Standardized Evaluation System for Left Ventricular Segmentation Algorithms in 3D Echocardiography

Bernard, Olivier; Bosch, Johan G.; Heyde, Brecht; Alessandrini, Martino; Barbosa, Daniel; Camarasu-Pop, Sorina; Cervenansky, Frédéric; Valette, Sébastien; Mirea, Oana; Bernier, Michel; Jodoin, Pierre-Marc; Domingos, Jaime Santo; Stebbing, Richard V.; Keraudren, Kevin; Oktay, Ozan; Caballero, José; Shi, Wei; Rueckert, Daniel; Milletarì, Fausto; Ahmadi, Seyed-Ahmad; Smistad, Erik; Lindseth, Frank; Stralen, Maartje M. van; Wang, Chen; Smedby, Örjan; Donal, Erwan; Monaghan, Mark; Papachristidis, Alex; Geleijnse, Marcel L.; Galli, Elena; D'hooge, Jan

doi:10.1109/tmi.2015.2503890

Cited by 94 publications

(71 citation statements)

References 40 publications

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“…To assess the relevance of the proposed localized tracking methodology on clinical data, the database of the recent Challenge on Endocardial Three‐Dimensional Ultrasound Segmentation (CETUS) was used . The database includes 45 sequences of 3D echocardiography acquired at 3 different hospitals and with 3 different brands of ultrasound machines.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…After isotropic resampling, the datasets presented a voxel resolution from 0.6 3 mm 3 to 1.1 3 mm 3 , with 13 to 47 frames. For further details regarding the CETUS data and ground truth delineations, the reader is kindly referred to Bernard et al and Papachristidis et al…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…To assess the relevance of the proposed localized tracking methodology on clinical data, the database of the recent Challenge on Endocardial Three-Dimensional Ultrasound Segmentation (CETUS) † was used. 50…”

Section: Data Descriptionmentioning

confidence: 99%

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Fast left ventricle tracking using localized anatomical affine optical flow

Queirós

Vilaça

Morais

et al. 2017

Numer Methods Biomed Eng

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In daily clinical cardiology practice, left ventricle (LV) global and regional function assessment is crucial for disease diagnosis, therapy selection, and patient follow-up. Currently, this is still a time-consuming task, spending valuable human resources. In this work, a novel fast methodology for automatic LV tracking is proposed based on localized anatomically constrained affine optical flow. This novel method can be combined to previously proposed segmentation frameworks or manually delineated surfaces at an initial frame to obtain fully delineated datasets and, thus, assess both global and regional myocardial function. Its feasibility and accuracy were investigated in 3 distinct public databases, namely in realistically simulated 3D ultrasound, clinical 3D echocardiography, and clinical cine cardiac magnetic resonance images. The method showed accurate tracking results in all databases, proving its applicability and accuracy for myocardial function assessment. Moreover, when combined to previous state-of-the-art segmentation frameworks, it outperformed previous tracking strategies in both 3D ultrasound and cardiac magnetic resonance data, automatically computing relevant cardiac indices with smaller biases and narrower limits of agreement compared to reference indices. Simultaneously, the proposed localized tracking method showed to be suitable for online processing, even for 3D motion assessment. Importantly, although here evaluated for LV tracking only, this novel methodology is applicable for tracking of other target structures with minimal adaptations.

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Section: Experiments and Resultsmentioning

confidence: 99%

Section: Experiments and Resultsmentioning

confidence: 99%

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Fast left ventricle tracking using localized anatomical affine optical flow

Queirós

Vilaça

Morais

et al. 2017

Numer Methods Biomed Eng

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“…We used 3DE data from CETUS challenge 2014 [16] for training and testing by segmentation accuracy (the mean of mean surface distance dM, hausdorff surface distance dH, modified dice similarity index D*). There are 45 sequences of 3D ultrasound volumes, 15 volumes of training set and 30 volumes of test dataset.…”

Section: Datasetsmentioning

confidence: 99%

“…In term of mean surface distance (dM), hausdorff surface distance (dH), modified dice similarity index (D*), we compared the accuracy of our method with the ground truth from cardiologist [16]. S and t S denote the surface from proposed method and surface from ground truth, respectively.…”

Section: Metricsmentioning

confidence: 99%

A Left Ventricular Segmentation Method on 3D Echocardiography Using Deep Learning and Snake

Dong

Luo

Sun

et al. 2016

2016 Computing in Cardiology Conference (CinC)

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Segmentation of left ventricular (LV) endocardium IntroductionCardiac left ventricular plays a crucial role in the cardiac functions and diagnosis of cardiac disease. Hence, LV volume estimation has attracted much research attention, and segmentation of left ventricular endocardium from 3D echocardiography (3DE) has become a hot topic. Although this is a challenging task, which has to handle many problems inherent in ultrasound imaging, such as low signal-noise ratio, edge dropout and artifacts, researchers have proposed many methods to segment the left ventricular endocardium from 3DE. The typical proposed method can be classified as: deformable models, statistical models and classification methods.Deformable models are the most common methods of the segmentation of LV endocardium, which translate the problem to an optimization of energy function which is defined by the geometrical constraints.[1] proposed a coupled deformable model which used the speckle statistics information and volume information to form and evolve two surfaces to segment the myocardium. This method can make an automatic segmentation of the full myocardium. Due to intensity distribution of ultrasound tend to Gaussian approximately, [2] combined regionand edge-based level to acquire coarse shape, then adopted this coarse shape as the initial boundary and additional constraint to deform to segment echocardiography.[3] made a motion prior energy as the new constraint of level-set to track the whole myocardium, and used anatomical and image information to adjust hyperparameters. Although many researches have shown deformable models are successful, the models depend much on the initialization and image conditions.Statistical models, such as active shape model and active appearance model, are based on large labeled information from experts.[4] used a 3D active shape model whose parameters were updated by an extended Kalman filter to segment 3D cardiac ultrasound and the results were promising.[5] proposed a model driven method combining 3D Active Shape Model with local appearance models to segment 3D left ventricular and quantify the left ventricular function. The experiments results suggested the proposed method can achieve acceptable accuracy for the segmentation of fast rotating ultrasound.[6] developed Multiview, multiframe and landmark, and dynamic programming constrained active appearance models. Local edge detector was incorporated into these models. Comparing the experiments results, dynamic programming constrained model is better than the landmark constrained model and active appearance model. Due to depending on a large annotated dataset, initialization and assumption of shape and appearance [7], statistical models are limited for the segmentation of LV.Classification methods train classifier which is based on different features to segment LV. [8] proposed an automated method and system which used knowledgebased probabilistic model and marginal space learning and can be used to detect standard multiplanar reformatted planes.[9]...

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Automatic Myocardial Strain Imaging in Echocardiography Using Deep Learning

Østvik

Smistad

Espeland

et al. 2018

Lecture Notes in Computer Science

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Recent studies in the field of deep learning suggest that motion estimation can be treated as a learnable problem. In this paper we propose a pipeline for functional imaging in echocardiography consisting of four central components, (i) classification of cardiac view, (ii) semantic partitioning of the left ventricle (LV) myocardium, (iii) regional motion estimates and (iv) fusion of measurements. A U-Net type of convolutional neural network (CNN) was developed to classify muscle tissue, and partitioned into a semantic measurement kernel based on LV length and ventricular orientation. Dense tissue motion was predicted using stacked U-Net architectures with image warping of intermediate flow, designed to tackle variable displacements. Training was performed on a mixture of real and synthetic data. The resulting segmentation and motion estimates was fused in a Kalman filter and used as basis for measuring global longitudinal strain. For reference, 2D ultrasound images from 21 subjects were acquired using a GE Vivid system. Data was analyzed by two specialists using a semi-automatic tool for longitudinal function estimates in a commercial system, and further compared to output of the proposed method. Qualitative assessment showed comparable deformation trends as the clinical analysis software. The average deviation for the global longitudinal strain was (−0.6 ± 1.6)% for apical four-chamber view. The system was implemented with Tensorflow, and working in an end-to-end fashion without any ad-hoc tuning. Using a modern graphics processing unit, the average inference time is estimated to (115 ± 3) ms per frame.

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Standardized Evaluation System for Left Ventricular Segmentation Algorithms in 3D Echocardiography

Cited by 94 publications

References 40 publications

Fast left ventricle tracking using localized anatomical affine optical flow

Fast left ventricle tracking using localized anatomical affine optical flow

A Left Ventricular Segmentation Method on 3D Echocardiography Using Deep Learning and Snake

Automatic Myocardial Strain Imaging in Echocardiography Using Deep Learning

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