2013 IEEE 10th International Symposium on Biomedical Imaging最新文献_第10页

Predicting lung tumor evolution during radiotherapy from PET images using a patient specific model 使用患者特异性模型从PET图像预测放射治疗期间肺肿瘤的演变

2013 IEEE 10th International Symposium on Biomedical Imaging Pub Date : 2013-04-07 DOI: 10.1109/ISBI.2013.6556796

Hongmei Mi, C. Petitjean, S. Ruan, P. Vera, B. Dubray

引用次数: 5

Loosely coupled level sets for retinal layer segmentation in optical coherence tomography 光学相干断层成像中视网膜层分割的松耦合水平集

2013 IEEE 10th International Symposium on Biomedical Imaging Pub Date : 2013-04-07 DOI: 10.1109/ISBI.2013.6556648

J. Novosel, Koen A. Vermeer, G. Thepass, H. Lemij, L. Vliet

引用次数: 19

Graph walks for classification of histopathological images 用于组织病理图像分类的图行走

2013 IEEE 10th International Symposium on Biomedical Imaging Pub Date : 2013-04-07 DOI: 10.1109/ISBI.2013.6556677

Gulden Olgun, C. Sokmensuer, Cigdem Demir

引用次数: 4

Compressed sensing HARDI via rotation-invariant concise dictionaries, flexible K-space undersampling, and multiscale spatial regularity 压缩感知HARDI通过旋转不变简洁字典，灵活的k空间欠采样和多尺度空间规则

2013 IEEE 10th International Symposium on Biomedical Imaging Pub Date : 2013-04-07 DOI: 10.1109/ISBI.2013.6556399

Suyash P. Awate, E. D. Bella

{"title":"Compressed sensing HARDI via rotation-invariant concise dictionaries, flexible K-space undersampling, and multiscale spatial regularity","authors":"Suyash P. Awate, E. D. Bella","doi":"10.1109/ISBI.2013.6556399","DOIUrl":"https://doi.org/10.1109/ISBI.2013.6556399","url":null,"abstract":"Current methods to reduce acquisition time for high angular resolution diffusion imaging (HARDI) (i) employ large dictionaries where atoms explicitly model finitely-many tract orientations, limiting estimation accuracy of the true tract orientation, (ii) subsample gradient directions only, ignoring k-space undersampling for diffusion-weighted images, (iii) restrict to sparse models that use either frames or dictionaries, and (iv) enforce spatial regularity by penalizing total variation. This paper proposes rotation-invariant dictionaries, enabling a concise dictionary (few atoms representing key diffusion-signal types) by explicitly optimizing the rotation for each atom during sparse fitting. The proposed framework generalizes undersampling strategies to both k-space and gradient directions, thereby enabling a balanced undersampling of k-space over all directions. This paper combines frames and dictionaries for sparse modeling HARDI images. The frame model reduces the need for large intricate dictionaries and enforces spatial regularity over multiple scales. Results on simulated and clinical undersampled HARDI show improved reconstructions via the proposed framework.","PeriodicalId":178011,"journal":{"name":"2013 IEEE 10th International Symposium on Biomedical Imaging","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130385500","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: 20

Variational attenuation correction of two-view confocal microscopic recordings 双视角共聚焦显微记录的变分衰减校正

2013 IEEE 10th International Symposium on Biomedical Imaging Pub Date : 2013-04-07 DOI: 10.1109/ISBI.2013.6556439

Thorsten Schmidt, J. Dürr, M. Keuper, T. Blein, K. Palme, O. Ronneberger

引用次数: 2

A radiographic-based method for marginal bone loss measurement in dental implants 牙种植体边缘骨丢失测量的影像学方法

2013 IEEE 10th International Symposium on Biomedical Imaging Pub Date : 2013-04-07 DOI: 10.1109/ISBI.2013.6556429

E. Veronese, M. Veronese, S. Sivolella, E. Grisan

引用次数: 2

Fiber orientation distribution from non-negative sparse recovery 非负稀疏恢复的纤维取向分布

2013 IEEE 10th International Symposium on Biomedical Imaging Pub Date : 2013-04-07 DOI: 10.1109/ISBI.2013.6556460

Aurobrata Ghosh, Thinhinane Megherbi, Fatima Oulebsir-Boumghar, R. Deriche

{"title":"Fiber orientation distribution from non-negative sparse recovery","authors":"Aurobrata Ghosh, Thinhinane Megherbi, Fatima Oulebsir-Boumghar, R. Deriche","doi":"10.1109/ISBI.2013.6556460","DOIUrl":"https://doi.org/10.1109/ISBI.2013.6556460","url":null,"abstract":"We revisit the theory of spherical deconvolution and propose a new fiber orientation distribution (FOD) model that can efficiently reconstruct extremely narrow fiber-crossings from limited number of acquisitions. First, we show how to physically model fiber-orientations as rank-1 tensors. Then, we parameterize the FODs with tensors that are decomposable into non-negative sums of rank-1 tensors and finally, we propose a non-negative sparse recovery scheme to estimate FODs of any tensor order from limited acquisitions. Our method features three important advantages: (1) it estimates non-negative FODs, (2) it estimates the number of fiber-compartments, which need not be predefined and (3) it computes the fiber-directions directly, rendering maxima detection superfluous. We test for various SNRs on synthetic, phantom and real data and find our method accurate and robust to signal-noise: fibers crossing up to 23° are recovered from just 21 acquisitions. This opens new and exciting perspectives in diffusion MRI (dMRI), where our improved characterization of the FOD can be of great help for applications such as tractography.","PeriodicalId":178011,"journal":{"name":"2013 IEEE 10th International Symposium on Biomedical Imaging","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129353337","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: 8

Cortex parcellation via diffusion data as prior knowledge for the MEG inverse problem 通过扩散数据作为先验知识对脑磁图逆问题进行皮层分割

2013 IEEE 10th International Symposium on Biomedical Imaging Pub Date : 2013-04-07 DOI: 10.1109/ISBI.2013.6556644

A. Philippe, Maureen Clerc, T. Papadopoulo, R. Deriche

引用次数: 2

An alternative method to classical beamforming for transverse oscillation images: Application to elastography 横向振荡图像的经典波束形成替代方法:在弹性成像中的应用

2013 IEEE 10th International Symposium on Biomedical Imaging Pub Date : 2013-04-07 DOI: 10.1109/ISBI.2013.6556575

F. Varray, H. Liebgott

引用次数: 16

Anatomical landmark detection using multiple instance boosting with spatial regularization 基于空间正则化的多实例增强解剖地标检测

2013 IEEE 10th International Symposium on Biomedical Imaging Pub Date : 2013-04-07 DOI: 10.1109/ISBI.2013.6556451

P. Swoboda, David Liu, S. Zhou

{"title":"Anatomical landmark detection using multiple instance boosting with spatial regularization","authors":"P. Swoboda, David Liu, S. Zhou","doi":"10.1109/ISBI.2013.6556451","DOIUrl":"https://doi.org/10.1109/ISBI.2013.6556451","url":null,"abstract":"We propose a novel multiple instance boosting approach with spatial regularization for detecting anatomical landmark to alleviate the manual annotation burden and to address imprecise annotations. It features three contributions. The first is the introduction of soft max cost function for better handling the practical situation in object detection that most positive bags only contain very few true positives while including the ISR rule and AdaBoost as special examples. The second is to exploit for better detection the spatial context embedded in a medical image, specifically the grid arrangement of the training instances with strong correlation. This is in contrast with conventional methods that treat instances in a bag independently. The third is to encourage a concentrated detection response map so that the final detection result can be derived with more confidence. The latter two contributions are realized using total variation regularization. Experimentally the proposed approach achieves significantly better detection performance than state-of-the-art detection methods in detecting anatomical landmarks with few or even no annotations.","PeriodicalId":178011,"journal":{"name":"2013 IEEE 10th International Symposium on Biomedical Imaging","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125562911","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