Place: Large lecture Room
Affiliation: Universitat de Barcelona and Computer Vision Centre
In this seminar, several methods for the automatic analysis of Intravascular Ultrasound (IVUS) sequences are presented, aimed at assisting physicians in the diagnosis, the assessment of the intervention and the monitoring of the patients with coronary disease. The basis for the developed frameworks are machine learning, pattern recognition and image processing techniques.
First, a novel approach for the automatic detection of vascular bifurcations in IVUS is presented. The task is addressed as a binary classification problem (identifying bifurcation and non-bifurcation angular sectors in the sequence images). The multiscale stacked sequential learning algorithm is applied, to take into account the spatial and temporal context in IVUS sequences, and the results are refined using a-priori information about branching dimensions and geometry. The achieved performance is comparable to intra- and inter-observer variability.
Then, we propose a novel method for the automatic non-rigid alignment of IVUS sequences of the same patient, acquired at different moments (before and after percutaneous coronary intervention, or at baseline and follow-up examinations). The method is based on the description of the morphological content of the vessel, obtained by extracting temporal morphological profiles from the IVUS acquisitions, by means of methods for segmentation, characterization and detection in IVUS. A technique for non-rigid sequence alignment – the Dynamic Time Warping algorithm – is applied to the profiles and adapted to the specific clinical problem. Two different robust strategies are proposed to address the partial overlapping between frames of corresponding sequences, and a regularization term is introduced to compensate for possible errors in the profile extraction. The benefits of the proposed strategy are demonstrated by extensive validation on synthetic and in-vivo data. The results show the interest of the proposed non-linear alignment and the clinical value of the method.
Finally, a novel automatic approach for the extraction of the luminal border in IVUS images is presented. The method applies the multiscale stacked sequential learning algorithm and extends it to 2-D+T, in a first classification phase (the identification of lumen and non-lumen regions of the images), while an active contour model is used in a second phase, to identify the lumen contour. The method is extended to the longitudinal dimension of the sequences and it is validated on a challenging data-set.