PT Unknown
AU Francesco Ciompi
   Oriol Pujol
   Oriol Rodriguez-Leor
   Angel Serrano
   J. Mauri
   Petia Radeva
TI On in-vitro and in-vivo IVUS data fusion
BT 12th International Conference of the Catalan Association for Artificial Intelligence
PY 2009
BP 147
EP 156
VL 202
DI 10.3233/978-1-60750-061-2-147
AB The design and the validation of an automatic plaque characterization technique based on Intravascular Ultrasound (IVUS) usually requires a data ground-truth. The histological analysis of post-mortem coronary arteries is commonly assumed as the state-of-the-art process for the extraction of a reliable data-set of atherosclerotic plaques. Unfortunately, the amount of data provided by this technique is usually few, due to the difficulties in collecting post-mortem cases and phenomena of tissue spoiling during histological analysis. In this paper we tackle the process of fusing in-vivo and in-vitro IVUS data starting with the analysis of recently proposed approaches for the creation of an enhanced IVUS data-set; furthermore, we propose a new approach, named pLDS, based on semi-supervised learning with a data selection criterion. The enhanced data-set obtained by each one of the analyzed approaches is used to train a classifier for tissue characterization purposes. Finally, the discriminative power of each classifier is quantitatively assessed and compared by classifying a data-set of validated in-vitro IVUS data.
ER