<big><big></big></big><small><big><big>Martin Simonovsky, LIGM, A3SI, IMAGINE</big></big></small>
<big>A Deep Metric for Multimodal Registration</big>
<big></big>Multimodal registration is a challenging problem in medical imaging due the high variability of tissue appearance under different imaging modalities. The crucial component here is the choice of the right similarity measure. We make a step towards a general learning-based solution that can be adapted to specific situations and present a metric based on a convolutional neural network. Our network can be trained from scratch even from a few aligned image pairs. The metric is validated on intersubject deformable registration on a dataset different from the one used for training, demonstrating good generalization. In this task, we outperform mutual information by a significant margin.
<small><big><big>Ketan Bacchuwar, LIGM, A3SI, ESIEE</big></big></small>
<big>Towards PCI procedure modelling: empty catheter segmentation</big>
<big></big>We present empty guiding catheter segmentation, a preliminary result in the development of a complete framework of Percutaneous Coronary Intervention (PCI) procedure modelling (analysis of image stream in term of clinical activities). In number of clinical situations, the guiding catheter, a commonly visible landmark is empty and appears as a low contrasted structure with two parallel and partially disconnected edges. To segment it, we work on the level-set (non-linear) scale-space of image, the min tree, to extract curve blobs. We then propose a novel structural scale-space, a hierarchy built on these curve blobs. The deep connected component, i.e. the cluster of curve blobs on this hierarchy, that maximizes the likelihood to be an empty catheter is retained as final segmentation. We develop a novel structural scale-space to segment out a structured object, the empty catheter in challenging imaging situations where the information content is very sparse.
<small><big><big>Stéphane Breuils, LIGM, A3SI, ESIEE</big></big></small>
<big>An efficient implementation of geometric algebra to handle high and low dimensional spaces</big>
<big></big>Geometric algebras can be understood as a set of very intuitive tools to represent, construct and manipulate geometric objects. There already exists some methods to represent and compute geometric algebra elements but none of them can handle efficiently high dimensional spaces. During this talk, I will present a method based on binary trees and table to efficiently compute Geometric algebra. The key feature of our approach is to optimize the complexity of the products. In order to achieve this, we exploit the particular structure of geometric algebra products. The resulting implementations are usable for any dimensions, including high dimensions. The tests show that our implementation is faster for high dimensional spaces and at least as fast as for low dimensional spaces.