinal Polyp Image Synthesis 基于gan的逼真胃肠道息肉图像合成
2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) Pub Date : 2022-03-28 DOI: 10.1109/ISBI52829.2022.9761447
Ataher Sams, Homaira Huda Shomee
{"title":"GAN-Based Realistic Gastrointestinal Polyp Image Synthesis","authors":"Ataher Sams, Homaira Huda Shomee","doi":"10.1109/ISBI52829.2022.9761447","DOIUrl":"https://doi.org/10.1109/ISBI52829.2022.9761447","url":null,"abstract":"Polyps in the gastrointestinal (GI) tract in the human body are one of the most significant symptoms of gastric and colorectal cancer and some other diseases. This paper proposes Generative Adversarial Networks (GANs) based methods that first use a StyleGAN2-ada to generate random polyp masks, which are used to create composite images with healthy GI images. Then a conditional GAN is used to translate these composite images into synthetic polyp images. The proposed approach can produce a high amount of realistic GI polyp images and can increase F1-score and IoU in polyp detection by around 4% when used in the training phase of the YOLOv4 object detector.","PeriodicalId":6827,"journal":{"name":"2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)","volume":"15 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":"80963442","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
Evaluation of an Automated Method to Detect Missed Focal Liver Findings In Single-Phase CT Images of The Abdomen 一种自动检测腹部单相CT图像中肝脏遗漏的方法的评价
2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) Pub Date : 2022-03-28 DOI: 10.1109/ISBI52829.2022.9761517
P. Esquinas, Yen-Fu Luo, P. Farzam, Tyler Baldwin, Moshe Raboh, T. Binder, Arkadiusz Sitek, O. Sakhi, Yi-Qing Wang, Sameer Suman, G. Palma, Paul Dufort, Benedikt Graf
{"title":"Evaluation of an Automated Method to Detect Missed Focal Liver Findings In Single-Phase CT Images of The Abdomen","authors":"P. Esquinas, Yen-Fu Luo, P. Farzam, Tyler Baldwin, Moshe Raboh, T. Binder, Arkadiusz Sitek, O. Sakhi, Yi-Qing Wang, Sameer Suman, G. Palma, Paul Dufort, Benedikt Graf","doi":"10.1109/ISBI52829.2022.9761517","DOIUrl":"https://doi.org/10.1109/ISBI52829.2022.9761517","url":null,"abstract":"In the present study, an automated method to identify potential missed focal liver lesions in abdominal CT scans is described and evaluated. The method analyzes radiology reports and DICOM data via natural language processing and deep-learning based imaging algorithms, respectively, aiming to detect and classify liver lesions in studies where the original radiologist found no evidence of them. The proposed approach was evaluated on a cohort of 13500 contrast-enhanced abdominal CT studies and yielded a total of 25 potential missed liver lesions which were subsequently reviewed by 5 independent radiologists. On average, 48.8% of studies flagged by the method contained actual liver lesions not reported by the original radiologist, and 15.2% of all findings were deemed to be clinically significant. The proposed method could be a valuable tool to inform radiologists of potential missed focal liver lesions.","PeriodicalId":6827,"journal":{"name":"2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)","volume":"2015 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":"73551445","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
An Efficient Anchor-Free Universal Lesion Detection in Ct-Scans ct扫描中一种有效的无锚点普遍病变检测方法
2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) Pub Date : 2022-03-28 DOI: 10.1109/ISBI52829.2022.9761698
Manu Sheoran, Meghal Dani, Monika Sharma, L. Vig
{"title":"An Efficient Anchor-Free Universal Lesion Detection in Ct-Scans","authors":"Manu Sheoran, Meghal Dani, Monika Sharma, L. Vig","doi":"10.1109/ISBI52829.2022.9761698","DOIUrl":"https://doi.org/10.1109/ISBI52829.2022.9761698","url":null,"abstract":"Existing universal lesion detection (ULD) methods utilize compute-intensive anchor-based architectures which rely on predefined anchor boxes, resulting in unsatisfactory detection performance, especially in small and mid-sized lesions. Further, these default fixed anchor-sizes and ratios do not generalize well to different datasets. Therefore, we propose a robust one-stage anchor-free lesion detection network that can perform well across varying lesions sizes by exploiting the fact that the box predictions can be sorted for relevance based on their center rather than their overlap with the object. Furthermore, we demonstrate that the ULD can be improved by explicitly providing it the domain-specific information in the form of multi-intensity images generated using multiple HU windows, followed by self-attention based feature-fusion and backbone initialization using weights learned via self-supervision over CT-scans. We obtain comparable results to the state-of-the-art methods, achieving an overall sensitivity of 86.05% on the DeepLesion dataset, which comprises of approximately 32K CT-scans with lesions annotated across various body organs.","PeriodicalId":6827,"journal":{"name":"2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)","volume":"15 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":"86288761","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
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