2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops)最新文献_第2页

Advances in Geometrical Analysis of Topologically-Varying Shapes 拓扑变化形状的几何分析研究进展

2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops) Pub Date : 2020-04-01 DOI: 10.1109/ISBIWorkshops50223.2020.9153426

Anuj Srivastava, Xiaoyang Guo, Hamid Laga

引用次数: 2

Deeply-Supervised Multi-Dose Prior Learning For Low-Dose Pet Imaging 用于低剂量Pet成像的深度监督多剂量先验学习

2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops) Pub Date : 2020-04-01 DOI: 10.1109/ISBIWorkshops50223.2020.9153450

Yu Gong, Hongming Shan, Yueyang Teng, Hairong Zheng, Ge Wang, Shanshan Wang

引用次数: 2

Pixel-Based Iris and Pupil Segmentation in Cataract Surgery Videos Using Mask R-CNN 基于像素的基于R-CNN掩膜的白内障手术视频虹膜和瞳孔分割

2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops) Pub Date : 2020-04-01 DOI: 10.1109/ISBIWorkshops50223.2020.9153367

Natalia Sokolova, M. Taschwer, S. Sarny, Doris Putzgruber-Adamitsch, K. Schoeffmann

{"title":"Pixel-Based Iris and Pupil Segmentation in Cataract Surgery Videos Using Mask R-CNN","authors":"Natalia Sokolova, M. Taschwer, S. Sarny, Doris Putzgruber-Adamitsch, K. Schoeffmann","doi":"10.1109/ISBIWorkshops50223.2020.9153367","DOIUrl":"https://doi.org/10.1109/ISBIWorkshops50223.2020.9153367","url":null,"abstract":"Automatically detecting clinically relevant events in surgery video recordings is becoming increasingly important for documentary, educational, and scientific purposes in the medical domain. From a medical image analysis perspective, such events need to be treated individually and associated with specific visible objects or regions. In the field of cataract surgery (lens replacement in the human eye), pupil reaction (dilation or restriction) during surgery may lead to complications and hence represents a clinically relevant event. Its detection requires automatic segmentation and measurement of pupil and iris in recorded video frames. In this work, we contribute to research on pupil and iris segmentation methods by (1) providing a dataset of 82 annotated images for training and evaluating suitable machine learning algorithms, and (2) applying the Mask R-CNN algorithm to this problem, which—in contrast to existing techniques for pupil segmentation—predicts free-form pixel-accurate segmentation masks for iris and pupil. The proposed approach achieves consistent high segmentation accuracies on several metrics while delivering an acceptable prediction efficiency, establishing a promising basis for further segmentation and event detection approaches on eye surgery videos.","PeriodicalId":329356,"journal":{"name":"2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops)","volume":"3 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":"128042617","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

Csrgan: Medical Image Super-Resolution Using A Generative Adversarial Network 使用生成对抗网络的医学图像超分辨率

2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops) Pub Date : 2020-04-01 DOI: 10.1109/ISBIWorkshops50223.2020.9153436

Yongpei Zhu, Zicong Zhou, G. Liao, Kehong Yuan

引用次数: 7

Material Decomposition Problem in Spectral CT: A Transfer Deep Learning Approach 光谱CT中的材料分解问题:一种迁移深度学习方法

2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops) Pub Date : 2020-04-01 DOI: 10.1109/ISBIWorkshops50223.2020.9153440

J. Abascal, N. Ducros, V. Pronina, S. Bussod, A. Hauptmann, S. Arridge, P. Douek, F. Peyrin

引用次数: 5

DP2 Block: An Improved Multi-Scale Block for Pulmonary Nodule Detection DP2块:一种改进的肺结节多尺度块检测方法

2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops) Pub Date : 2020-04-01 DOI: 10.1109/ISBIWorkshops50223.2020.9153448

Hao Zhang, Haoqian Wang, Yongbing Zhang, Yanbin Peng

{"title":"DP2 Block: An Improved Multi-Scale Block for Pulmonary Nodule Detection","authors":"Hao Zhang, Haoqian Wang, Yongbing Zhang, Yanbin Peng","doi":"10.1109/ISBIWorkshops50223.2020.9153448","DOIUrl":"https://doi.org/10.1109/ISBIWorkshops50223.2020.9153448","url":null,"abstract":"Pulmonary nodule detection is a challenging problem in biomedical imaging. Most existing approaches exploit the multi-scale features at a layer level to detect nodule. However, the effect of features at a layer level is limited. This study proposes an improved architecture unit, which we term the 3D DP2 block. Just as its name implies, it is improved by the idea of the 3D dual-path network. It combines multiscale features not only in a layer-wise manner but also at a granular level, which means it can combine global features with local and increases the scales of receptive fields. Moreover, we adopt the coordination-guided convolutional layers (CoordConvs) and design a loss function inspired by the loss of Fast R-CNN. The proposed 3D DP2 block can be easily plugged into the backbone CNN architectures such as the U-Net model without additional parameters introduced while increasing the model accuracy. Our 3D DP2 block based on U-Net is validated on a public LUNA16 dataset. It improves the nodule detection accuracy compared with the baseline model. This demonstrates that pulmonary nodule detection can highly benefit from the multi-scale features at a granular level. And the proposed 3D DP2Net should be useful to other medical detection problems.","PeriodicalId":329356,"journal":{"name":"2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops)","volume":"291 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":"116051082","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

Detection Of Foreign Objects In Chest Radiographs Using Deep Learning 基于深度学习的胸片异物检测

2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops) Pub Date : 2020-04-01 DOI: 10.1109/ISBIWorkshops50223.2020.9153350

Hrishikesh Deshpande, T. Harder, A. Saalbach, A. Sawarkar, T. Buelow

{"title":"Detection Of Foreign Objects In Chest Radiographs Using Deep Learning","authors":"Hrishikesh Deshpande, T. Harder, A. Saalbach, A. Sawarkar, T. Buelow","doi":"10.1109/ISBIWorkshops50223.2020.9153350","DOIUrl":"https://doi.org/10.1109/ISBIWorkshops50223.2020.9153350","url":null,"abstract":"We propose a deep learning framework for the automated detection of foreign objects in chest radiographs. Foreign objects can affect the diagnostic quality of an image and could affect the performance of CAD systems. Their automated detection could alert the technologists to take corrective actions. In addition, the detection of foreign objects such as pacemakers or placed devices could also help automate clinical workflow. We used a subset of the MIMIC CXR dataset and annotated 6061 images for six foreign object categories namely tubes and wires, pacemakers, implants, small external objects, jewelry and push-buttons. A transfer learning based approach was developed for both binary and multi-label classification. All networks were pre-trained using the computer vision database ImageNet and the NIH database ChestX-ray14. The evaluation was performed using 5-fold cross-validation (CV) with 4704 images and an additional test set with 1357 images. We achieved the best average area under the ROC curve (AUC) of 0.972 for binary classification and 0.969 for multilabel classification using 5-fold CV. On the test dataset, the respective best AUCs of 0.984 and 0.969 were obtained using a dense convolutional network.","PeriodicalId":329356,"journal":{"name":"2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops)","volume":"112 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":"124753325","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

Preliminary Studies On Training And Fine-Tuning Deep Denoiser Neural Networks In Learned D-Amp For Undersampled Real Mr Measurements 欠采样实际Mr测量中学习D-Amp深度去噪神经网络的训练与微调的初步研究

2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops) Pub Date : 2020-04-01 DOI: 10.1109/ISBIWorkshops50223.2020.9153368

Hanvit Kim, Dong Un Kang, S. Chun

{"title":"Preliminary Studies On Training And Fine-Tuning Deep Denoiser Neural Networks In Learned D-Amp For Undersampled Real Mr Measurements","authors":"Hanvit Kim, Dong Un Kang, S. Chun","doi":"10.1109/ISBIWorkshops50223.2020.9153368","DOIUrl":"https://doi.org/10.1109/ISBIWorkshops50223.2020.9153368","url":null,"abstract":"Recently, deep learning based MR image reconstructions have shown outstanding performance. While there have been many direct mapping based methods by deep neural networks without taking advantage of known physical model of medical imaging modality, some groups investigated combining conventional model-based image reconstruction (MBIR) and learning based method to enhance performance and computation speed of MBIR. Here, we investigated learned denoiser-based approximate message passing (LDAMP) with undersampled MR measurements. LDAMP yielded favorable performance over BM3D-based AMP even though ground truth images were noisy and deep denoisers were trained only for Gaussian noise, not for undersampling artifacts. We further investigated the feasibility of using Stein’s unbiased risk estimator (SURE) to fine-tune deep denoisers with given undersampled MR measurement. Our proposed SURE based unsupervised fine-tuning method faithfully reconstructed images corresponding to the measurement and demonstrated the potential of enhancing the image quality of LDAMP results on real MRI dataset.","PeriodicalId":329356,"journal":{"name":"2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops)","volume":"81 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":"116975557","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

ISBI Workshops 2020 Author Index ISBI研讨会2020作者索引

2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops) Pub Date : 2020-04-01 DOI: 10.1109/isbiworkshops50223.2020.9153371

引用次数: 0

Lung Lobe Segmentation With Automated Quality Assurance Using Deep Convolutional Neural Networks 使用深度卷积神经网络的自动质量保证肺叶分割

2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops) Pub Date : 2020-04-01 DOI: 10.1109/ISBIWorkshops50223.2020.9153455

Sundaresh Ram, S. Humphries, D. Lynch, C. Galbán, C. Hatt

引用次数: 2