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

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Dual-Encoder-Unet For Fast Mri Reconstruction 双编码器- unet用于快速Mri重建
2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops) Pub Date : 2020-04-01 DOI: 10.1109/ISBIWorkshops50223.2020.9153453
Amrit Kumar Jethi, Balamurali Murugesan, K. Ram, M. Sivaprakasam
{"title":"Dual-Encoder-Unet For Fast Mri Reconstruction","authors":"Amrit Kumar Jethi, Balamurali Murugesan, K. Ram, M. Sivaprakasam","doi":"10.1109/ISBIWorkshops50223.2020.9153453","DOIUrl":"https://doi.org/10.1109/ISBIWorkshops50223.2020.9153453","url":null,"abstract":"Deep learning has shown great promise for successful acceleration of MRI data acquisition. A variety of architectures have been proposed to obtain high fidelity image from partially observed kspace or undersampled image. U-Net has demonstrated impressive performance for providing high quality reconstruction from undersampled image data. The recently proposed dAutomap is an innovative approach to directly learn the domain transformation from source kspace to target image domain. However these networks operate only on a single domain where information from the excluded domain is not utilized for reconstruction. This paper provides a deep learning based strategy by simultaneously optimizing both the raw kspace data and undersampled image data for reconstruction. Our experiments demonstrate that, such a hybrid approach can potentially improve reconstruction, compared to deep learning networks that operate solely on a single domain.","PeriodicalId":329356,"journal":{"name":"2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops)","volume":"139 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":"124374422","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: 6
Fusion of Color Bands Using Genetic Algorithm to Segment Melanoma 基于遗传算法的色带融合黑色素瘤分割
2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops) Pub Date : 2020-04-01 DOI: 10.1109/ISBIWorkshops50223.2020.9153438
R. L. Araújo, D. Ushizima, Romuere R. V. Silva
{"title":"Fusion of Color Bands Using Genetic Algorithm to Segment Melanoma","authors":"R. L. Araújo, D. Ushizima, Romuere R. V. Silva","doi":"10.1109/ISBIWorkshops50223.2020.9153438","DOIUrl":"https://doi.org/10.1109/ISBIWorkshops50223.2020.9153438","url":null,"abstract":"Melanoma is often associated with changes in the color, size or shape of a mole. Several tools, including smartphone apps, have been developed for the detection of melanoma through medical images. To interpret the information in these images efficiently, it is necessary to isolate key regions of interest. In this research, we analyze the impact of evolutionary computing on the segmentation of melanoma through the fusion of color bands from the images. The tests performed on the PH2 image base showed a 20% improvement in the average Dice compared to using the standard intensity, a promising result toward obtaining more efficient and accurate melanoma screening.","PeriodicalId":329356,"journal":{"name":"2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops)","volume":"47 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":"131669705","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
Data Preprocessing Via Compositions Multi-Channel MRI Images to Improve Brain Tumor Segmentation 基于复合多通道MRI图像的数据预处理改进脑肿瘤分割
2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops) Pub Date : 2020-04-01 DOI: 10.1109/ISBIWorkshops50223.2020.9153416
N. Tolstokulakov, Evgeny Nikolaevich Pavlovskiy, B. Tuchinov, E. Amelina, M. Amelin, A. Letyagin, S. Golushko, V. Groza
{"title":"Data Preprocessing Via Compositions Multi-Channel MRI Images to Improve Brain Tumor Segmentation","authors":"N. Tolstokulakov, Evgeny Nikolaevich Pavlovskiy, B. Tuchinov, E. Amelina, M. Amelin, A. Letyagin, S. Golushko, V. Groza","doi":"10.1109/ISBIWorkshops50223.2020.9153416","DOIUrl":"https://doi.org/10.1109/ISBIWorkshops50223.2020.9153416","url":null,"abstract":"Magnetic resonance imaging (MRI) stays one of the most essential noninvasive methods for brain diagnostics. It allows obtaining the detailed 3D image of the brain, including various types of soft tissues. In this paper, we compare the influence of the multichannel data composition approach on the model’s performance. We consider the binary brain tumor segmentation problem evaluating the Dice, Recall and Precision metrics. One common way to process the medical images with the use of neural networks is to use 2D slices as the input. In contrast to the RGB images, there are plenty of methods of how to combine the multi-channel MRI data structure into the common format for ML-based algorithms. After evaluating several possible combinations we demonstrate the most performance improvement by 6–7% in Dice & Recall metrics using the pseudo-RGB approach.","PeriodicalId":329356,"journal":{"name":"2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops)","volume":"1 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":"131311780","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
Joint Low Dose CT Denoising And Kidney Segmentation 关节低剂量CT去噪与肾脏分割
2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops) Pub Date : 2020-04-01 DOI: 10.1109/ISBIWorkshops50223.2020.9153392
M. Eslami, Solale Tabarestani, M. Adjouadi
{"title":"Joint Low Dose CT Denoising And Kidney Segmentation","authors":"M. Eslami, Solale Tabarestani, M. Adjouadi","doi":"10.1109/ISBIWorkshops50223.2020.9153392","DOIUrl":"https://doi.org/10.1109/ISBIWorkshops50223.2020.9153392","url":null,"abstract":"In this research, both image denoising and kidney segmentation tasks are addressed jointly via one multitask deep convolutional network. This multitasking scheme yields better results for both tasks compared to separate single-task methods. Also, to the best of our knowledge, this is a first time attempt at addressing these joint tasks in low-dose CT scans (LDCT). This new network is a conditional generative adversarial network (C-GAN) and is an extension of the image-to-image translation network. To investigate the generalized nature of the network, two other conventional single task networks are also exploited, including the well-known 2D UNet method for segmentation and the more recently proposed method WGAN for LDCT denoising. Implementation results proved that the proposed method outperforms UNet and WGAN for both tasks.","PeriodicalId":329356,"journal":{"name":"2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops)","volume":"1 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":"116004373","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
Deepsharpen: Deep-Learning Based Sharpening Of 3D Reconstruction Map From Cryo-Electron Microscopy deep锐化:基于深度学习的冷冻电子显微镜三维重建图锐化
2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops) Pub Date : 2020-04-01 DOI: 10.1109/ISBIWorkshops50223.2020.9153369
Mona Zehni, M. Do, Zhizhen Zhao
{"title":"Deepsharpen: Deep-Learning Based Sharpening Of 3D Reconstruction Map From Cryo-Electron Microscopy","authors":"Mona Zehni, M. Do, Zhizhen Zhao","doi":"10.1109/ISBIWorkshops50223.2020.9153369","DOIUrl":"https://doi.org/10.1109/ISBIWorkshops50223.2020.9153369","url":null,"abstract":"Cryo-electron microscopy (cryo-EM) has proven to be a promising tool for recovering the 3D structure of biological macromolecules. The cryo-EM map which is reconstructed from a large set of projection images, is then used for recovering the atomic model of the molecule. The accuracy of the fitted atomic model depends on the quality of the cryo-EM map. Due to current limitations during imaging or reconstruction process, the reconstructed map usually lacks interpretability and requires further quality enhancement post-processing. In this work, we present a data-driven solution to improve the quality of low-resolution cryo-EM maps. For this purpose, we generate a synthetic dataset generated from deposited protein structures in protein data bank (PDB). This dataset includes low and high-resolution map pairs in multiple resolutions. This dataset is then used to train a fully convolutional network. Our results justify the potential of our method in successfully recovering details for simulated and experimental maps. Moreover, compared to state-of-the-art cryo-EM map sharpening methods, our approach not only provides good results but is also computationally efficient.","PeriodicalId":329356,"journal":{"name":"2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops)","volume":"21 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":"132513128","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
ISBI Workshops 2020 Copyright Page
2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops) Pub Date : 2020-04-01 DOI: 10.1109/isbiworkshops50223.2020.9153351
{"title":"ISBI Workshops 2020 Copyright Page","authors":"","doi":"10.1109/isbiworkshops50223.2020.9153351","DOIUrl":"https://doi.org/10.1109/isbiworkshops50223.2020.9153351","url":null,"abstract":"","PeriodicalId":329356,"journal":{"name":"2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops)","volume":"4 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":"126054383","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
Low-Dose Pet Image Restoration With 2D And 3D Network Prior Learning 低剂量Pet图像恢复与2D和3D网络先验学习
2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops) Pub Date : 2020-04-01 DOI: 10.1109/ISBIWorkshops50223.2020.9153435
Yu Gong, Hongming Shan, Yueyang Teng, Hairong Zheng, Ge Wang, Shanshan Wang
{"title":"Low-Dose Pet Image Restoration With 2D And 3D Network Prior Learning","authors":"Yu Gong, Hongming Shan, Yueyang Teng, Hairong Zheng, Ge Wang, Shanshan Wang","doi":"10.1109/ISBIWorkshops50223.2020.9153435","DOIUrl":"https://doi.org/10.1109/ISBIWorkshops50223.2020.9153435","url":null,"abstract":"Reducing the dose of positron emission tomography (PET) imaging is a hot research area for avoiding too much radiation exposure. However, low-dose imaging faces the challenges of different degradation factors such as noise and artifacts. To restore high-quality PET images, we propose a mixed 2D and 3D encoder-decoder network to draw the mapping prior between low-dose and normal-dose PET images under the generative adversarial network framework with Wasserstein distance (WGAN). The proposed method has been evaluated on the in vivo dataset, showing encouraging restoration performances when compared to other state-of-the-art methods.","PeriodicalId":329356,"journal":{"name":"2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops)","volume":"25 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":"133231061","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
A GMM Based Algorithm To Generate Point-Cloud And Its Application To Neuroimaging 基于GMM的点云生成算法及其在神经影像学中的应用
2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops) Pub Date : 2019-11-05 DOI: 10.1109/ISBIWorkshops50223.2020.9153437
Liu Yang, Rudrasis Chakraborty
{"title":"A GMM Based Algorithm To Generate Point-Cloud And Its Application To Neuroimaging","authors":"Liu Yang, Rudrasis Chakraborty","doi":"10.1109/ISBIWorkshops50223.2020.9153437","DOIUrl":"https://doi.org/10.1109/ISBIWorkshops50223.2020.9153437","url":null,"abstract":"Recent years have witnessed the emergence of 3D medical imaging techniques with the development of 3D sensors and technology. Due to the presence of noise in image acquisition, registration is a must, yet incur error which propagates to the subsequent analysis. An alternative way to analyze medical imaging is by understanding the 3D shapes represented in terms of point-cloud. Though in the medical imaging community, 3D point-cloud is not a “go-to” choice, it is a “natural” way to capture 3D shapes. Another hurdle presented in applying deep learning techniques to medical imaging is the lack of samples. A way to overcome this limitation is by generating samples using GAN like schemes. However, due to different modality in medical images, standard generative models can not be applied directly. In this work, we use the advantage of the 3D point-cloud representation of medical images and propose a Gaussian mixture model based generation and interpolation scheme. For interpolation, given two 3D structures represented as point-clouds, we can generate point-clouds in between, and the experimental validation shows the goodness of the interpolated samples. We also generate new point-clouds for subjects with and without dementia and show that the generated samples are indeed closely matched to the respective training samples from the same class.","PeriodicalId":329356,"journal":{"name":"2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops)","volume":"749 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122991190","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
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