发布求助
文献互助 智能选刊 最新文献

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

筛选
英文 中文
Differentiable Projection from Optical Coherence Tomography B-Scan without Retinal Layer Segmentation Supervision 无视网膜层分割监督下光学相干断层b扫描的可微投影
2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) Pub Date : 2022-03-28 DOI: 10.1109/ISBI52829.2022.9761656
Dingyi Rong, Jiancheng Yang, Bingbing Ni, B. Ke
{"title":"Differentiable Projection from Optical Coherence Tomography B-Scan without Retinal Layer Segmentation Supervision","authors":"Dingyi Rong, Jiancheng Yang, Bingbing Ni, B. Ke","doi":"10.1109/ISBI52829.2022.9761656","DOIUrl":"https://doi.org/10.1109/ISBI52829.2022.9761656","url":null,"abstract":"Projection map (PM) from optical coherence tomography (OCT) B-scan is an important tool to diagnose retinal diseases, which typically requires retinal layer segmentation. In this study, we present a novel end-to-end framework to predict PMs from B-scans. Instead of segmenting retinal layers explicitly, we represent them implicitly as predicted coordinates. By pixel interpolation on uniformly sampled coordinates between retinal layers, the corresponding PMs could be easily obtained with pooling. Notably, all the operators are differentiable; therefore, this Differentiable Projection Module (DPM) enables end-to-end training with the ground truth of PMs rather than retinal layer segmentation. Our framework produces high-quality PMs, significantly outperforming baselines, including a vanilla CNN without DPM and an optimization-based DPM without a deep prior. Furthermore, the proposed DPM, as a novel neural representation of areas/volumes between curves/surfaces, could be of independent interest for geometric deep learning.","PeriodicalId":6827,"journal":{"name":"2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)","volume":"75 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":"79692338","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
Transformer Graph Network for Coronary Plaque Localization in CCTA 变换图网络在冠状动脉斑块定位中的应用
2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) Pub Date : 2022-03-28 DOI: 10.1109/ISBI52829.2022.9761646
Mario Viti, H. Talbot, N. Gogin
{"title":"Transformer Graph Network for Coronary Plaque Localization in CCTA","authors":"Mario Viti, H. Talbot, N. Gogin","doi":"10.1109/ISBI52829.2022.9761646","DOIUrl":"https://doi.org/10.1109/ISBI52829.2022.9761646","url":null,"abstract":"Coronary CT angiography (CCTA) is the only non-invasive imaging technique that reliably depicts the anatomic extent of Coronary Artery Disease (CAD). While occlusion remains a highly predictive indicator of major cardiovascular events (MACE), there is growing evidence that the presence and characteristics of coronary atherosclerosis provide additional prognostic information. In CCTA calcified plaques display high-intensity Hounsfield Units (HU) representative features while more complex representations characterize high-risk soft plaques. As such, accurate identification and quantification is burdensome and time consuming because of the limited temporal, spatial and contrast resolutions of X-ray scanners. Despite the success of deep learning in medical imaging, automatic localization of coronary plaques and especially soft plaques remains a challenging subject in CCTA vessel analysis. For this study, 150 CCTA scans were retrospectively collected. All patients were accepted at triage with minimal to severe CAD suspicion. Selection was carried out with uniform CAD-RADS severity distribution which normally follows an exponential decay function, thus obtaining a higher than normal concentration of plaques. The proposed method outperforms the state of the art for the localization of diverse types of plaques by exploiting the self-attention mechanism of transformers networks to embed contextual features of the coronary tree.","PeriodicalId":6827,"journal":{"name":"2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)","volume":"52 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":"79044445","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
Spatially-Preserving Flattening for Location-Aware Classification of Findings in Chest X-Rays 保留空间的平坦化方法在胸部x射线中定位感知分类
2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) Pub Date : 2022-03-28 DOI: 10.1109/ISBI52829.2022.9761630
Neha Srivathsa, Razi Mahmood, T. Syeda-Mahmood
{"title":"Spatially-Preserving Flattening for Location-Aware Classification of Findings in Chest X-Rays","authors":"Neha Srivathsa, Razi Mahmood, T. Syeda-Mahmood","doi":"10.1109/ISBI52829.2022.9761630","DOIUrl":"https://doi.org/10.1109/ISBI52829.2022.9761630","url":null,"abstract":"Chest X-rays have become the focus of vigorous deep learning research in recent years due to the availability of large labeled datasets. While classification of anomalous findings is now possible, ensuring that they are correctly localized still remains challenging, as this requires recognition of anomalies within anatomical regions. Existing deep learning networks for fine-grained anomaly classification learn location-specific findings using architectures where the location and spatial contiguity information is lost during the flattening step before classification. In this paper, we present a new spatially preserving deep learning network that preserves location and shape information through auto-encoding of feature maps during flattening. The feature maps, auto-encoder and classifier are then trained in an end-to-end fashion to enable location aware classification of findings in chest X-rays. Results are shown on a large multi-hospital chest X-ray dataset indicating a significant improvement in the quality of finding classification over state-of-the-art methods.","PeriodicalId":6827,"journal":{"name":"2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)","volume":"258 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":"77086136","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
Multi-Planar T2W MRI for an Improved Prostate Cancer Lesion Classification 多平面T2W MRI对前列腺癌病变分类的改进
2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) Pub Date : 2022-03-28 DOI: 10.1109/ISBI52829.2022.9761514
Alvaro Fernandez-Quilez, T. Eftestøl, S. R. Kjosavik, M. G. Olsen, K. Oppedal
{"title":"Multi-Planar T2W MRI for an Improved Prostate Cancer Lesion Classification","authors":"Alvaro Fernandez-Quilez, T. Eftestøl, S. R. Kjosavik, M. G. Olsen, K. Oppedal","doi":"10.1109/ISBI52829.2022.9761514","DOIUrl":"https://doi.org/10.1109/ISBI52829.2022.9761514","url":null,"abstract":"Prostate cancer (PCa) is the fifth leading cause of death world-wide. In spite of the urgency for a timely and accurate diagnostic, the current PCa diagnostic pathway suffers from over-diagnosis of indolent lesions and under-diagnosis of highly invasive ones. The advent of deep learning (DL) techniques has enabled automatic and accurate computer-assisted systems that rival human performance. However, current approaches for PCa diagnostic are heavily reliant on T2w axial MRI, which suffer from low out-of-plane resolution. Sagittal and coronal MRI scans are usually acquired by default along with the axial one but are generally ignored by DL classification algorithms. We propose a multi-stream approach to accommodate sagittal, coronal and axial planes and improve the performance of PCa lesion classification. We evaluate our method on a publicly available dataset and demonstrate that it provides better results when compared with a single-plane approach over a range of different DL architectures.","PeriodicalId":6827,"journal":{"name":"2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)","volume":"44 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":"74128289","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
Adaptation of a Multi-Site Network to a New Clinical Site Via Batch-Normalization Similarity 通过批归一化相似性使多站点网络适应新的临床站点
2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) Pub Date : 2022-03-28 DOI: 10.1109/ISBI52829.2022.9761487
Shira Kasten Serlin, J. Goldberger, H. Greenspan
{"title":"Adaptation of a Multi-Site Network to a New Clinical Site Via Batch-Normalization Similarity","authors":"Shira Kasten Serlin, J. Goldberger, H. Greenspan","doi":"10.1109/ISBI52829.2022.9761487","DOIUrl":"https://doi.org/10.1109/ISBI52829.2022.9761487","url":null,"abstract":"This paper tackles the challenging problem of medical site adaptation; i.e., learning a model from multi-site source data such that it can be modified and adapted to a new site using only unlabeled data from the new site. The method is based on Domain Specific Batch Normalization architecture and uses the Batch Normalization statistics of the new site to find the most similar internal site. The similarity measure is computed in an embedded space of the BN parameters. We evaluated our method on the task of MRI prostate segmentation. Public datasets from six different institutions were used, containing distribution shifts. The experimental results show that the proposed approach outperforms other generalization and adaptation methods.","PeriodicalId":6827,"journal":{"name":"2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)","volume":"14 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":"75247002","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
Denoisereg: Unsupervised Joint Denoising and Registration of Time-Lapse Live Cell Microscopy Images Using Deep Learning Denoisereg:使用深度学习的延时活细胞显微镜图像的无监督联合去噪和配准
2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) Pub Date : 2022-03-28 DOI: 10.1109/ISBI52829.2022.9761507
Kerem Celikay, Vadim O. Chagin, M. C. Cardoso, K. Rohr
{"title":"Denoisereg: Unsupervised Joint Denoising and Registration of Time-Lapse Live Cell Microscopy Images Using Deep Learning","authors":"Kerem Celikay, Vadim O. Chagin, M. C. Cardoso, K. Rohr","doi":"10.1109/ISBI52829.2022.9761507","DOIUrl":"https://doi.org/10.1109/ISBI52829.2022.9761507","url":null,"abstract":"Image registration is important for analysing time-lapse live cell microscopy images. However, this is challenging due to significant image noise and complex cell movement. We propose a novel end-to-end trainable deep neural network for joint denoising and affine registration of temporal live cell microscopy images. Our network is trained unsupervised, and only a single network is required for both tasks which reduces overfitting. Our experiments show that the proposed network performs better than deep affine registration without denoising, and better than sequential deep denoising and affine registration. In combination with deep non-rigid registration, we outperform state-of-the-art non-rigid registration methods.","PeriodicalId":6827,"journal":{"name":"2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)","volume":"114 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":"75330949","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
Weakly-Supervised Lesion Segmentation with Self-Guidance by CT Intensity Clustering 基于CT强度聚类的弱监督自引导病灶分割
2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) Pub Date : 2022-03-28 DOI: 10.1109/ISBI52829.2022.9761552
Xueyu Zhu, A. J. Ma
{"title":"Weakly-Supervised Lesion Segmentation with Self-Guidance by CT Intensity Clustering","authors":"Xueyu Zhu, A. J. Ma","doi":"10.1109/ISBI52829.2022.9761552","DOIUrl":"https://doi.org/10.1109/ISBI52829.2022.9761552","url":null,"abstract":"To aid clinicians diagnose diseases and monitor lesion conditions more efficiently, automated lesion segmentation is a convincing approach. As it is time-consuming and costly to obtain pixel-level annotations, weakly-supervised learning has become a promising trend. Recent works based on Class Activation Mapping (CAM) achieve success for natural images, but they have not fully utilized the intensity property in medical images such that the performance may not be good enough. In this work, we propose a novel weakly-supervised lesion segmentation framework with self-guidance by CT intensity clustering. The proposed method takes full advantages of the properties that CT intensity represents the density of materials and partitions pixels into different groups by intensity clustering. Clusters with high lesion probability determined by the CAM are selected to generate lesion masks. Such lesion masks are used to derive self-guided loss functions which improve the CAM for better lesion segmentation. Our method achieves the Dice score of 0.5874 on the COVID-19 dataset and 0.4534 on the Liver Tumor Segmentation Challenge (LiTS) dataset.","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":"73580829","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
TDM-Stargan: Stargan Using Time Difference Map to Generate Dynamic Contrast-Enhanced Mri from Ultrafast Dynamic Contrast-Enhanced Mri TDM-Stargan: Stargan利用时差图从超快动态增强Mri生成动态增强Mri
2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) Pub Date : 2022-03-28 DOI: 10.1109/ISBI52829.2022.9761463
Young-Tack Oh, Eunsook Ko, Hyunjin Park
{"title":"TDM-Stargan: Stargan Using Time Difference Map to Generate Dynamic Contrast-Enhanced Mri from Ultrafast Dynamic Contrast-Enhanced Mri","authors":"Young-Tack Oh, Eunsook Ko, Hyunjin Park","doi":"10.1109/ISBI52829.2022.9761463","DOIUrl":"https://doi.org/10.1109/ISBI52829.2022.9761463","url":null,"abstract":"Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a sensitive imaging technique to manage many types of cancer including breast cancer. The conventional DCE-MRI takes a long time (7-12 minutes) to acquire and there is a clinical need to reduce scan time. Ultrafast DCE-MRI takes less than a minute to acquire and has sufficient information relative to conventional DCE-MRI. We propose a generative adversarial network (GAN) to generate the delay phase of synthetic conventional DCE-MRI from ultrafast DCE-MRI. We allow our model to better generate the area expected to be a lesion through the difference map of different phases to incorporate time-varying enhancement patterns. The difference map also allows us to generate pseudo tumor labels for segmentation. Our approach was trained and tested on 300 cases using three evaluation metrics. Our method showed better performance (structural similarity index map increase of 11.69%) compared to Pix2Pix baseline method.","PeriodicalId":6827,"journal":{"name":"2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)","volume":"115 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":"76246835","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
Faster R-CNN for IPSC-Derived Mesenchymal Stromal Cells Senescent Detection from Bright-Field Microscopy 基于R-CNN的ipsc间充质间质细胞衰老检测
2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) Pub Date : 2022-03-28 DOI: 10.1109/ISBI52829.2022.9761548
Mingzhu Li, Liang He, Xinglie Wang, Tianfu Wang, Guanghui Yue, Guangqian Zhou, Baiying Lei
{"title":"Faster R-CNN for IPSC-Derived Mesenchymal Stromal Cells Senescent Detection from Bright-Field Microscopy","authors":"Mingzhu Li, Liang He, Xinglie Wang, Tianfu Wang, Guanghui Yue, Guangqian Zhou, Baiying Lei","doi":"10.1109/ISBI52829.2022.9761548","DOIUrl":"https://doi.org/10.1109/ISBI52829.2022.9761548","url":null,"abstract":"iPSC-derived mesenchymal stromal cells (iMSCs) play an important role in cell therapy and regenerative medicine, but the differentiation and proliferation ability of senescent iMSCs decline greatly, which will also bring heterogeneity and potential side effects. The whole senescent degree of iMSCs can only be obtained by vital stain. However, this process will cost a lot of manpower, money and time. To solve this problem, we apply deep learning for automated iMSCs senescent recognition, which can quickly and accurately get the senescent situation of single-cell without staining. The adopted Faster R-CNN uses ResNet as the backbone network with an FPN module. Experiments on the collected dataset show that our method has achieved a detection accuracy of 0.768 in the mixed test set of each generation of cells and the independent test set of each generation of cells.","PeriodicalId":6827,"journal":{"name":"2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)","volume":"13 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":"80109791","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
Sample Alignment for Image-to-Image Translation Based Medical Domain Adaptation 基于医学领域自适应的图像到图像翻译的样本对齐
2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) Pub Date : 2022-03-28 DOI: 10.1109/ISBI52829.2022.9761597
Heng Li, Haofeng Liu, Xiaoxuan Wang, Chenlang Yi, Hao Chen, Yan Hu, Jiang Liu
{"title":"Sample Alignment for Image-to-Image Translation Based Medical Domain Adaptation","authors":"Heng Li, Haofeng Liu, Xiaoxuan Wang, Chenlang Yi, Hao Chen, Yan Hu, Jiang Liu","doi":"10.1109/ISBI52829.2022.9761597","DOIUrl":"https://doi.org/10.1109/ISBI52829.2022.9761597","url":null,"abstract":"Image-to-image (I2I) translation is a popular paradigm in domain adaptation (DA), and has been frequently used to address the lack of labeled data. However, as a result of the sample bias in medical data caused by the attributes of imaging modality or pathology, the I2I translation based DA always suffers from synthesis artifacts. For boosting the DA in medical scenarios, a sample alignment algorithm is proposed to correct the sample bias in medical data. Specifically, diffeomorphic transformation and symmetric resampling are employed to implement the sample alignment. The topological structure in medical samples is first aligned using diffeomorphic transformation. Then paired image data are collected from the aligned samples by symmetric resampling to train the I2I translation models. In the experiment, the proposed algorithm was applied to boost the DA of cross-modality data and pathological ones. Our algorithm not only improved the quality of synthesized images, but also promoted the DA of diagnosis models learned from synthesized data.","PeriodicalId":6827,"journal":{"name":"2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)","volume":"40 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":"90175151","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
首页 上一页 下一页 尾页
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整，为了账户安全，请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们：info@booksci.cn Book学术提供免费学术资源搜索服务，方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号：604180095
Book学术官方微信