Medical Imaging: Image Processing最新文献

Transfer generative adversarial network for multimodal CT image super-resolution (Conference Presentation) 多模态CT图像超分辨率的传递生成对抗网络(会议报告)

Medical Imaging: Image Processing Pub Date : 2020-03-17 DOI: 10.1117/12.2549533

Yao Xiao, R. Fang

引用次数: 1

Are today's Mixed Reality experience pillars and hardware architectures well aligned with the specific needs of medical imaging and surgical guidance? (Conference Presentation) 今天的混合现实体验支柱和硬件架构是否与医学成像和手术指导的特定需求相一致?(会议)

Medical Imaging: Image Processing Pub Date : 2020-03-17 DOI: 10.1117/12.2555909

B. Kress

引用次数: 0

Joint intensity fusion with normalized cross-correlation metric for cross-modality MRI synthesis (Conference Presentation) 用归一化互相关度量融合关节强度用于跨模态MRI合成(会议报告)

Medical Imaging: Image Processing Pub Date : 2020-03-17 DOI: 10.1117/12.2550009

Kathryn Ufford, S. Vandekar, I. Oguz

引用次数: 1

Network features of simultaneous EEG and fMRI predict working memory load 同时用脑电和功能磁共振成像的网络特征预测工作记忆负荷

Medical Imaging: Image Processing Pub Date : 2020-03-16 DOI: 10.1117/12.2548415

Yutong Wang, Y. Liu, Li Yao, Xiao-jie Zhao

{"title":"Network features of simultaneous EEG and fMRI predict working memory load","authors":"Yutong Wang, Y. Liu, Li Yao, Xiao-jie Zhao","doi":"10.1117/12.2548415","DOIUrl":"https://doi.org/10.1117/12.2548415","url":null,"abstract":"The identification of mental load has important practical significance for human life. In recent years, studies have used physiological signals of different modalities to measure mental load. However, there is no single indicator that can fully assess mental load, and the fusion of multimodal signals to measure this variable is still controversial. The emergence of simultaneous EEG and fMRI enables researchers to explore brain function, especially working memory, with high temporal and spatial resolution. In this study, we fused EEG and fMRI data on network features to estimate working memory load. By introducing a filter bank, the phase synchronization values covering the full frequency band were extracted as the EEG network features. Then, we proposed the Wasserstein distance to measure functional connectivity in single-trial fMRI data. Finally, Fisher vector was used to fuse these features from the above two modalities, and this method was compared with the direct splicing features. The results showed that the Fisher vector was more effective for recognition than direct splicing was, providing a new option for multimodality feature fusion. In addition, distance-based functional connectivity extraction on the individual level enriches the range of tools with which to study brain network.","PeriodicalId":300887,"journal":{"name":"Medical Imaging: Image Processing","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127216905","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

Choroidal atrophy segmentation based on deep network with deep-supervision and EDT-auxiliary-loss 基于深度监督和edt辅助损失的深度网络脉络膜萎缩分割

Medical Imaging: Image Processing Pub Date : 2020-03-10 DOI: 10.1117/12.2547763

Ruyi Lu, Weifang Zhu, Xuena Cheng, Xinjian Chen

{"title":"Choroidal atrophy segmentation based on deep network with deep-supervision and EDT-auxiliary-loss","authors":"Ruyi Lu, Weifang Zhu, Xuena Cheng, Xinjian Chen","doi":"10.1117/12.2547763","DOIUrl":"https://doi.org/10.1117/12.2547763","url":null,"abstract":"The prevalence of myopia is rapidly increasing worldwide. Along with the deepening of myopia, there will be various pathological changes of retina, such as choroidal atrophy, choroidal neovascularization, etc. In this paper, a U-Net based deep network is proposed to automatically segment choroidal atrophy in fundus images. We use U-Net as the main structure, which can learn rich hierarchical feature representations. In the decoder path, Squeeze-and-Excitation (SE) block is employed before each deconvolution to adaptively recalibrate channel feature response. We introduce deep-supervision mechanism and merge all the early prediction maps to obtain final prediction map. To ensure the smoothness of segmentation results, we propose a new loss function, which is termed EDT-auxiliary-loss (Euclidean distance transformation auxiliary loss). EDT-auxiliary-loss consists of Dice loss for ground truth and mean square error (MSE) loss for distance map. Another strategy for performance improvement is utilizing the information of optic disc (OD), which is usually adjacent to atrophy. The proposed method was evaluated on ISBI 2019 Pathologic Myopia Challenge dataset, which consists of 400 fundus images from161 normal eyes, 26 high myopia eyes and 213 pathologic myopia eyes. The proposed network was validated with four-fold cross validation. The experiment results show that the proposed method can successfully segment choroidal atrophy and achieve better performance than traditional U-Net.","PeriodicalId":300887,"journal":{"name":"Medical Imaging: Image Processing","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116915041","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

Hybrid dictionary learning-ICA approaches built on novel instantaneous dynamic connectivity metric provide new multiscale insights into dynamic brain connectivity 基于新的即时动态连接度量的混合字典学习- ica方法为动态大脑连接提供了新的多尺度见解

Medical Imaging: Image Processing Pub Date : 2020-03-10 DOI: 10.1117/12.2549368

Robyn L. Miller, V. Calhoun

{"title":"Hybrid dictionary learning-ICA approaches built on novel instantaneous dynamic connectivity metric provide new multiscale insights into dynamic brain connectivity","authors":"Robyn L. Miller, V. Calhoun","doi":"10.1117/12.2549368","DOIUrl":"https://doi.org/10.1117/12.2549368","url":null,"abstract":"The study of brain network connectivity as a time‐varying property began relatively recently and to date has remained primarily concerned with capturing a handful of discrete static states that characterize connectivity as measured on a timescale shorter than that of the full scan. Capturing representations of temporally evolving patterns of connectivity is a challenging and important next step in fully leveraging the information available in fMRI data. We introduce a constellation of interrelated data‐driven methods that hierarchically employ multichannel 1D sparse convolutional dictionary learning (SCDL) and independent component analysis (ICA) for extracting multiscale time‐varying representations of functional network connectivity (FNC). This work also relies upon a novel wavelet‐based method for computing dynamically varying FNC (dFNC) at each timepoint in the scan, yielding a much more resolved picture of evolving connectivity than currently popular sliding‐window approaches. The methods are validated in application to a large multisite fMRI study of schizophrenia where they expose properties of time‐varying connectivity in schizophrenia patients vs. controls that are surprising based on long‐accepted theories of the disorder.","PeriodicalId":300887,"journal":{"name":"Medical Imaging: Image Processing","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116719358","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

Spatial information-embedded fully convolutional networks for multi-organ segmentation with improved data augmentation and instance normalization 基于改进的数据增强和实例归一化的空间信息嵌入全卷积网络多器官分割

Medical Imaging: Image Processing Pub Date : 2020-03-10 DOI: 10.1117/12.2550496

Chen Shen, Chenglong Wang, H. Roth, M. Oda, Y. Hayashi, K. Misawa, K. Mori

{"title":"Spatial information-embedded fully convolutional networks for multi-organ segmentation with improved data augmentation and instance normalization","authors":"Chen Shen, Chenglong Wang, H. Roth, M. Oda, Y. Hayashi, K. Misawa, K. Mori","doi":"10.1117/12.2550496","DOIUrl":"https://doi.org/10.1117/12.2550496","url":null,"abstract":"The purpose of this paper is to present multi-organ segmentation method using spatial information-embedded fully convolutional networks (FCNs). Semantic segmentation of major anatomical structure from CT volumes is promising to apply in clinical work ows. A multitude of deep-learning-based approaches have been proposed for 3D image processing. With the rapid development of FCNs, the encoder-decoder network architecture is proved to achieved acceptable performance on segmentation tasks. However, it is hard to obtain the spatial information from sub-volumes during training. In this paper, we extend the spatial position information-embeded FCNs which designed for binary segmentation tor multi-class organ segmentation. We introduced gamma correction in data augmentation to improve the FCNs robustness. We compared the FCNs performance with different normalization methods, including batch normalization and instance normalization. Experiment results showed that our modifications positively influence the segmentation performance on abdominal CT dataset. Our highest average dice score achieves 87.2%, while the previous method achieved 86.2%.","PeriodicalId":300887,"journal":{"name":"Medical Imaging: Image Processing","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129878821","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

Fine-grained tumor segmentation on computed tomography slices by leveraging bottom-up and top-down strategies 利用自底向上和自顶向下策略对计算机断层扫描切片进行细粒度肿瘤分割

Medical Imaging: Image Processing Pub Date : 2020-03-10 DOI: 10.1117/12.2550511

Zhenmei Yu, Shuchao Pang, Anan Du, M. Orgun, Yan Wang, Hong Lin

{"title":"Fine-grained tumor segmentation on computed tomography slices by leveraging bottom-up and top-down strategies","authors":"Zhenmei Yu, Shuchao Pang, Anan Du, M. Orgun, Yan Wang, Hong Lin","doi":"10.1117/12.2550511","DOIUrl":"https://doi.org/10.1117/12.2550511","url":null,"abstract":"Fully Convolutional Neural Networks (FCNNs) have been widely employed to solve object segmentation tasks effectively in both the computer vision and medical image processing fields in recent years. In object segmentation, FCNNs play a pixel-level prediction role in generating segmented predictions pixel-by-pixel, but they ignore the relationships among generated pixels on the output image. Moreover, blurry boundaries of predicted objects are another common obstacle in this task, because FCNNs usually generate low-frequency components of an image well, but they lack clear high-frequency information inside. In order to solve these problems, we introduce a top-down strategy by globally considering object shapes and context information. Moreover, based on original pixel-wise loss functions which we call a bottom-up strategy, we formulate the tumor segmentation task as a regression problem by using Jaccard Similarity Coefficient (JSC) which is usually one of the main metrics for evaluating the performance of segmentation methods. We directly propose a JSC loss to further optimize network parameters for globally evaluating the whole outputs of tumors. Furthermore, the new loss also alleviates the effect of severe class imbalanced problems between tumor regions and non-tumor regions when training FCNNs. By leveraging the bottom-up and top-down strategies together, our model can obtain more fine-grained tumor segmentation results and also be easily embedded into any FCNN framework for other object segmentation tasks. Detailed experimental results on common metrics demonstrate the superior performance of our proposed method for Kidney Tumor Segmentation Challenge 2019 among more than 100 involved teams.","PeriodicalId":300887,"journal":{"name":"Medical Imaging: Image Processing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128461266","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

Deep learning and multi-contrast-based denoising for low-SNR Arterial Spin Labeling (ASL) MRI 低信噪比动脉自旋标记(ASL) MRI的深度学习和多对比去噪

Medical Imaging: Image Processing Pub Date : 2020-03-10 DOI: 10.1117/12.2549765

E. Gong, Jia Guo, Jiang Liu, A. Fan, J. Pauly, G. Zaharchuk

引用次数: 9

Feature-based retinal image registration for longitudinal analysis of patients with age-related macular degeneration 基于特征的视网膜图像配准用于年龄相关性黄斑变性患者的纵向分析

Medical Imaging: Image Processing Pub Date : 2020-03-10 DOI: 10.1117/12.2549969

T. D. Silva, Nathan A Hotaling, E. Chew, C. Cukras

引用次数: 3