Proceedings. International Conference on Image Processing最新文献_第2页

QUANTIFYING ACTIN FILAMENTS IN MICROSCOPIC IMAGES USING KEYPOINT DETECTION TECHNIQUES AND A FAST MARCHING ALGORITHM. 使用关键点检测技术和快速行进算法量化显微图像中的肌动蛋白丝。

Proceedings. International Conference on Image Processing Pub Date : 2020-10-01 Epub Date: 2020-09-30 DOI: 10.1109/ICIP40778.2020.9191337

Yi Liu, Alexander Nedo, Kody Seward, Jeffrey Caplan, Chandra Kambhamettu

{"title":"QUANTIFYING ACTIN FILAMENTS IN MICROSCOPIC IMAGES USING KEYPOINT DETECTION TECHNIQUES AND A FAST MARCHING ALGORITHM.","authors":"Yi Liu, Alexander Nedo, Kody Seward, Jeffrey Caplan, Chandra Kambhamettu","doi":"10.1109/ICIP40778.2020.9191337","DOIUrl":"10.1109/ICIP40778.2020.9191337","url":null,"abstract":"The actin filament plays a fundamental role in numerous cellular processes such as cell growth, proliferation, migration, division, and locomotion. The actin cytoskeleton is highly dynamical and can polymerize and depolymerize in a very short time under different stimuli. To study the mechanics of actin filament, quantifying the length and number of actin filaments in each time frame of microscopic images is fundamental. In this paper, we adopt a Convolutional Neural Network (CNN) to segment actin filaments first, and then we utilize a modified Resnet to detect junctions and endpoints of filaments. With binary segmentation and detected keypoints, we apply a fast marching algorithm to obtain the number and length of each actin filament in microscopic images. We have also collected a dataset of 10 microscopic images of actin filaments to test our method. Our experiments show that our approach outperforms other existing approaches tackling this problem regarding both accuracy and inference time.","PeriodicalId":74572,"journal":{"name":"Proceedings. International Conference on Image Processing","volume":"2020 ","pages":"2506-2510"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7983297/pdf/nihms-1673276.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25510030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A REAL-TIME MEDICAL ULTRASOUND SIMULATOR BASED ON A GENERATIVE ADVERSARIAL NETWORK MODEL. 基于生成对抗网络模型的实时医学超声模拟器。

Proceedings. International Conference on Image Processing Pub Date : 2019-09-01 Epub Date: 2019-08-26 DOI: 10.1109/icip.2019.8803570

Bo Peng, Xing Huang, Shiyuan Wang, Jingfeng Jiang

{"title":"A REAL-TIME MEDICAL ULTRASOUND SIMULATOR BASED ON A GENERATIVE ADVERSARIAL NETWORK MODEL.","authors":"Bo Peng, Xing Huang, Shiyuan Wang, Jingfeng Jiang","doi":"10.1109/icip.2019.8803570","DOIUrl":"https://doi.org/10.1109/icip.2019.8803570","url":null,"abstract":"This paper presents an artificial intelligence-based ultrasound simulator suitable for medical simulation and clinical training. Particularly, we propose a machine learning approach to realistically simulate ultrasound images based on generative adversarial networks (GANs). Using B-mode ultrasound images simulated by a known ultrasound simulator, Field II, an \"image-to-image\" ultrasound simulator was trained. Then, through evaluations, we found that the GAN-based simulator can generate B-mode images following Rayleigh scattering. Our preliminary study demonstrated that ultrasound B-mode images from anatomies inferred from magnetic resonance imaging (MRI) data were feasible. While some image blurring was observed, ultrasound B- mode images obtained were both visually and quantitatively comparable to those obtained using the Field II simulator. It is also important to note that the GAN-based ultrasound simulator was computationally efficient and could achieve a frame rate of 15 frames/second using a regular laptop computer. In the future, the proposed GAN-based simulator will be used to synthesize more realistic looking ultrasound images with artifacts such as shadowing.","PeriodicalId":74572,"journal":{"name":"Proceedings. International Conference on Image Processing","volume":"2019 ","pages":"4629-4633"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/icip.2019.8803570","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25541753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 12

EARLY ASSESSMENT OF RENAL TRANSPLANTS USING BOLD-MRI: PROMISING RESULTS. 使用bold-mri对肾移植的早期评估:有希望的结果。

Proceedings. International Conference on Image Processing Pub Date : 2019-09-01 Epub Date: 2019-08-26 DOI: 10.1109/ICIP.2019.8803042

M Shehata, A Shalaby, M Ghazal, M Abou El-Ghar, M A Badawy, G Beache, A Dwyer, M El-Melegy, G Giridharan, R Keynton, A El-Baz

{"title":"EARLY ASSESSMENT OF RENAL TRANSPLANTS USING BOLD-MRI: PROMISING RESULTS.","authors":"M Shehata, A Shalaby, M Ghazal, M Abou El-Ghar, M A Badawy, G Beache, A Dwyer, M El-Melegy, G Giridharan, R Keynton, A El-Baz","doi":"10.1109/ICIP.2019.8803042","DOIUrl":"https://doi.org/10.1109/ICIP.2019.8803042","url":null,"abstract":"Non-invasive evaluation of renal transplant function is essential to minimize and manage renal rejection. A computer-assisted diagnostic (CAD) system was developed to evaluate kidney function post-transplantation. The developed CAD system utilizes the amount of blood-oxygenation extracted from 3D (2D + time) blood oxygen level-dependent magnetic resonance imaging (BOLD-MRI) to estimate renal function. BOLD-MRI scans were acquired at five different echo-times (2, 7, 12, 17, and 22) ms from 15 transplant patients. The developed CAD system first segments kidneys using the level-sets method followed by estimation of the amount of deoxyhemoglobin, also known as apparent relaxation rate (R2*). These R2* estimates were used as discriminatory features (global features (mean R2*) and local features (pixel-wise R2*)) to train and test state-of-the-art machine learning classifiers to differentiate between non-rejection (NR) and acute renal rejection. Using a leave-one-out cross-validation approach along with an artificial neural network (ANN) classifier, the CAD system demonstrated 93.3% accuracy, 100% sensitivity, and 90% specificity in distinguishing AR from non-rejection . These preliminary results demonstrate the efficacy of the CAD system to detect renal allograft status non-invasively.","PeriodicalId":74572,"journal":{"name":"Proceedings. International Conference on Image Processing","volume":"2019 ","pages":"1395-1399"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/ICIP.2019.8803042","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39555134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

DEEP MR IMAGE SUPER-RESOLUTION USING STRUCTURAL PRIORS. 使用结构先验的深度磁共振图像超分辨率。

Proceedings. International Conference on Image Processing Pub Date : 2018-10-01 Epub Date: 2018-09-06 DOI: 10.1109/ICIP.2018.8451496

Venkateswararao Cherukuri, Tiantong Guo, Steven J Schiff, Vishal Monga

{"title":"DEEP MR IMAGE SUPER-RESOLUTION USING STRUCTURAL PRIORS.","authors":"Venkateswararao Cherukuri, Tiantong Guo, Steven J Schiff, Vishal Monga","doi":"10.1109/ICIP.2018.8451496","DOIUrl":"https://doi.org/10.1109/ICIP.2018.8451496","url":null,"abstract":"High resolution magnetic resonance (MR) images are desired for accurate diagnostics. In practice, image resolution is restricted by factors like hardware, cost and processing constraints. Recently, deep learning methods have been shown to produce compelling state of the art results for image superresolution. Paying particular attention to desired hi-resolution MR image structure, we propose a new regularized network that exploits image priors, namely a low-rank structure and a sharpness prior to enhance deep MR image superresolution. Our contributions are then incorporating these priors in an analytically tractable fashion in the learning of a convolutional neural network (CNN) that accomplishes the super-resolution task. This is particularly challenging for the low rank prior, since the rank is not a differentiable function of the image matrix (and hence the network parameters), an issue we address by pursuing differentiable approximations of the rank. Sharpness is emphasized by the variance of the Laplacian which we show can be implemented by a fixed feedback layer at the output of the network. Experiments performed on two publicly available MR brain image databases exhibit promising results particularly when training imagery is limited.","PeriodicalId":74572,"journal":{"name":"Proceedings. International Conference on Image Processing","volume":"2018 ","pages":"410-414"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/ICIP.2018.8451496","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37106530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

DEEP LEARNING BASED SUPERVISED SEMANTIC SEGMENTATION OF ELECTRON CRYO-SUBTOMOGRAMS. 基于深度学习的电子冷冻子图监督语义分割。

Proceedings. International Conference on Image Processing Pub Date : 2018-10-01 Epub Date: 2018-09-06 DOI: 10.1109/icip.2018.8451386

Chang Liu, Xiangrui Zeng, Ruogu Lin, Xiaodan Liang, Zachary Freyberg, Eric Xing, Min Xu

{"title":"DEEP LEARNING BASED SUPERVISED SEMANTIC SEGMENTATION OF ELECTRON CRYO-SUBTOMOGRAMS.","authors":"Chang Liu, Xiangrui Zeng, Ruogu Lin, Xiaodan Liang, Zachary Freyberg, Eric Xing, Min Xu","doi":"10.1109/icip.2018.8451386","DOIUrl":"10.1109/icip.2018.8451386","url":null,"abstract":"Cellular Electron Cryo-Tomography (CECT) is a powerful imaging technique for the 3D visualization of cellular structure and organization at submolecular resolution. It enables analyzing the native structures of macromolecular complexes and their spatial organization inside single cells. However, due to the high degree of structural complexity and practical imaging limitations, systematic macromolecular structural recovery inside CECT images remains challenging. Particularly, the recovery of a macromolecule is likely to be biased by its neighbor structures due to the high molecular crowding. To reduce the bias, here we introduce a novel 3D convolutional neural network inspired by Fully Convolutional Network and Encoder-Decoder Architecture for the supervised segmentation of macromolecules of interest in subtomograms. The tests of our models on realistically simulated CECT data demonstrate that our new approach has significantly improved segmentation performance compared to our baseline approach. Also, we demonstrate that the proposed model has generalization ability to segment new structures that do not exist in training data.","PeriodicalId":74572,"journal":{"name":"Proceedings. International Conference on Image Processing","volume":"2018 ","pages":"1578-1582"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/icip.2018.8451386","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41143083","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 14

WEAKLY SUPERVISED FOOD IMAGE SEGMENTATION USING CLASS ACTIVATION MAPS. 使用类激活映射的弱监督食物图像分割。

Proceedings. International Conference on Image Processing Pub Date : 2017-09-01 Epub Date: 2018-02-22 DOI: 10.1109/ICIP.2017.8296487

Yu Wang, Fengqing Zhu, Carol J Boushey, Edward J Delp

引用次数: 0

STRUCTURED LOW-RANK RECOVERY OF PIECEWISE CONSTANT SIGNALS WITH PERFORMANCE GUARANTEES. 具有性能保证的片常数信号结构化低阶恢复。

Proceedings. International Conference on Image Processing Pub Date : 2016-09-01 Epub Date: 2016-08-19 DOI: 10.1109/icip.2016.7532500

Greg Ongie, Sampurna Biswas, Mathews Jacob

引用次数: 0

DISJUNCTIVE NORMAL LEVEL SET: AN EFFICIENT PARAMETRIC IMPLICIT METHOD. 析取正态水平集:一种有效的参数隐式方法。

Proceedings. International Conference on Image Processing Pub Date : 2016-09-01 Epub Date: 2016-08-19 DOI: 10.1109/ICIP.2016.7533171

Fitsum Mesadi, Mujdat Cetin, Tolga Tasdizen

引用次数: 5

EFFICIENT SUPERPIXEL BASED SEGMENTATION FOR FOOD IMAGE ANALYSIS. 基于超像素的高效食品图像分割分析。

Proceedings. International Conference on Image Processing Pub Date : 2016-09-01 Epub Date: 2016-12-08 DOI: 10.1109/ICIP.2016.7532818

Yu Wang, Chang Liu, Fengqing Zhu, Carol J Boushey, Edward J Delp

引用次数: 15

A COMPARISON OF FOOD PORTION SIZE ESTIMATION USING GEOMETRIC MODELS AND DEPTH IMAGES. 使用几何模型和深度图像的食物分量大小估计的比较。

Proceedings. International Conference on Image Processing Pub Date : 2016-09-01 Epub Date: 2016-12-08 DOI: 10.1109/ICIP.2016.7532312

Shaobo Fang, Fengqing Zhu, Chufan Jiang, Song Zhang, Carol J Boushey, Edward J Delp

引用次数: 32