2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)最新文献

Correction of partial volume effect in 99mTc-TRODAT-1 brain SPECT images using an edge-preserving weighted regularization 用保边加权正则化校正99mTc-TRODAT-1脑SPECT图像的部分体积效应

2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) Pub Date : 2017-06-15 DOI: 10.1109/ISBI.2017.7950702

T. Yin, N. Chiu

引用次数: 0

Registration of ultra-high resolution 3D PLI data of human brain sections to their corresponding high-resolution counterpart 将人脑切片的超高分辨率3D PLI数据与相应的高分辨率数据进行配准

2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) Pub Date : 2017-06-15 DOI: 10.1109/ISBI.2017.7950550

Sharib Ali, K. Rohr, M. Axer, K. Amunts, R. Eils, S. Wörz

{"title":"Registration of ultra-high resolution 3D PLI data of human brain sections to their corresponding high-resolution counterpart","authors":"Sharib Ali, K. Rohr, M. Axer, K. Amunts, R. Eils, S. Wörz","doi":"10.1109/ISBI.2017.7950550","DOIUrl":"https://doi.org/10.1109/ISBI.2017.7950550","url":null,"abstract":"The structural analysis of nerve fibers of the human brain is an important topic in current neuroscience. To obtain information about neural connections with micrometer resolution, polarized light imaging (3D PLI) of histological brain sections is well suited. In our application, both high-resolution (HR, 64µm in-plane pixel size) and ultra-high resolution (ultra-HR, 1.3µm) 3D PLI data of human brain sections are acquired. However, due to arbitrary translations and rotations caused by the sectioning and mounting process, spatial coherence between sections is lost and image registration is necessary. We introduce a new feature-based approach for registration of ultra-HR 3D PLI data to their corresponding HR images. The approach is based on a novel multi-scale salient feature detection method that is well suited for 3D PLI data. We have successfully evaluated the approach and applied it to 83 sections of a human brain. An experimental comparison with previous state-of-the-art feature detectors demonstrates the superior performance of our approach.","PeriodicalId":6547,"journal":{"name":"2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89151056","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

Classification of adrenal lesions through spatial Bayesian modeling of GLCM 基于GLCM空间贝叶斯模型的肾上腺病变分类

2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) Pub Date : 2017-06-15 DOI: 10.1109/ISBI.2017.7950489

X. Li, M. Guindani, C. Ng, B. Hobbs

{"title":"Classification of adrenal lesions through spatial Bayesian modeling of GLCM","authors":"X. Li, M. Guindani, C. Ng, B. Hobbs","doi":"10.1109/ISBI.2017.7950489","DOIUrl":"https://doi.org/10.1109/ISBI.2017.7950489","url":null,"abstract":"Radiomics, an emerging field of quantitative imaging, encompasses a broad class of analytical techniques. Recent literature have interrogated associations between quantitatively derived GLCM-based texture features and clinical/pathology information using machine learning algorithms in many cancer settings, but often fail to elucidate the predictive power of these features. Moreover, for many cancers characterized by complex histopathological profiles, such as adrenocortical carcinoma, reducing the multivariate functional structure of GLCM to a set of summary statistics is potentially reductive, masking the patterns that distinguish malignancy from benignity. We develop a Bayesian probabilistic framework for predictive classification of lesion types, based on the entire GLCM. Our method, which uses a spatial Gaussian random field to model dependencies among neighboring cells of the GLCMs, was applied in a cancer detection context to discriminant malignant from benign adrenal lesions using GLCMs arising from non-contrast CT scans. Our method is shown to yield improved predictive power both in simulations as well as the adrenal CT application when compared to state-of-the-art diagnostic algorithms that use GLCM derived features.","PeriodicalId":6547,"journal":{"name":"2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76261515","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

Two-dimensional speckle tracking using parabolic polynomial expansion with Riesz transform 利用抛物多项式展开和Riesz变换进行二维散斑跟踪

2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) Pub Date : 2017-06-15 DOI: 10.1109/ISBI.2017.7950501

M. Almekkawy, E. Ebbini

{"title":"Two-dimensional speckle tracking using parabolic polynomial expansion with Riesz transform","authors":"M. Almekkawy, E. Ebbini","doi":"10.1109/ISBI.2017.7950501","DOIUrl":"https://doi.org/10.1109/ISBI.2017.7950501","url":null,"abstract":"Ultrasound speckle tracking provides a robust motion estimation of fine tissue displacements along the beam direction. Extensions to 2-D have been proposed in recent years. Due to relatively coarse lateral sampling, several solutions relied on lateral interpolation in order to achieve subsample accuracy. We introduce a new multi-dimensional speckle tracking method (MDST) with subsample accuracy in all dimensions. The proposed algorithm is based on solving a least squares problem to estimate the coefficients of a second order polynomial expansion to fit the magnitude of the two dimensional complex normalized correlation of the generalized analytic signal in the vicinity of the true peak. The generalization method utilizes the Riesz transform which is the multidimensional Hilbert transform. The displacement is estimated from acquired successive radio-frequency data frames of the region of interest. Field II simulation of flow data in a channel with a bench mark known displacement is generated to validate the accuracy of the method. In addition, the new MDST method is applied to imaging data from a flow phantom (ATS Model 524) to estimate the flow motion and pulsating channel wall. Simulations and experimental results demonstrate the effectiveness of the proposed technique.","PeriodicalId":6547,"journal":{"name":"2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85714227","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: 10

Elastic registration of high-resolution 3D PLI data of the human brain 人脑高分辨率三维PLI数据的弹性配准

2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) Pub Date : 2017-06-15 DOI: 10.1109/ISBI.2017.7950720

Sharib Ali, K. Rohr, M. Axer, D. Gräßel, Philipp Schlömer, K. Amunts, R. Eils, S. Wörz

引用次数: 6

Fast reconstruction of image deformation field using radial basis function 基于径向基函数的图像变形场快速重建

2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) Pub Date : 2017-04-19 DOI: 10.1109/ISBI.2017.7950719

Lukás Rucka, I. Peterlík

引用次数: 5

Lung nodule detection in CT using 3D convolutional neural networks 三维卷积神经网络在CT肺结节检测中的应用

2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) Pub Date : 2017-04-18 DOI: 10.1109/ISBI.2017.7950542

Xiaojie Huang, Junjie Shan, V. Vaidya

{"title":"Lung nodule detection in CT using 3D convolutional neural networks","authors":"Xiaojie Huang, Junjie Shan, V. Vaidya","doi":"10.1109/ISBI.2017.7950542","DOIUrl":"https://doi.org/10.1109/ISBI.2017.7950542","url":null,"abstract":"We propose a new computer-aided detection system that uses 3D convolutional neural networks (CNN) for detecting lung nodules in low dose computed tomography. The system leverages both a priori knowledge about lung nodules and confounding anatomical structures and data-driven machine-learned features and classifier. Specifically, we generate nodule candidates using a local geometric-model-based filter and further reduce the structure variability by estimating the local orientation. The nodule candidates in the form of 3D cubes are fed into a deep 3D convolutional neural network that is trained to differentiate nodule and non-nodule inputs. We use data augmentation techniques to generate a large number of training examples and apply regularization to avoid overfitting. On a set of 99 CT scans, the proposed system achieved state-of-the-art performance and significantly outperformed a similar hybrid system that uses conventional shallow learning. The experimental results showed benefits of using a priori models to reduce the problem space for data-driven machine learning of complex deep neural networks. The results also showed the advantages of 3D CNN over 2D CNN in volumetric medical image analysis.","PeriodicalId":6547,"journal":{"name":"2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73200279","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: 163

Reducing data acquisition for fast Structured Illumination Microscopy using Compressed Sensing 使用压缩感知减少快速结构照明显微镜的数据采集

2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) Pub Date : 2017-04-18 DOI: 10.1109/ISBI.2017.7950461

William Meiniel, P. Spinicelli, E. Angelini, A. Fragola, V. Loriette, F. Orieux, E. Sepúlveda, J. Olivo-Marin

引用次数: 10

Automatic Carotid ultrasound segmentation using deep Convolutional Neural Networks and phase congruency maps 使用深度卷积神经网络和相位一致性图的颈动脉超声自动分割

2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) Pub Date : 2017-04-18 DOI: 10.1109/ISBI.2017.7950598

Carl Azzopardi, Y. Hicks, K. Camilleri

{"title":"Automatic Carotid ultrasound segmentation using deep Convolutional Neural Networks and phase congruency maps","authors":"Carl Azzopardi, Y. Hicks, K. Camilleri","doi":"10.1109/ISBI.2017.7950598","DOIUrl":"https://doi.org/10.1109/ISBI.2017.7950598","url":null,"abstract":"The segmentation of media-adventitia and lumen-intima boundaries of the Carotid Artery forms an essential part in assessing plaque morphology in Ultrasound Imaging. Manual methods are tedious and prone to variability and thus, developing automated segmentation algorithms is preferable. In this paper, we propose to use deep convolutional networks for automated segmentation of the media-adventitia boundary in transverse and longitudinal sections of carotid ultrasound images. Deep networks have recently been employed with good success on image segmentation tasks, and we thus propose their application on ultrasound data, using an encoder-decoder convolutional structure which allows the network to be trained end-to-end for pixel-wise classification. Concurrently, we evaluate the performance for various configurations, depths and filter sizes within the network. In addition, we further propose a novel fusion of envelope and phase congruency data as an input to the network, as the latter provides an intensity-invariant data source to the network. We show that this data fusion and the proposed network structure yields higher segmentation performance than the state-of-the-art techniques.","PeriodicalId":6547,"journal":{"name":"2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89202546","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: 24

Recurrent neural network based retinal nerve fiber layer defect detection in early glaucoma 基于递归神经网络的早期青光眼视网膜神经纤维层缺损检测

2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) Pub Date : 2017-04-18 DOI: 10.1109/ISBI.2017.7950614

Rashmi Panda, N. Puhan, A. Rao, Debananda Padhy, G. Panda

引用次数: 14