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

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Change assessment for CT spine imaging CT脊柱影像学变化评估
2013 IEEE 10th International Symposium on Biomedical Imaging Pub Date : 2013-04-07 DOI: 10.1109/ISBI.2013.6556421
C. Lorenz, T. Netsch, T. Klinder, D. Müller, T. Baum, J. Bauer, P. Noël
{"title":"Change assessment for CT spine imaging","authors":"C. Lorenz, T. Netsch, T. Klinder, D. Müller, T. Baum, J. Bauer, P. Noël","doi":"10.1109/ISBI.2013.6556421","DOIUrl":"https://doi.org/10.1109/ISBI.2013.6556421","url":null,"abstract":"Efficient change quantification is a key issue in follow-up studies. For the support of spine follow up examinations in the context of osteoporosis, we successfully apply in this paper a model based vertebra assessment framework. Automated vertebra labeling and segmentation is accompanied by registration of each vertebra-pair in base-line and follow-up CT scan. The latter is achieved by a point-wise registration of the individualized model meshes. A surface displacement distance measure between pairs of vertebra is introduced and applied to detect interval changes caused by osteoporotic compression fractures of the vertebral body. The associated ROC curve is characterized by an area-under-curve value of 0.97, indicating a high classification power.","PeriodicalId":178011,"journal":{"name":"2013 IEEE 10th International Symposium on Biomedical Imaging","volume":"44 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":"122740759","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
Neuroimaging biomarker based prediction of Alzheimer'S disease severity with optimized graph construction 基于神经成像生物标志物的阿尔茨海默病严重程度预测优化图构建
2013 IEEE 10th International Symposium on Biomedical Imaging Pub Date : 2013-04-07 DOI: 10.1109/ISBI.2013.6556779
Sidong Liu, Weidong (Tom) Cai, L. Wen, D. Feng
{"title":"Neuroimaging biomarker based prediction of Alzheimer'S disease severity with optimized graph construction","authors":"Sidong Liu, Weidong (Tom) Cai, L. Wen, D. Feng","doi":"10.1109/ISBI.2013.6556779","DOIUrl":"https://doi.org/10.1109/ISBI.2013.6556779","url":null,"abstract":"The prediction of Alzheimer's disease (AD) severity is very important in AD diagnosis and patient care, especially for patients at early stage when clinical intervention is most effective and no irreversible damages have been formed to brains. To achieve accurate diagnosis of AD and identify the subjects who have higher risk to convert to AD, we proposed an AD severity prediction method based on the neuroimaging predictors evaluated by the region-wise atrophy patterns. The proposed method introduced a global cost function that encodes the empirical conversion rates for subjects at different progression stages from normal aging through mild cognitive impairment (MCI) to AD, based on the classic graph cut algorithm. Experimental results on ADNI baseline dataset of 758 subjects validated the efficacy of the proposed method.","PeriodicalId":178011,"journal":{"name":"2013 IEEE 10th International Symposium on Biomedical Imaging","volume":"140 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":"133579608","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: 28
Implicit surface registration with surface-oriented anisotropic deformation field smoothing 面向曲面的各向异性变形场平滑隐式曲面配准
2013 IEEE 10th International Symposium on Biomedical Imaging Pub Date : 2013-04-07 DOI: 10.1109/ISBI.2013.6556516
Paul C. Pearlman, I. Išgum, K. J. Kersbergen, M. Benders, M. Viergever, J. Pluim
{"title":"Implicit surface registration with surface-oriented anisotropic deformation field smoothing","authors":"Paul C. Pearlman, I. Išgum, K. J. Kersbergen, M. Benders, M. Viergever, J. Pluim","doi":"10.1109/ISBI.2013.6556516","DOIUrl":"https://doi.org/10.1109/ISBI.2013.6556516","url":null,"abstract":"This paper introduces a variant of the Demon's algorithm with an anisotropic smoothing term designed for the registration of surfaces with significant conformal differences. Because of the chosen implicit surface representation, the deformation field at each iteration can be easily defined with respect to the evolving surface. Anisotropic smoothing is performed preferentially in the tangent plane to the surface, preserving local, complex deformations. This method is applied to the registration of consecutive neonatal cortical surfaces from MR images acquired at 30 and 40 week gestational ages. Our method preserves the emergence of small, new folds, while greatly reducing noise in the resulting deformation field.","PeriodicalId":178011,"journal":{"name":"2013 IEEE 10th International Symposium on Biomedical Imaging","volume":"3 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":"128099566","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 Adaptive Random Forests 空间适应性随机森林
2013 IEEE 10th International Symposium on Biomedical Imaging Pub Date : 2013-04-07 DOI: 10.1109/ISBI.2013.6556781
Ezequiel Geremia, Bjoern H Menze, N. Ayache
{"title":"Spatially Adaptive Random Forests","authors":"Ezequiel Geremia, Bjoern H Menze, N. Ayache","doi":"10.1109/ISBI.2013.6556781","DOIUrl":"https://doi.org/10.1109/ISBI.2013.6556781","url":null,"abstract":"Medical imaging protocols produce large amounts of multimodal volumetric images. The large size of the datasets contributes to the success of supervised discriminative methods for semantic image segmentation. Classifying relevant structures in medical images is challenging due to (a) the large size of data volumes, and (b) the severe class overlap in the feature space. Subsampling the training data addresses the first issue at the cost of discarding potentially useful image information. Increasing feature dimensionality addresses the second but requires dense sampling. We propose a general and efficient solution to these problems. “Spatially Adaptive Random Forests” (SARF) is a supervised learning algorithm. SARF aims at automatic semantic labelling of large medical volumes. During training, it learns the optimal image sampling associated to the classification task. During testing, the algorithm quickly handles the background and focuses challenging image regions to refine the classification. SARF demonstrated top performance in the context of multi-class gliomas segmentation in multi-modal MR images.","PeriodicalId":178011,"journal":{"name":"2013 IEEE 10th International Symposium on Biomedical Imaging","volume":"31 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":"133414626","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: 54
Patch-driven neonatal brain MRI segmentation with sparse representation and level sets 基于稀疏表示和水平集的补丁驱动新生儿脑MRI分割
2013 IEEE 10th International Symposium on Biomedical Imaging Pub Date : 2013-04-07 DOI: 10.1109/ISBI.2013.6556668
Li Wang, F. Shi, Gang Li, Weili Lin, J. Gilmore, D. Shen
{"title":"Patch-driven neonatal brain MRI segmentation with sparse representation and level sets","authors":"Li Wang, F. Shi, Gang Li, Weili Lin, J. Gilmore, D. Shen","doi":"10.1109/ISBI.2013.6556668","DOIUrl":"https://doi.org/10.1109/ISBI.2013.6556668","url":null,"abstract":"Neonatal brain MR image segmentation is challenging due to the poor image quality. In this paper, we propose a novel patch-driven level sets method for segmentation of neonatal brain images by taking advantage of sparse representation techniques. Specifically, we first build a subject-specific atlas from a library of aligned, manually segmented images by using sparse representation in a patch-based fashion. Then, the spatial consistency in the subject-specific atlas is further enforced by considering the similarities of a patch with its neighboring patches. Finally, this subject-specific atlas is integrated into a coupled level set framework for surface-based neonatal brain segmentation. The proposed method has been extensively evaluated on 20 training subjects using leave-one-out cross validation, and on 132 additional testing subjects. Both quantitative and qualitative evaluation results demonstrate the validity of the proposed method.","PeriodicalId":178011,"journal":{"name":"2013 IEEE 10th International Symposium on Biomedical Imaging","volume":"19 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":"133439020","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: 7
A denoising method forwhole-body low-dose x-ray imageswith adaptable parameter control 一种自适应参数控制的全身低剂量x射线图像去噪方法
2013 IEEE 10th International Symposium on Biomedical Imaging Pub Date : 2013-04-07 DOI: 10.1109/ISBI.2013.6556705
P. Irrera, I. Bloch, M. Delplanque
{"title":"A denoising method forwhole-body low-dose x-ray imageswith adaptable parameter control","authors":"P. Irrera, I. Bloch, M. Delplanque","doi":"10.1109/ISBI.2013.6556705","DOIUrl":"https://doi.org/10.1109/ISBI.2013.6556705","url":null,"abstract":"A denoising method is proposed for full body X-ray images, acquired under low dose conditions. The suggested algorithm is based on a non local means filter adapted to the statistics of Poisson noise. A new feature of the method is to locally set the filtering parameters in order to denoise while preserving details in low absorption regions. Thus, we propose to adapt the denoising parameters for each pixel by exploiting a global noise level measure and the standard deviation image of the gradient magnitude. Quantitative and visual results on phantom and clinical images show the interest of the method, achieving the objectives.","PeriodicalId":178011,"journal":{"name":"2013 IEEE 10th International Symposium on Biomedical Imaging","volume":"55 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":"133637671","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: 5
Tight frame learning for cardiovascular MRI 心血管MRI的紧密框架学习
2013 IEEE 10th International Symposium on Biomedical Imaging Pub Date : 2013-04-07 DOI: 10.1109/ISBI.2013.6556469
Qiu Wang, Jun Liu, N. Janardhanan, M. Zenge, E. Mueller, M. Nadar
{"title":"Tight frame learning for cardiovascular MRI","authors":"Qiu Wang, Jun Liu, N. Janardhanan, M. Zenge, E. Mueller, M. Nadar","doi":"10.1109/ISBI.2013.6556469","DOIUrl":"https://doi.org/10.1109/ISBI.2013.6556469","url":null,"abstract":"Dynamic cardiovascular MRI facilitates the assessment of the structure and function of the cardiovascular system. One of the challenges in dynamic MRI is the prolonged data acquisition time. In order to fit the data acquisition time inside the motion cycles of the imaging subject, the data must be highly undersampled. Compressed sensing or sparsity based MR reconstruction takes advantage of the fact that the image is compressible in some transform domain, and enables reconstruction based on under-sampled k-space data thereby reducing the acquisition time. The design of such transform is key to the success of the reconstruction. In this paper, we propose to use tight frame learning for computing data-driven transforms. Empirical results demonstrate improvement over the transform associated with the redundant Haar Wavelets.","PeriodicalId":178011,"journal":{"name":"2013 IEEE 10th International Symposium on Biomedical Imaging","volume":"10 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":"132287747","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
Fast myocardial motion and strain estimation in 3D cardiac ultrasound with Sparse Demons 稀疏三维心脏超声快速心肌运动与应变估计
2013 IEEE 10th International Symposium on Biomedical Imaging Pub Date : 2013-04-07 DOI: 10.1109/ISBI.2013.6556691
O. Somphone, M. D. Craene, R. Ardon, B. Mory, P. Allain, Hang Gao, J. D’hooge, S. Marchesseau, Maxime Sermesant, H. Delingette, E. Saloux
{"title":"Fast myocardial motion and strain estimation in 3D cardiac ultrasound with Sparse Demons","authors":"O. Somphone, M. D. Craene, R. Ardon, B. Mory, P. Allain, Hang Gao, J. D’hooge, S. Marchesseau, Maxime Sermesant, H. Delingette, E. Saloux","doi":"10.1109/ISBI.2013.6556691","DOIUrl":"https://doi.org/10.1109/ISBI.2013.6556691","url":null,"abstract":"This article presents a new method for motion and strain estimation in 3D echocardiography, called Sparse Demons, along with quantitative and qualitative evaluations from a dataset of synthetic ultrasound sequences. Motion estimation is based on a fast demons-like algorithm focusing on myocardial tissue. Synthetic 3D ultrasound images were generated by combining a biomechanical model of the heart with a realistic ultrasound imaging model. Ischemic areas were defined in the mechanical model to investigate whether our algorithm can discriminate healthy from diseased segments.","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":"130324033","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: 27
Optimized linear combinations of channels for complex multiple-coil B1 field estimation with Bloch-Siegert B1 mapping in MRI 基于Bloch-Siegert B1成像的MRI复杂多线圈B1场估计通道的优化线性组合
2013 IEEE 10th International Symposium on Biomedical Imaging Pub Date : 2013-04-07 DOI: 10.1109/ISBI.2013.6556631
Feng Zhao, J. Fessler, S. Wright, J. Rispoli, D. Noll
{"title":"Optimized linear combinations of channels for complex multiple-coil B1 field estimation with Bloch-Siegert B1 mapping in MRI","authors":"Feng Zhao, J. Fessler, S. Wright, J. Rispoli, D. Noll","doi":"10.1109/ISBI.2013.6556631","DOIUrl":"https://doi.org/10.1109/ISBI.2013.6556631","url":null,"abstract":"Bloch-Siegert B1 mapping for multiple-channel parallel excitation systems usually produces noisy estimates in low intensity regions. Methods that use linear combinations of multiple coils have been proposed to mitigate this problem. However, little work has been done to optimize these coil combinations to improve the signal-to-noise ratio of B1 mapping in a robust way. In this paper, we propose a Cramer-Rao Lower Bound analysis based method to optimize the coil combination matrix by minimizing the variance of B1 map estimation for the previously proposed Bloch-Siegert B1 mapping method. We illustrate how optimizing the coil combinations yields improved B1 estimates in a simulation of brain imaging with a 3T MRI scan.","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":"130330747","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: 5
A method for z-shim compensated EPI-bold imaging in a single shot 一种z-垫片补偿EPI-bold单镜头成像方法
2013 IEEE 10th International Symposium on Biomedical Imaging Pub Date : 2013-04-07 DOI: 10.1109/ISBI.2013.6556481
W. Hoge, R. Kraft, E. Stern, H. Pan
{"title":"A method for z-shim compensated EPI-bold imaging in a single shot","authors":"W. Hoge, R. Kraft, E. Stern, H. Pan","doi":"10.1109/ISBI.2013.6556481","DOIUrl":"https://doi.org/10.1109/ISBI.2013.6556481","url":null,"abstract":"Functional MR Imaging (fMRI) is a widely used mechanism to non-invasively evaluate and assess neural activity in the brain, with echo planar imaging (EPI) the most common sequence used in fMRI studies. EPI methods are attractive because they provide functional imaging data of the whole brain at reasonable temporal resolutions. However, EPI is notoriously susceptible to multiple artifacts. This includes Nyquist ghosting caused by mismatched data sampled on positive versus negative readout gradients, and signal loss caused by local magnetic field inhomogeneities. We have recently proposed novel methods to correct for both of these artifacts in EPI. In this paper, we report on the effectiveness of these methods to improve functional MRI studies of deep brain regions including the amygdala and the ventrial medial pre-frontal and orbital-frontal-cortex (vmPFC/vmOFC). Here we review our GESTE method for ghost correction and single-shot z-shim compensation for recovery of signal affected by magnetic susceptibility loss, and compare the results of an emotional stimulus paradigm task both with and with-out these improvements.","PeriodicalId":178011,"journal":{"name":"2013 IEEE 10th International Symposium on Biomedical Imaging","volume":"7 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":"122184395","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
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