2021 IEEE International Conference on Imaging Systems and Techniques (IST)最新文献_第2页

Left ventricular segmentation from cardiac MRI data 心脏MRI数据的左心室分割

2021 IEEE International Conference on Imaging Systems and Techniques (IST) Pub Date : 2021-08-24 DOI: 10.1109/ist50367.2021.9651383

Michał Motak, A. Skalski

{"title":"Left ventricular segmentation from cardiac MRI data","authors":"Michał Motak, A. Skalski","doi":"10.1109/ist50367.2021.9651383","DOIUrl":"https://doi.org/10.1109/ist50367.2021.9651383","url":null,"abstract":"Analysis of cardiac magnetic resonance imaging data is essential in diagnosis of many cardiovascular diseases. To assist doctors in manual interpretation, numerous automatic methods are being developed. Recent segmentation algorithms based on deep learning perform the best on homogeneous data, but poorly on the data from different sources. For this reason, modern methods struggle in real world application. Improvement of the effectiveness on the unknown data can be done via several techniques like transfer learning or domain adaptation. In this paper, we proposed a segmentation framework with strong generalisability skills. The pipeline includes domain adaptation with CycleGAN architecture and segmentation of the left ventricular using U-Net 2D convolutional neural network. The method was evaluated on the data set from M& Ms challenge which is formed from data acquisited by several MRI scanners, in different medical centers. Presented method obtained a weighted dice coefficient score of $90.15pm 6.40$% and weighted Hausdorff Distance of $11.74pm 13.11 mm$. The results show that applied methods improved generalization of the model and decreased gap between metrics scores for different vendors. Presented method can be classified among other participants of the challenge.","PeriodicalId":433402,"journal":{"name":"2021 IEEE International Conference on Imaging Systems and Techniques (IST)","volume":"124 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126868712","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: 0

Fuzzy Adaptive Focal Loss for Imbalanced Datasets 不平衡数据集的模糊自适应焦点损失

2021 IEEE International Conference on Imaging Systems and Techniques (IST) Pub Date : 2021-08-24 DOI: 10.1109/ist50367.2021.9651474

T. Hong, Wei Peng, Ja-Hwung Su, Shyue-Liang Wang

引用次数: 0

A Novel Effective Framework for Medical Images Secure Storage Using Advanced Cipher Text Algorithm in Cloud Computing 基于云计算先进密文算法的医学图像安全存储新框架

2021 IEEE International Conference on Imaging Systems and Techniques (IST) Pub Date : 2021-08-24 DOI: 10.1109/ist50367.2021.9651475

S. Praveen, S. Sindhura, A. Madhuri, Dimitrios Alexios Karras

引用次数: 9

Deep-Learning based Scatter Correction in Digital Radiography 基于深度学习的数字放射成像散射校正

2021 IEEE International Conference on Imaging Systems and Techniques (IST) Pub Date : 2021-08-24 DOI: 10.1109/ist50367.2021.9651422

N. Sakaltras, Carlos F. Del Cerro, M. Desco, M. Abella

引用次数: 0

Comparison of ASM and CNN based prostate segmentation in CT images 基于ASM和CNN的前列腺分割在CT图像中的比较

2021 IEEE International Conference on Imaging Systems and Techniques (IST) Pub Date : 2021-08-24 DOI: 10.1109/ist50367.2021.9651376

Artur Kos, J. Bulat

{"title":"Comparison of ASM and CNN based prostate segmentation in CT images","authors":"Artur Kos, J. Bulat","doi":"10.1109/ist50367.2021.9651376","DOIUrl":"https://doi.org/10.1109/ist50367.2021.9651376","url":null,"abstract":"Accurate localisation of the prostate boundaries in the CT data is a necessary step in radiation therapy treatment planning. Manual outlining is a challenging real-world problem, thus an automated methods are needed to relieve the medical doctors. This, however, is not a trivial task due to unclear boarder between the neighbouring organs and variation in shape, size and location of the prostate itself. Recent advances in deep learning show applications in semantic image segmentation having performances superior to the traditional, non learning based methods. In this paper, we compare performance of the two approaches: one based on the Active Shape Models and the second exploiting the convolutional neural networks (CNN). Both compared methods were trained using CT volumes belonging to the same set of the real patients data annotated by clinical experts. CNN turned out to offer better performances in terms of accuracy and robustness. Segmentation accuracy reached by both methods was measured using Dice Similarity Coefficient (DSC) and Jacard Coefficient (JC) and was equal in average 0,720 DSC, 0.598 JC, and 0.796 DSC, 0.663 JC for ASM and CNN respectively.","PeriodicalId":433402,"journal":{"name":"2021 IEEE International Conference on Imaging Systems and Techniques (IST)","volume":"499 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116547563","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: 0

Building an Annotated Damage Image Database to Support AI-Assisted Hurricane Impact Analysis 建立一个带注释的损害图像数据库，以支持人工智能辅助的飓风影响分析

2021 IEEE International Conference on Imaging Systems and Techniques (IST) Pub Date : 2021-08-24 DOI: 10.1109/ist50367.2021.9651432

Hao Nan Ou, Sun Ho Ro, Jie Gong, Zhigang Zhu

{"title":"Building an Annotated Damage Image Database to Support AI-Assisted Hurricane Impact Analysis","authors":"Hao Nan Ou, Sun Ho Ro, Jie Gong, Zhigang Zhu","doi":"10.1109/ist50367.2021.9651432","DOIUrl":"https://doi.org/10.1109/ist50367.2021.9651432","url":null,"abstract":"Building an annotated damage image database is the first step to support AI-assisted hurricane impact analysis. Up to now, annotated datasets for model training are insufficient at a local level despite abundant raw data that have been collected for decades. This paper provides a systematic approach for establishing an annotated hurricane-damaged building image database to support AI-assisted damage assessment and analysis. Optimal rectilinear images were generated from panoramic images collected from Hurricane Harvey, Texas 2017. Then, deep learning models, including Amazon Web Service (AWS) Rekognition and Mask R-CNN (Region Based Convolutional Neural Networks), were retrained on the data to develop a pipeline for building detection and structural component extraction. A web-based dashboard was developed for building data management and processed image visualization along with detected structural components and their damage ratings. The proposed AI-assisted labeling tool and trained models can intelligently and rapidly assist potential users such as hazard researchers, practitioners, and government agencies on natural disaster damage management.","PeriodicalId":433402,"journal":{"name":"2021 IEEE International Conference on Imaging Systems and Techniques (IST)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123969160","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

GeoPixAI: From Pixels to Intelligent, Unbiased and Automated Fast Track Subsurface Characterization GeoPixAI:从像素到智能，无偏和自动化快速跟踪地下表征

2021 IEEE International Conference on Imaging Systems and Techniques (IST) Pub Date : 2021-08-24 DOI: 10.1109/ist50367.2021.9651426

Shariar Sattarin, T. Muther, A. K. Dahaghi, S. Negahban, Bryan Bell

{"title":"GeoPixAI: From Pixels to Intelligent, Unbiased and Automated Fast Track Subsurface Characterization","authors":"Shariar Sattarin, T. Muther, A. K. Dahaghi, S. Negahban, Bryan Bell","doi":"10.1109/ist50367.2021.9651426","DOIUrl":"https://doi.org/10.1109/ist50367.2021.9651426","url":null,"abstract":"The core analysis is one of the current inefficacies in rock characterization and petrophysical evaluation practices directly impacting well-completion decisions. The second problem is the lack of an intelligent multi-scale image processor essential for integrating the data into digital 4.0 platforms, which has a significant impact on G&G teams’ decisions for better well completion and reservoir evaluation. In this paper, we discuss the example of slab core analysis using our proposed GeoPixAI workflow. In this application, two different Convolution Neural Networks (CNNs) are designed to measure oil saturation, lithology, and fracture distributions. The results are then compared to the lab measure values. Our techniques show great accuracy while producing results at a fraction of the time it takes to do lab analysis. Also, the results are found to be consistent with lab measurements. While this study focuses on slab core analysis, GeoPixAI is a project focus on Digital Subsurface Processing for intelligent segmentation and information extraction from “raw” images at any scale. GeoPixAI can analyze and extract information from core Micro-CT scans, seismic data, FMI logs, and other image data.","PeriodicalId":433402,"journal":{"name":"2021 IEEE International Conference on Imaging Systems and Techniques (IST)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122648040","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

Human Tracking in Top-view Fisheye Images with Color Histograms via Deep Learning Detection 基于深度学习检测的彩色直方图俯视图鱼眼图像人体跟踪

2021 IEEE International Conference on Imaging Systems and Techniques (IST) Pub Date : 2021-08-24 DOI: 10.1109/ist50367.2021.9651451

Olfa Haggui, Marina Vert, Kieran McNamara, Bastien Brieussel, Baptiste Magnier

引用次数: 2

[IST 2021 Front cover] [IST 2021封面]

2021 IEEE International Conference on Imaging Systems and Techniques (IST) Pub Date : 2021-08-24 DOI: 10.1109/ist50367.2021.9651351

引用次数: 0

Web acquired image datasets need curation: an examplar pipeline evaluated on Greek food images 网络获取的图像数据集需要管理:对希腊食物图像进行评估的示例管道

2021 IEEE International Conference on Imaging Systems and Techniques (IST) Pub Date : 2021-08-24 DOI: 10.1109/ist50367.2021.9651419

V. Sevetlidis, G. Pavlidis, Vasileios Arampatzakis, C. Kiourt, S. Mouroutsos

引用次数: 0