MRBrainS Challenge: Online Evaluation Framework for Brain Image Segmentation in 3T MRI Scans.

Many methods have been proposed for tissue segmentation in brain MRI scans. The multitude of methods proposed complicates the choice of one method above others. We have therefore established the MRBrainS online evaluation framework for evaluating (semi)automatic algorithms that segment gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) on 3T brain MRI scans of elderly subjects (65-80 y). Participants apply their algorithms to the provided data, after which their results are evaluated and ranked. Full manual segmentations of GM, WM, and CSF are available for all scans and used as the reference standard. Five datasets are provided for training and fifteen for testing. The evaluated methods are ranked based on their overall performance to segment GM, WM, and CSF and evaluated using three evaluation metrics (Dice, H95, and AVD) and the results are published on the MRBrainS13 website. We present the results of eleven segmentation algorithms that participated in the MRBrainS13 challenge workshop at MICCAI, where the framework was launched, and three commonly used freeware packages: FreeSurfer, FSL, and SPM. The MRBrainS evaluation framework provides an objective and direct comparison of all evaluated algorithms and can aid in selecting the best performing method for the segmentation goal at hand.

Endocardial surface delineation in 3-D transesophageal echocardiography.

We describe and compare several methods for recovering endocardial walls from 3-D transesophageal echocardiography (3-D TEE), which can help with diagnostics or providing input into biomechanical models. We employ a segmentation method based on 3-D level sets that maximizes enclosed volume while minimizing surface area and uses a growth inhibition function that includes 3-D gradient magnitude (to locate the endocardial walls) and a thin tissue detector (for the mitral valve leaflets). We also study delineation using a graph cut method that performs automated seeding by leveraging a fast radial symmetry transform to determine a central axis along which the 3-D volume is warped into a cylindrical coordinate space. Finally, a random walker approach is also used for automated delineation. The methods are used to estimate clinically relevant cardiovascular volumetric parameters such as stroke volume and left ventricular ejection fraction. Experiments are performed on clinical data collected from patients undergoing cardiothoracic surgery. Performance evaluation includes comparisons of the automated delineations against expert-defined ground truth using a number of error metrics, as well as errors between automatically computed and expert-derived physiologic parameters.

Patient-specific mitral valve closure prediction using 3D echocardiography.

This article presents an approach to modeling the closure of the mitral valve using patient-specific anatomical information derived from 3D transesophageal echocardiography (TEE). Our approach uses physics-based modeling to solve for the stationary configuration of the closed valve structure from the patient-specific open valve structure, which is recovered using a user-in-the-loop, thin-tissue detector segmentation. The method uses a tensile shape-finding approach based on energy minimization. This method is employed to predict the aptitude of the mitral valve leaflets to coapt. We tested the method using 10 intraoperative 3D TEE sequences by comparing the closed valve configuration predicted from the segmented open valve with the segmented closed valve, taken as ground truth. Experiments show promising results, with prediction errors on par with 3D TEE resolution and with good potential for applications in pre-operative planning.

Computing myocardial motion in 4-dimensional echocardiography.

We describe a novel method for computing dense 3D myocardial motion with high accuracy in four-dimensional (4D) echocardiography (3 dimensions spatial plus time). The method is based on a classic variational optical flow technique but exploits modern developments in optical flow research to utilize the full capabilities of 4D echocardiography. Using a variety of metrics, we present an in-depth performance evaluation of the method on synthetic, phantom, and intraoperative 4D transesophageal echocardiographic data. When compared with state-of-the-art optical flow and speckle tracking techniques currently found in 4D echocardiography, the method we present shows notable improvements in error rates. We believe the performance improvements shown can have a positive impact when the method is used as input for various applications, such as strain computation, biomechanical modeling, and automated diagnostics.