Simulation and synthesis in medical imaging : ... International Workshop, SASHIMI ..., held in conjunction with MICCAI ..., proceedings. SASHIMI (Workshop)最新文献

{"title":"Simulation and Synthesis in Medical Imaging: 5th International Workshop, SASHIMI 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Proceedings","authors":"Ninon Burgos, D. Svoboda, J. Wolterink, Can Zhao","doi":"10.1007/978-3-030-59520-3","DOIUrl":"https://doi.org/10.1007/978-3-030-59520-3","url":null,"abstract":"","PeriodicalId":91967,"journal":{"name":"Simulation and synthesis in medical imaging : ... International Workshop, SASHIMI ..., held in conjunction with MICCAI ..., proceedings. SASHIMI (Workshop)","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88209507","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}

{"title":"Simulation and Synthesis in Medical Imaging: 4th International Workshop, SASHIMI 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Proceedings","authors":"","doi":"10.1007/978-3-030-32778-1","DOIUrl":"https://doi.org/10.1007/978-3-030-32778-1","url":null,"abstract":"","PeriodicalId":91967,"journal":{"name":"Simulation and synthesis in medical imaging : ... International Workshop, SASHIMI ..., held in conjunction with MICCAI ..., proceedings. SASHIMI (Workshop)","volume":"34 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81337763","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}

{"title":"A supervoxel based random forest synthesis framework for bidirectional MR/CT synthesis.","authors":"Can Zhao,&nbsp;Aaron Carass,&nbsp;Junghoon Lee,&nbsp;Amod Jog,&nbsp;Jerry L Prince","doi":"10.1007/978-3-319-68127-6_4","DOIUrl":"https://doi.org/10.1007/978-3-319-68127-6_4","url":null,"abstract":"<p><p>Synthesizing magnetic resonance (MR) and computed tomography (CT) images (from each other) has important implications for clinical neuroimaging. The MR to CT direction is critical for MRI-based radiotherapy planning and dose computation, whereas the CT to MR direction can provide an economic alternative to real MRI for image processing tasks. Additionally, synthesis in both directions can enhance MR/CT multi-modal image registration. Existing approaches have focused on synthesizing CT from MR. In this paper, we propose a multi-atlas based hybrid method to synthesize T1-weighted MR images from CT and CT images from T1-weighted MR images using a common framework. The task is carried out by: (a) computing a label field based on supervoxels for the subject image using joint label fusion; (b) correcting this result using a random forest classifier (RF-C); (c) spatial smoothing using a Markov random field; (d) synthesizing intensities using a set of RF regressors, one trained for each label. The algorithm is evaluated using a set of six registered CT and MR image pairs of the whole head.</p>","PeriodicalId":91967,"journal":{"name":"Simulation and synthesis in medical imaging : ... International Workshop, SASHIMI ..., held in conjunction with MICCAI ..., proceedings. SASHIMI (Workshop)","volume":"10557 ","pages":"33-40"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/978-3-319-68127-6_4","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36496848","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}

{"title":"Registration of Pathological Images.","authors":"Xiao Yang,&nbsp;Xu Han,&nbsp;Eunbyung Park,&nbsp;Stephen Aylward,&nbsp;Roland Kwitt,&nbsp;Marc Niethammer","doi":"10.1007/978-3-319-46630-9_10","DOIUrl":"https://doi.org/10.1007/978-3-319-46630-9_10","url":null,"abstract":"<p><p>This paper proposes an approach to improve atlas-to-image registration accuracy with large pathologies. Instead of directly registering an atlas to a pathological image, the method learns a mapping from the pathological image to a quasi-normal image, for which more accurate registration is possible. Specifically, the method uses a deep variational convolutional encoder-decoder network to learn the mapping. Furthermore, the method estimates local mapping uncertainty through network inference statistics and uses those estimates to down-weight the image registration similarity measure in areas of high uncertainty. The performance of the method is quantified using synthetic brain tumor images and images from the brain tumor segmentation challenge (BRATS 2015).</p>","PeriodicalId":91967,"journal":{"name":"Simulation and synthesis in medical imaging : ... International Workshop, SASHIMI ..., held in conjunction with MICCAI ..., proceedings. SASHIMI (Workshop)","volume":"9968 ","pages":"97-107"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/978-3-319-46630-9_10","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36218184","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}

{"title":"Patch Based Synthesis of Whole Head MR Images: Application to EPI Distortion Correction.","authors":"Snehashis Roy, Yi-Yu Chou, Amod Jog, John A Butman, Dzung L Pham","doi":"10.1007/978-3-319-46630-9_15","DOIUrl":"10.1007/978-3-319-46630-9_15","url":null,"abstract":"<p><p>Different magnetic resonance imaging pulse sequences are used to generate image contrasts based on physical properties of tissues, which provide different and often complementary information about them. Therefore multiple image contrasts are useful for multimodal analysis of medical images. Often, medical image processing algorithms are optimized for particular image contrasts. If a desirable contrast is unavailable, contrast synthesis (or modality synthesis) methods try to \"synthesize\" the unavailable constrasts from the available ones. Most of the recent image synthesis methods generate synthetic brain images, while whole head magnetic resonance (MR) images can also be useful for many applications. We propose an atlas based patch matching algorithm to synthesize <i>T</i>₂-w whole head (including brain, skull, eyes etc) images from <i>T</i>₁-w images for the purpose of distortion correction of diffusion weighted MR images. The geometric distortion in diffusion MR images due to in-homogeneous <i>B</i>₀ magnetic field are often corrected by non-linearly registering the corresponding <i>b</i> = 0 image with zero diffusion gradient to an undistorted <i>T</i>₂-w image. We show that our synthetic <i>T</i>₂-w images can be used as a template in absence of a real <i>T</i>₂-w image. Our patch based method requires multiple atlases with <i>T</i>₁ and <i>T</i>₂ to be registeLowRes to a given target <i>T</i>₁. Then for every patch on the target, multiple similar looking matching patches are found on the atlas <i>T</i>₁ images and corresponding patches on the atlas <i>T</i>₂ images are combined to generate a synthetic <i>T</i>₂ of the target. We experimented on image data obtained from 44 patients with traumatic brain injury (TBI), and showed that our synthesized <i>T</i>₂ images produce more accurate distortion correction than a state-of-the-art registration based image synthesis method.</p>","PeriodicalId":91967,"journal":{"name":"Simulation and synthesis in medical imaging : ... International Workshop, SASHIMI ..., held in conjunction with MICCAI ..., proceedings. SASHIMI (Workshop)","volume":"9968 ","pages":"146-156"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5375117/pdf/nihms851685.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34877361","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}