2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)最新文献_第4页

Deep residual learning for compressed sensing MRI 压缩感知MRI的深度残差学习

2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) Pub Date : 2017-04-18 DOI: 10.1109/ISBI.2017.7950457

Dongwook Lee, J. Yoo, J. C. Ye

引用次数: 195

Automatic 3D ultrasound segmentation of the first trimester placenta using deep learning 利用深度学习技术对妊娠早期胎盘进行自动三维超声分割

2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) Pub Date : 2017-04-18 DOI: 10.1109/ISBI.2017.7950519

P. Looney, G. Stevenson, K. Nicolaides, W. Plasencia, Malid Molloholli, S. Natsis, S. Collins

{"title":"Automatic 3D ultrasound segmentation of the first trimester placenta using deep learning","authors":"P. Looney, G. Stevenson, K. Nicolaides, W. Plasencia, Malid Molloholli, S. Natsis, S. Collins","doi":"10.1109/ISBI.2017.7950519","DOIUrl":"https://doi.org/10.1109/ISBI.2017.7950519","url":null,"abstract":"Placental volume measured with 3D ultrasound in the first trimester has been shown to be correlated to adverse pregnancy outcomes. This could potentially be used as a screening test to predict the “at risk” pregnancy. However, manual segmentation whilst previously shown to be accurate and repeatable is very time consuming and semi-automated methods still require operator input. To generate a screening tool, fully automated placental segmentation is required. In this work, a deep convolutional neural network (cNN), DeepMedic, was trained using the output of the semi-automated Random Walker method as ground truth. 300 3D ultrasound scans of first trimester placentas were used to train, validate and test the cNN. Compared against the semi-automated segmentation, resultant median (1st Quartile, 3rd Quartile) Dice Similarity Coefficient was 0.73 (0.66, 0.76). The median (1st Quartile, 3rd Quartile) Hausdorff distance was 27 mm (18 mm, 36 mm). We present the first attempt at using a deep cNN for segmentation of 3D ultrasound of the placenta. This work shows that feasible results compared to ground truth were obtained that could form the basis of a fully automatic segmentation method.","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":"75005918","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: 48

Domain specific convolutional neural nets for detection of architectural distortion in mammograms 用于乳房x光片结构畸变检测的领域特定卷积神经网络

2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) Pub Date : 2017-04-18 DOI: 10.1109/ISBI.2017.7950581

Rami Ben-Ari, A. Akselrod-Ballin, Leonid Karlinsky, Sharbell Y. Hashoul

引用次数: 32

Osteoporosis prescreening using dental panoramic radiographs feature analysis 骨质疏松症的牙科全景x线片预筛特征分析

2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) Pub Date : 2017-04-18 DOI: 10.1109/ISBI.2017.7950498

Chunjuan Bo, Xin Liang, Peng Chu, Jonathan Xu, D. Wang, Jie Yang, V. Megalooikonomou, H. Ling

引用次数: 13

Coherent temporal extrapolation of labeled images 标记图像的连贯时间外推

2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) Pub Date : 2017-04-18 DOI: 10.1109/ISBI.2017.7950554

G. Malandain, G. Michelin

引用次数: 1

Compressed sensing for dose reduction in STEM tomography 压缩感知在STEM断层扫描中的剂量降低

2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) Pub Date : 2017-04-18 DOI: 10.1109/ISBI.2017.7950459

Laurène Donati, M. Nilchian, M. Unser, S. Trépout, C. Messaoudi, S. Marcoy

引用次数: 2

Shear wave elastography for the characterization of arterial wall stiffness: A thin-plate phantom and ex vivo aorta study 剪切波弹性成像表征动脉壁刚度:薄板模拟和离体主动脉研究

2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) Pub Date : 2017-04-18 DOI: 10.1109/ISBI.2017.7950518

E. J. Chang, Yuexin Guo, Wei-Ning Lee

{"title":"Shear wave elastography for the characterization of arterial wall stiffness: A thin-plate phantom and ex vivo aorta study","authors":"E. J. Chang, Yuexin Guo, Wei-Ning Lee","doi":"10.1109/ISBI.2017.7950518","DOIUrl":"https://doi.org/10.1109/ISBI.2017.7950518","url":null,"abstract":"Ultrasound shear wave elastography (SWE) is an emerging technique for characterizing local arterial stiffness - a known indicator for vascular health. However, the implications due to vascular anatomy and tissue environment are still relatively under-examined. Using polyvinyl alcohol (PVA) based tissue mimicking phantoms, this study assessed the current signal processing framework in demonstrating the challenges due to the wave dispersion (at the medium thicknesses smaller than the shear wavelength) and wave interference at the interface of different media which cause biased stiffness estimations. Hence, 5% PVA and 10% PVA phantoms of varying thicknesses (from 1 to 10 mm) were imaged when placed in water and in 5% PVA and 10% PVA phantoms. Our results confirmed that shear wave propagation was thickness dependent (315% underestimation in 10% PVA). The shear wave velocity was shown to be influenced by the surrounding media with a 150% overestimation in 5% PVA surrounded by 10% PVA. It also demonstrated a key limitation of arterial SWE in that the current phase velocity estimation does not provide accurate SWV estimation, requiring optimization for addressing wave interference.","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":"88505491","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

Biopsy-guided learning with deep convolutional neural networks for Prostate Cancer detection on multiparametric MRI 基于深度卷积神经网络的多参数MRI前列腺癌活检引导学习

2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) Pub Date : 2017-04-18 DOI: 10.1109/ISBI.2017.7950602

Yohannes K. Tsehay, Nathan S. Lay, Xiaosong Wang, J. T. Kwak, B. Turkbey, P. Choyke, P. Pinto, B. Wood, R. Summers

引用次数: 37

Removal of the twin image artifact in holographic lens-free imaging by sparse dictionary learning and coding 基于稀疏字典学习和编码的全息无透镜成像中孪生图像伪影的去除

2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) Pub Date : 2017-04-18 DOI: 10.1109/ISBI.2017.7950625

B. Haeffele, Sophie Roth, Lin Zhou, R. Vidal

{"title":"Removal of the twin image artifact in holographic lens-free imaging by sparse dictionary learning and coding","authors":"B. Haeffele, Sophie Roth, Lin Zhou, R. Vidal","doi":"10.1109/ISBI.2017.7950625","DOIUrl":"https://doi.org/10.1109/ISBI.2017.7950625","url":null,"abstract":"Mitigating the effects of the twin image artifact is one of the key challenges in holographic lens-free microscopy. This artifact arises due to the fact that imaging detectors can only record the magnitude of the hologram wavefront but not the phase. Prior work addresses this problem by attempting to simultaneously estimate the missing phase and reconstruct an image of the object specimen. Here we explore a fundamentally different approach based on post-processing the reconstructed image using sparse dictionary learning and coding techniques originally developed for processing conventional images. First, a dictionary of atoms representing characteristics from either the true image of the specimen or the twin image are learned from a collection of patches of the observed images. Then, by expressing each patch of the observed image as a sparse linear combination of the dictionary atoms, the observed image is decomposed into a component that corresponds to the true image and another one that corresponds to the twin image artifact. Experiments on counting red blood cells demonstrate the effectiveness of the proposed approach.","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":"87561574","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

Learning size adaptive local maxima selection for robust nuclei detection in histopathology images 组织病理图像鲁棒核检测的学习大小自适应局部最大值选择

2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) Pub Date : 2017-04-18 DOI: 10.1109/ISBI.2017.7950670

N. Brieu, G. Schmidt

引用次数: 12