Molecular imaging, reconstruction and analysis of moving body organs, and stroke imaging and treatment : Fifth International Workshop, CMMI 2017, Second International Workshop, RAMBO 2017, and First International Workshop, SWITCH 2017, ...最新文献

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3D Alpha Matting Based Co-segmentation of Tumors on PET-CT Images. 基于 PET-CT 图像上肿瘤的 3D Alpha Matting 协同分割。

Molecular imaging, reconstruction and analysis of moving body organs, and stroke imaging and treatment : Fifth International Workshop, CMMI 2017, Second International Workshop, RAMBO 2017, and First International Workshop, SWITCH 2017, ... Pub Date : 2017-09-01 Epub Date: 2017-09-09 DOI: 10.1007/978-3-319-67564-0_4

Zisha Zhong, Yusung Kim, John Buatti, Xiaodong Wu

{"title":"3D Alpha Matting Based Co-segmentation of Tumors on PET-CT Images.","authors":"Zisha Zhong, Yusung Kim, John Buatti, Xiaodong Wu","doi":"10.1007/978-3-319-67564-0_4","DOIUrl":"10.1007/978-3-319-67564-0_4","url":null,"abstract":"Positron emission tomography - computed tomography (PET-CT) has been widely used in modern cancer imaging. Accurate tumor delineation from PET and CT plays an important role in radiation therapy. The PET-CT co-segmentation technique, which makes use of advantages of both modalities, has achieved impressive performance for tumor delineation. In this work, we propose a novel 3D image matting based semi-automated co-segmentation method for tumor delineation on dual PET-CT scans. The \"matte\" values generated by 3D image matting are employed to compute the region costs for the graph based co-segmentation. Compared to previous PET-CT co-segmentation methods, our method is completely data-driven in the design of cost functions, thus using much less hyper-parameters in our segmentation model. Comparative experiments on 54 PET-CT scans of lung cancer patients demonstrated the effectiveness of our method.","PeriodicalId":92268,"journal":{"name":"Molecular imaging, reconstruction and analysis of moving body organs, and stroke imaging and treatment : Fifth International Workshop, CMMI 2017, Second International Workshop, RAMBO 2017, and First International Workshop, SWITCH 2017, ...","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6886662/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78698800","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

Motion-Robust Spatially Constrained Parameter Estimation in Renal Diffusion-Weighted MRI by 3D Motion Tracking and Correction of Sequential Slices. 基于三维运动跟踪和序列切片校正的肾弥散加权MRI运动鲁棒空间约束参数估计。

Molecular imaging, reconstruction and analysis of moving body organs, and stroke imaging and treatment : Fifth International Workshop, CMMI 2017, Second International Workshop, RAMBO 2017, and First International Workshop, SWITCH 2017, ... Pub Date : 2017-01-01 Epub Date: 2017-09-09 DOI: 10.1007/978-3-319-67564-0_8

Sila Kurugol, Bahram Marami, Onur Afacan, Simon K Warfield, Ali Gholipour

{"title":"Motion-Robust Spatially Constrained Parameter Estimation in Renal Diffusion-Weighted MRI by 3D Motion Tracking and Correction of Sequential Slices.","authors":"Sila Kurugol, Bahram Marami, Onur Afacan, Simon K Warfield, Ali Gholipour","doi":"10.1007/978-3-319-67564-0_8","DOIUrl":"https://doi.org/10.1007/978-3-319-67564-0_8","url":null,"abstract":"In this work, we introduce a novel motion-robust spatially constrained parameter estimation (MOSCOPE) technique for kidney diffusion-weighted MRI. The proposed motion compensation technique does not require a navigator, trigger, or breath-hold but only uses the intrinsic features of the acquired data to track and compensate for motion to reconstruct precise models of the renal diffusion signal. We have developed a technique for physiological motion tracking based on robust state estimation and sequential registration of diffusion sensitized slices acquired within 200ms. This allows a sampling rate of 5Hz for state estimation in motion tracking that is sufficiently faster than both respiratory and cardiac motion rates in children and adults, which range between 0.8 to 0.2Hz, and 2.5 to 1Hz, respectively. We then apply the estimated motion parameters to data from each slice and use motion-compensated data for 1) robust intra-voxel incoherent motion (IVIM) model estimation in the kidney using a spatially constrained model fitting approach, and 2) robust weighted least squares estimation of the diffusion tensor model. Experimental results, including precision of IVIM model parameters using bootstrap-sampling and in-vivo whole kidney tractography, showed significant improvement in precision and accuracy of these models using the proposed method compared to models based on the original data and volumetric registration.","PeriodicalId":92268,"journal":{"name":"Molecular imaging, reconstruction and analysis of moving body organs, and stroke imaging and treatment : Fifth International Workshop, CMMI 2017, Second International Workshop, RAMBO 2017, and First International Workshop, SWITCH 2017, ...","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/978-3-319-67564-0_8","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35842725","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