Étude de contraintes spatiales bas niveau appliquées à la vision par ordinateur

Thèse numérisée par la Direction des bibliothèques de l'Université de Montréal

GridNet with automatic shape prior registration for automatic MRI cardiac segmentation

In this paper, we propose a fully automatic MRI cardiac segmentation method based on a novel deep convolutional neural network (CNN) designed for the 2017 ACDC MICCAI challenge. The novelty of our network comes with its embedded shape prior and its loss function tailored to the cardiac anatomy. Our model includes a cardiac centerof-mass regression module which allows for an automatic shape prior registration. Also, since our method processes raw MR images without any manual preprocessing and/or image cropping, our CNN learns both high-level features (useful to distinguish the heart from other organs with a similar shape) and low-level features (useful to get accurate segmentation results). Those features are learned with a multi-resolution conv-deconv "grid" architecture which can be seen as an extension of the U-Net. Experimental results reveal that our method can segment the left and right ventricles as well as the myocardium from a 3D MRI cardiac volume in 0.4 second with an average Dice coefficient of 0.90 and an average Hausdorff distance of 10.4 mm.Comment: 8 pages, 1 tables, 2 figure

Behavior subtraction

Background subtraction has been a driving engine for many computer vision and video analytics tasks. Although its many variants exist, they all share the underlying assumption that photometric scene properties are either static or exhibit temporal stationarity. While this works in many applications, the model fails when one is interested in discovering changes in scene dynamics instead of changes in scene's photometric properties; the detection of unusual pedestrian or motor traffic patterns are but two examples. We propose a new model and computational framework that assume the dynamics of a scene, not its photometry, to be stationary, i.e., a dynamic background serves as the reference for the dynamics of an observed scene. Central to our approach is the concept of an event, which we define as short-term scene dynamics captured over a time window at a specific spatial location in the camera field of view. Unlike in our earlier work, we compute events by time-aggregating vector object descriptors that can combine multiple features, such as object size, direction of movement, speed, etc. We characterize events probabilistically, but use low-memory, low-complexity surrogates in a practical implementation. Using these surrogates amounts to behavior subtraction, a new algorithm for effective and efficient temporal anomaly detection and localization. Behavior subtraction is resilient to spurious background motion, such as due to camera jitter, and is content-blind, i.e., it works equally well on humans, cars, animals, and other objects in both uncluttered and highly cluttered scenes. Clearly, treating video as a collection of events rather than colored pixels opens new possibilities for video analytics.Accepted manuscrip

Mixup-Privacy: A simple yet effective approach for privacy-preserving segmentation

Privacy protection in medical data is a legitimate obstacle for centralized machine learning applications. Here, we propose a client-server image segmentation system which allows for the analysis of multi-centric medical images while preserving patient privacy. In this approach, the client protects the to-be-segmented patient image by mixing it to a reference image. As shown in our work, it is challenging to separate the image mixture to exact original content, thus making the data unworkable and unrecognizable for an unauthorized person. This proxy image is sent to a server for processing. The server then returns the mixture of segmentation maps, which the client can revert to a correct target segmentation. Our system has two components: 1) a segmentation network on the server side which processes the image mixture, and 2) a segmentation unmixing network which recovers the correct segmentation map from the segmentation mixture. Furthermore, the whole system is trained end-to-end. The proposed method is validated on the task of MRI brain segmentation using images from two different datasets. Results show that the segmentation accuracy of our method is comparable to a system trained on raw images, and outperforms other privacy-preserving methods with little computational overhead