Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention最新文献

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Robust Cell Detection and Segmentation in Histopathological Images Using Sparse Reconstruction and Stacked Denoising Autoencoders. 使用稀疏重构和堆叠去噪自动编码器在组织病理学图像中进行稳健的细胞检测和分割

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Pub Date : 2015-10-01 Epub Date: 2015-11-18 DOI: 10.1007/978-3-319-24574-4_46

Hai Su, Fuyong Xing, Xiangfei Kong, Yuanpu Xie, Shaoting Zhang, Lin Yang

{"title":"Robust Cell Detection and Segmentation in Histopathological Images Using Sparse Reconstruction and Stacked Denoising Autoencoders.","authors":"Hai Su, Fuyong Xing, Xiangfei Kong, Yuanpu Xie, Shaoting Zhang, Lin Yang","doi":"10.1007/978-3-319-24574-4_46","DOIUrl":"https://doi.org/10.1007/978-3-319-24574-4_46","url":null,"abstract":"Computer-aided diagnosis (CAD) is a promising tool for accurate and consistent diagnosis and prognosis. Cell detection and segmentation are essential steps for CAD. These tasks are challenging due to variations in cell shapes, touching cells, and cluttered background. In this paper, we present a cell detection and segmentation algorithm using the sparse reconstruction with trivial templates and a stacked denoising autoencoder (sDAE). The sparse reconstruction handles the shape variations by representing a testing patch as a linear combination of shapes in the learned dictionary. Trivial templates are used to model the touching parts. The sDAE, trained with the original data and their structured labels, is used for cell segmentation. To the best of our knowledge, this is the first study to apply sparse reconstruction and sDAE with structured labels for cell detection and segmentation. The proposed method is extensively tested on two data sets containing more than 3000 cells obtained from brain tumor and lung cancer images. Our algorithm achieves the best performance compared with other state of the arts.","PeriodicalId":94280,"journal":{"name":"Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention","volume":"9351 ","pages":"383-390"},"PeriodicalIF":0.0,"publicationDate":"2015-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5081214/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140290109","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

Automated Model-Based Segmentation of the Left and Right Ventricles in Tagged Cardiac MRI. 标记心脏MRI中左心室和右心室的自动模型分割。

Albert Montillo, Dimitris Metaxas, Leon Axel

引用次数: 20

Automated Segmentation of the Left and Right Ventricles in 4D Cardiac SPAMM Images. 4D心脏SPAMM图像中左心室和右心室的自动分割。

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Pub Date : 2002-09-01 Epub Date: 2002-10-10 DOI: 10.1007/3-540-45786-0_77

Albert Montillo, Dimitris Metaxas, Leon Axel

{"title":"Automated Segmentation of the Left and Right Ventricles in 4D Cardiac SPAMM Images.","authors":"Albert Montillo, Dimitris Metaxas, Leon Axel","doi":"10.1007/3-540-45786-0_77","DOIUrl":"https://doi.org/10.1007/3-540-45786-0_77","url":null,"abstract":"In this paper we describe a completely automated volume-based method for the segmentation of the left and right ventricles in 4D tagged MR (SPAMM) images for quantitative cardiac analysis. We correct the background intensity variation in each volume caused by surface coils using a new scale-based fuzzy connectedness procedure. We apply 3D grayscale opening to the corrected data to create volumes containing only the blood filled regions. We threshold the volumes by minimizing region variance or by an adaptive statistical thresholding method. We isolate the ventricular blood filled regions using a novel approach based on spatial and temporal shape similarity. We use these regions to define the endocardium contours and use them to initialize an active contour that locates the epicardium through the gradient vector flow of an edgemap of a grayscale-closed image. Both quantitative and qualitative results on normal and diseased patients are presented.","PeriodicalId":94280,"journal":{"name":"Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention","volume":"2488 ","pages":"620-633"},"PeriodicalIF":0.0,"publicationDate":"2002-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/3-540-45786-0_77","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49687025","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: 59

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