Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved?

Delineation of the left ventricular cavity, myocardium, and right ventricle from cardiac magnetic resonance images (multi-slice 2-D cine MRI) is a common clinical task to establish diagnosis. The automation of the corresponding tasks has thus been the subject of intense research over the past decades. In this paper, we introduce the "Automatic Cardiac Diagnosis Challenge" dataset (ACDC), the largest publicly available and fully annotated dataset for the purpose of cardiac MRI (CMR) assessment. The dataset contains data from 150 multi-equipments CMRI recordings with reference measurements and classification from two medical experts. The overarching objective of this paper is to measure how far state-of-the-art deep learning methods can go at assessing CMRI, i.e., segmenting the myocardium and the two ventricles as well as classifying pathologies. In the wake of the 2017 MICCAI-ACDC challenge, we report results from deep learning methods provided by nine research groups for the segmentation task and four groups for the classification task. Results show that the best methods faithfully reproduce the expert analysis, leading to a mean value of 0.97 correlation score for the automatic extraction of clinical indices and an accuracy of 0.96 for automatic diagnosis. These results clearly open the door to highly accurate and fully automatic analysis of cardiac CMRI. We also identify scenarios for which deep learning methods are still failing. Both the dataset and detailed results are publicly available online, while the platform will remain open for new submissions

Is a Memoryless Motion Detection Truly Relevant for Background Generation with LaBGen?

peer reviewedThe stationary background generation problem consists in generating a unique image representing the stationary background of a given video sequence. The LaBGen background generation method combines a pixel-wise median filter and a patch selection mechanism based on a motion detection performed by a background subtraction algorithm. In our previous works related to LaBGen, we have shown that, surprisingly, the frame difference algorithm provides the most effective motion detection on average. Compared to other background subtraction algorithms, it detects motion between two frames without relying on additional past frames, and is therefore memoryless. In this paper, we experimentally check whether the memoryless property is truly relevant for LaBGen, and whether the effective motion detection provided by the frame difference is not an isolated case. For this purpose, we introduce LaBGen-OF, a variant of LaBGen leverages memoryless dense optical flow algorithms for motion detection. Our experiments show that using a memoryless motion detector is an adequate choice for our background generation framework, and that LaBGen-OF outperforms LaBGen on the SBMnet dataset. We further provide an open-source C++ implementation of both methods at http://www.telecom.ulg.ac.be/labgen

A software for performance evaluation and comparison of people detection and tracking methods in video processing

WOS: 000294504600014Digital video content analysis is an important item for multimedia content-based indexing (MCBI), content-based video retrieval (CBVR) and visual surveillance systems. There are some frequently-used generic object detection and/or tracking (D&T) algorithms in the literature, such as Background Subtraction (BS), Continuously Adaptive Mean Shift (CMS), Optical Flow (OF) and etc. An important problem for performance evaluation is the absence of stable and flexible software for comparison of different algorithms. This software is able to compare them with the same metrics in real-time and at the same platform. In this paper, we have designed and implemented the software for the performance comparison and the evaluation of well-known video object D&T algorithms (for people D&T) at the same platform. The software works as an automatic and/or semi-automatic test environment in real-time, which uses the image and video processing essentials, e.g. morphological operations and filters, and ground-truth (GT) XML data files, charting/plotting capabilities and etc