Semi-supervised Bayesian Source Separation of Scintigraphic Image Sequences

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F18%3A00480504" target="_blank" >RIV/67985556:_____/18:00480504 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-319-68195-5_6" target="_blank" >http://dx.doi.org/10.1007/978-3-319-68195-5_6</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-68195-5_6" target="_blank" >10.1007/978-3-319-68195-5_6</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Semi-supervised Bayesian Source Separation of Scintigraphic Image Sequences

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Semi-supervised Bayesian Source Separation of Scintigraphic Image Sequences

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10103 - Statistics and probability

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2018

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS 2017: VipIMAGE 2017)

ISBN

978-3-319-68195-5

ISSN

2212-9391

e-ISSN

2212-9413

Počet stran výsledku

10

Strana od-do

52-61

Název nakladatele

Springer

Místo vydání

Cham

Místo konání akce

Porto

Datum konání akce

18. 10. 2017

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000437032100006