| bibtype |
C -
Conference Paper (international conference)
|
| ARLID |
0480504 |
| utime |
20240103214826.8 |
| mtime |
20171027235959.9 |
| SCOPUS |
85032330126 |
| WOS |
000437032100006 |
| DOI |
10.1007/978-3-319-68195-5_6 |
| title
(primary) (eng) |
Semi-supervised Bayesian Source Separation of Scintigraphic Image Sequences |
| specification |
| page_count |
10 s. |
| media_type |
P |
|
| serial |
| ARLID |
cav_un_epca*0480503 |
| ISBN |
978-3-319-68195-5 |
| ISSN |
2212-9391 |
| title
|
European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS 2017: VipIMAGE 2017) |
| part_num |
27 |
| page_num |
52-61 |
| publisher |
| place |
Cham |
| name |
Springer |
| year |
2018 |
|
|
| keyword |
Dynamic renal scintigraphy |
| keyword |
Regions of interest |
| keyword |
Blind source separation |
| keyword |
Factor analysis |
| keyword |
Variational Bayes method |
| author
(primary) |
| ARLID |
cav_un_auth*0352423 |
| name1 |
Bódiová |
| name2 |
L. |
| country |
CZ |
|
| author
|
| ARLID |
cav_un_auth*0267768 |
| name1 |
Tichý |
| name2 |
Ondřej |
| institution |
UTIA-B |
| full_dept (cz) |
Adaptivní systémy |
| full_dept |
Department of Adaptive Systems |
| department (cz) |
AS |
| department |
AS |
| full_dept |
Department of Adaptive Systems |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0101207 |
| name1 |
Šmídl |
| name2 |
Václav |
| institution |
UTIA-B |
| full_dept (cz) |
Adaptivní systémy |
| full_dept |
Department of Adaptive Systems |
| department (cz) |
AS |
| department |
AS |
| full_dept |
Department of Adaptive Systems |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| source |
|
| cas_special |
| abstract
(eng) |
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. |
| action |
| ARLID |
cav_un_auth*0352424 |
| name |
VI ECCOMAS Thematic Conference on Computational Vision and Medical Image Processing |
| dates |
20171018 |
| mrcbC20-s |
20171020 |
| place |
Porto |
| country |
PT |
|
| RIV |
BB |
| FORD0 |
10000 |
| FORD1 |
10100 |
| FORD2 |
10103 |
| reportyear |
2019 |
| num_of_auth |
3 |
| presentation_type |
PR |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0276748 |
| cooperation |
| ARLID |
cav_un_auth*0352425 |
| name |
Fakulta jaderná a fyzikálně inženýrská, ČVUT |
| institution |
FJFI ČVUT |
| country |
CZ |
|
| confidential |
S |
| mrcbC83 |
RIV/67985556:_____/18:00480504!RIV19-AV0-67985556 192095136 Doplnění UT WOS a Scopus |
| mrcbC86 |
3+4 Proceedings Paper Computer Science Artificial Intelligence|Computer Science Information Systems|Imaging Science Photographic Technology|Radiology Nuclear Medicine Medical Imaging |
| mrcbT16-4 |
Q4 |
| arlyear |
2018 |
| mrcbU14 |
85032330126 SCOPUS |
| mrcbU24 |
PUBMED |
| mrcbU34 |
000437032100006 WOS |
| mrcbU63 |
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 |
|