Delineation of the left ventricular cavity, myocardium and right ventricle from cardiac magnetic resonance images (multi-slice 2D 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 on-line, while the platform will remain open for new submissions.

Measures of overlap of labelled regions of images, such as the Dice and Tanimoto coefficients, have been extensively used to evaluate image registration and segmentation algorithms. Modern studies can include multiple labels defined on multiple images yet most evaluation schemes report one overlap per labelled region, simply averaged over multiple images. In this paper, common overlap measures are generalized to measure the total overlap of ensembles of labels defined on multiple test images and account for fractional labels using fuzzy set theory. This framework allows a single "figure-of-merit" to be reported which summarises the results of a complex experiment by image pair, by label or overall. A complementary measure of error, the overlap distance, is defined which captures the spatial extent of the nonoverlapping part and is related to the Hausdorff distance computed on grey level images. The generalized overlap measures are validated on synthetic images for which the overlap can be computed analytically and used as similarity measures in nonrigid registration of three-dimensional magnetic resonance imaging (MRI) brain images. Finally, a pragmatic segmentation ground truth is constructed by registering a magnetic resonance atlas brain to 20 individual scans, and used with the overlap measures to evaluate publicly available brain segmentation algorithms

Background—: Conducting channels are the target for ventricular tachycardia (VT) ablation. Conducting channels could be identified with contrast enhanced–cardiac magnetic resonance (ce-CMR) as border zone (BZ) corridors. A 3-dimensional (3D) reconstruction of the ce-CMR could allow visualization of the 3D structure of these BZ channels. Methods and Results—: We included 21 patients with healed myocardial infarction and VT. A 3D high-resolution 3T ce-CMR was performed before CARTO-guided VT ablation. The left ventricular wall was segmented and characterized using a pixel signal intensity algorithm at 5 layers (endocardium, 25%, 50%, 75%, epicardium). A 3D color-coded shell map was obtained for each layer to depict the scar core and BZ distribution. The presence/characteristics of BZ channels were registered for each layer. Scar area decreased progressively from endocardium to epicardium (scar area/left ventricular area: 34.0±17.4% at endocardium, 24.1±14.7% at 25%, 16.3±12.1% at 50%, 13.1±10.4 at 75%, 12.1±9.3% at epicardium; P <0.01). Forty-five BZ channels (2.1±1.0 per patient, 23.7±12.0 mm length, mean minimum width 2.5±1.5 mm) were identified, 85% between the endocardium and 50% shell and 76% present in ≥1 layer. The ce-CMR–defined BZ channels identified 74% of the critical isthmus of clinical VTs and 50% of all the conducting channels identified in electroanatomic maps. Conclusions—: Scar area in patients with healed myocardial infarction decreases from the endocardium to the epicardium. BZ channels, more commonly seen in the endocardium, display a 3D structure within the myocardial wall that can be depicted with ce-CMR. The use of ce-CMR–derived maps to guide VT ablation warrants further investigation.

This paper presents a new registration algorithm, called Temporal Diffeomorphic Free Form Deformation (TDFFD), and its application to motion and strain quantification from a sequence of 3D ultrasound (US) images. The originality of our approach resides in enforcing time consistency by representing the 4D velocity field as the sum of continuous spatiotemporal B-Spline kernels. The spatiotemporal displacement field is then recovered through forward Eulerian integration of the non-stationary velocity field. The strain tensor is computed locally using the spatial derivatives of the reconstructed displacement field. The energy functional considered in this paper weighs two terms: the image similarity and a regularization term. The image similarity metric is the sum of squared differences between the intensities of each frame and a reference one. Any frame in the sequence can be chosen as reference. The regularization term is based on the incompressibility of myocardial tissue. TDFFD was compared to pairwise 3D FFD and 3D+t FFD, both on displacement and velocity fields, on a set of synthetic 3D US images with different noise levels. TDFFD showed increased robustness to noise compared to these two state-of-the-art algorithms. TDFFD also proved to be more resistant to a reduced temporal resolution when decimating this synthetic sequence. Finally, this synthetic dataset was used to determine optimal settings of the TDFFD algorithm. Subsequently, TDFFD was applied to a database of cardiac 3D US images of the left ventricle acquired from 9 healthy volunteers and 13 patients treated by Cardiac Resynchronization Therapy (CRT). On healthy cases, uniform strain patterns were observed over all myocardial segments, as physiologically expected. On all CRT patients, the improvement in synchrony of regional longitudinal strain correlated with CRT clinical outcome as quantified by the reduction of end-systolic left ventricular volume at follow-up (6 and 12months), showing the potential of the proposed algorithm for the assessment of CRT.