Real-time 3D interactive segmentation of echocardiographic data through user-based deformation of B-spline explicit active surfaces

Validation of a Novel Software Tool for Automatic Aortic Annular Sizing in Three-Dimensional Transesophageal Echocardiographic Images

BACKGROUND

Accurate aortic annulus (AoA) sizing is crucial for transcatheter aortic valve implantation planning. Three-dimensional (3D) transesophageal echocardiography (TEE) is a viable alternative to the standard multidetector row computed tomography (MDCT) for such assessment, with few automatic software solutions available. The aim of this study was to present and evaluate a novel software tool for automatic AoA sizing by 3D TEE.

METHODS

One hundred one patients who underwent both preoperative MDCT and 3D TEE were retrospectively analyzed using the software. The automatic software measurements' accuracy was compared against values obtained using standard manual MDCT, as well as against those obtained using manual 3D TEE, and intraobserver, interobserver, and test-retest reproducibility was assessed. Because the software can be used as a fully automatic or as an interactive tool, both options were addressed and contrasted. The impact of these measures on the recommended prosthesis size was then evaluated to assess if the software's automated sizes were concordant with those obtained using an MDCT- or a TEE-based manual sizing strategy.

RESULTS

The software showed very good agreement with manual values obtained using MDCT and 3D TEE, with the interactive approach having slightly narrower limits of agreement. The latter also had excellent intra- and interobserver variability. Both fully automatic and interactive analyses showed excellent test-retest reproducibility, with the first having a faster analysis time. Finally, either approach led to good sizing agreement against the true implanted sizes (>77%) and against MDCT-based sizes (>88%).

CONCLUSIONS

Given the automated, reproducible, and fast nature of its analyses, the novel software tool presented here may potentially facilitate and thus increase the use of 3D TEE for preoperative transcatheter aortic valve implantation sizing.

A Combined Fully Convolutional Networks and Deformable Model for Automatic Left Ventricle Segmentation Based on 3D Echocardiography

Segmentation of the left ventricle (LV) from three-dimensional echocardiography (3DE) plays a key role in the clinical diagnosis of the LV function. In this work, we proposed a new automatic method for the segmentation of LV, based on the fully convolutional networks (FCN) and deformable model. This method implemented a coarse-to-fine framework. Firstly, a new deep fusion network based on feature fusion and transfer learning, combining the residual modules, was proposed to achieve coarse segmentation of LV on 3DE. Secondly, we proposed a method of geometrical model initialization for a deformable model based on the results of coarse segmentation. Thirdly, the deformable model was implemented to further optimize the segmentation results with a regularization item to avoid the leakage between left atria and left ventricle to achieve the goal of fine segmentation of LV. Numerical experiments have demonstrated that the proposed method outperforms the state-of-the-art methods on the challenging CETUS benchmark in the segmentation accuracy and has a potential for practical applications.

Left Ventricular Myocardial Segmentation in 3-D Ultrasound Recordings: Effect of Different Endocardial and Epicardial Coupling Strategies

Cardiac volume/function assessment remains a critical step in daily cardiology, and 3-D ultrasound plays an increasingly important role. Though development of automatic endocardial segmentation methods has received much attention, the same cannot be said about epicardial segmentation, in spite of the importance of full myocardial segmentation. In this paper, different ways of coupling the endocardial and epicardial segmentations are contrasted and compared with uncoupled segmentation. For this purpose, the B-spline explicit active surfaces framework was used; 27 3-D echocardiographic images were used to validate the different coupling strategies, which were compared with manual contouring of the endocardial and epicardial borders performed by an expert. It is shown that an independent segmentation of the endocardium followed by an epicardial segmentation coupled to the endocardium is the most advantageous. In this way, a framework for fully automatic 3-D myocardial segmentation is proposed using a novel coupling strategy.

Semi-automatic outlining of levator hiatus

OBJECTIVE

To create a semi-automated outlining tool for the levator hiatus, to reduce interobserver variability and and speed up analysis.

METHODS

The proposed automated hiatus segmentation (AHS) algorithm takes a C-plane image, in the plane of minimal hiatal dimensions, and manually defined vertical hiatal limits as input. The AHS then creates an initial outline by fitting predefined templates on an intensity-invariant edge map, which is further refined using the B-spline explicit active surfaces framework. The AHS was tested using 91 representative C-plane images. Reference hiatal outlines were obtained manually and compared with the AHS outlines by three independent observers. The mean absolute distance (MAD), Hausdorff distance and Dice and Jaccard coefficients were used to quantify segmentation accuracy. Each of these metrics was calculated both for computer-observer differences (COD) and for interobserver differences. The Williams index was used to test the null hypothesis that the automated method would agree with the operators at least as well as the operators agreed with each other. Agreement between the two methods was assessed using the intraclass correlation coefficient (ICC) and Bland-Altman plots.

RESULTS

The AHS contours matched well with the manual ones (median COD, 2.10 (interquartile range (IQR), 1.54) mm for MAD). The Williams index was greater than or close to 1 for all quality metrics, indicating that the algorithm performed at least as well as did the manual references in terms of interrater variability. The interobserver differences using each of the metrics were significantly lower, and a higher ICC was achieved (0.93), when obtaining outlines using the AHS compared with manually. The Bland-Altman plots showed negligible bias between the two methods. Using the AHS took a median time of 7.07 (IQR, 3.49) s, while manual outlining took 21.31 (IQR, 5.43) s, thus being almost three-fold faster. Using the AHS, in general, the hiatus could be outlined completely using only three points, two for initialization and one for manual adjustment.

CONCLUSIONS

We present a method for tracing the levator hiatal outline with minimal user input. The AHS is fast, robust and reliable and improves interrater agreement. Copyright © 2015 ISUOG. Published by John Wiley & Sons Ltd.

FISICO: Fast Image SegmentatIon COrrection

Background and Purpose

In clinical diagnosis, medical image segmentation plays a key role in the analysis of pathological regions. Despite advances in automatic and semi-automatic segmentation techniques, time-effective correction tools are commonly needed to improve segmentation results. Therefore, these tools must provide faster corrections with a lower number of interactions, and a user-independent solution to reduce the time frame between image acquisition and diagnosis.

Methods

We present a new interactive method for correcting image segmentations. Our method provides 3D shape corrections through 2D interactions. This approach enables an intuitive and natural corrections of 3D segmentation results. The developed method has been implemented into a software tool and has been evaluated for the task of lumbar muscle and knee joint segmentations from MR images.

Results

Experimental results show that full segmentation corrections could be performed within an average correction time of 5.5±3.3 minutes and an average of 56.5±33.1 user interactions, while maintaining the quality of the final segmentation result within an average Dice coefficient of 0.92±0.02 for both anatomies. In addition, for users with different levels of expertise, our method yields a correction time and number of interaction decrease from 38±19.2 minutes to 6.4±4.3 minutes, and 339±157.1 to 67.7±39.6 interactions, respectively.

Left-Atrial Segmentation From 3-D Ultrasound Using B-Spline Explicit Active Surfaces With Scale Uncoupling

Segmentation of the left atrium (LA) of the heart allows quantification of LA volume dynamics which can give insight into cardiac function. However, very little attention has been given to LA segmentation from three-dimensional (3-D) ultrasound (US), most efforts being focused on the segmentation of the left ventricle (LV). The B-spline explicit active surfaces (BEAS) framework has been shown to be a very robust and efficient methodology to perform LV segmentation. In this study, we propose an extension of the BEAS framework, introducing B-splines with uncoupled scaling. This formulation improves the shape support for less regular and more variable structures, by giving independent control over smoothness and number of control points. Semiautomatic segmentation of the LA endocardium using this framework was tested in a setup requiring little user input, on 20 volumetric sequences of echocardiographic data from healthy subjects. The segmentation results were evaluated against manual reference delineations of the LA. Relevant LA morphological and functional parameters were derived from the segmented surfaces, in order to assess the performance of the proposed method on its clinical usage. The results showed that the modified BEAS framework is capable of accurate semiautomatic LA segmentation in 3-D transthoracic US, providing reliable quantification of the LA morphology and function.

Mutual cineMR/RT3DUS cardiac segmentation

We present a new method to segment a cardiac RT3D ultrasound volume by integrating the registered segmentation of a cardiac cine-MR series in short axis of the same patient. The motivation behind our method is to improve the ultrasound segmentation process by integrating a reference shape built using the cine-MR segmentation on the same patient. As a side effect we obtain a close registration of the cine MR short axis slices with respect to the ultrasound volume. We use the level set framework with a functional including a region-based and a shape-based term. The reference shape is iteratively registered onto the contour during the ultrasound segmentation process and using an affine transform. The proposed method is demonstrated on the MICCAI11 Motion Tracking Challenge database.