0480504 20240103214826.820171027235959.9 85032330126 000437032100006 10.1007/978-3-319-68195-5_6 10 s. P cav_un_epca*0480503978-3-319-68195-52212-93912752-61ChamSpringer2018 Dynamic renal scintigraphy Regions of interest Blind source separation Factor analysis Variational Bayes method cav_un_auth*0352423 Bódiová L. CZ cav_un_auth*0267768 Tichý Ondřej UTIA-B Adaptivní systémy Department of Adaptive Systems AS AS Department of Adaptive Systems Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101207 Šmídl Václav UTIA-B Adaptivní systémy Department of Adaptive Systems AS AS Department of Adaptive Systems Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2017/AS/tichy-0480504.pdf Many diagnostic methods using scintigraphic image sequence require decomposition of the sequence into tissue images and their time-activity curves. Standard procedure for this task is still manual selection of regions of interest (ROIs) which can be highly subjective due to their overlaps and poor signal-to-noise ratio. This can be overcome by automatic decomposition, however, the results may not have good physiological meaning. In this contribution, we aim to combine these approaches in semi-supervised procedure which is based on Bayesian blind source separation with the possibility of manual interaction after each run until an acceptable solution is obtained. The manual interaction is based on manual ROI placement and using its position to modify the corresponding prior parameters of the model. Performance of the proposed method is studied on real scintigraphic image sequence as well as on estimation of the specific diagnostic parameter on representative dataset of 10 scintigraphic sequences. cav_un_auth*0352424 VI ECCOMAS Thematic Conference on Computational Vision and Medical Image Processing 20171018 20171020 Porto PT BB 10000 10100 10103 2019 3 PR RVO:67985556 http://hdl.handle.net/11104/0276748 cav_un_auth*0352425 Fakulta jaderná a fyzikálně inženýrská, ČVUT FJFI ČVUT CZ S RIV/67985556:_____/18:00480504!RIV19-AV0-67985556 192095136 Doplnění UT WOS a Scopus 3+4 Proceedings Paper Computer Science Artificial Intelligence|Computer Science Information Systems|Imaging Science Photographic Technology|Radiology Nuclear Medicine Medical Imaging Q4 2018 85032330126 SCOPUS PUBMED 000437032100006 WOS cav_un_epca*0480503 European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS 2017: VipIMAGE 2017) 27 978-3-319-68195-5 2212-9391 2212-9413 52 61 Cham Springer 2018 Lecture Notes in Computational Vision and Biomechanics 27