Segmentation of Multi-Center 3D Left Ventricular Echocardiograms by Active Appearance Models

Stralen, Marijn van; Haak, Alexander; Leung, KK; Burken, Gerard van; Bosch, Johan G.

doi:10.54294/cnimu5

Cited by 4 publications

(1 citation statement)

References 10 publications

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“…It includes image-based techniques (multi-scale quadrature filter) [9], a motion-based method (Kalman filter) [10], deformable models (BEAS, level-set) [4], [9] and a graph-based approach (graph-cut) [11]. The second group uses approaches with strong priors including a shape-prior (Hough forest) [12], an https://www.creatis.insa-lyon.fr/Challenge/CETUS/ active appearance model [13], an atlas-based method [14] and a machine learning algorithm (random forest) [12], [15], [16], each requiring a manually-annotated training set.…”

Section: Non-deep Learning Methodsmentioning

confidence: 99%

Deep Learning for Segmentation Using an Open Large-Scale Dataset in 2D Echocardiography

Leclerc

Smistad²,

Pedrosa³

et al. 2019

IEEE Trans. Med. Imaging

405

306

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Delineation of the cardiac structures from 2D echocardiographic images is a common clinical task to establish a diagnosis. Over the past decades, the automation of this task has been the subject of intense research. In this paper, we evaluate how far the state-of-the-art encoder-decoder deep convolutional neural network methods can go at assessing 2D echocardiographic images, i.e. segmenting cardiac structures as well as estimating clinical indices, on a dataset especially designed to answer this objective. We therefore introduce the Cardiac Acquisitions for Multi-structure Ultrasound Segmentation (CA-MUS) dataset, the largest publicly-available and fully-annotated dataset for the purpose of echocardiographic assessment. The dataset contains two and four-chamber acquisitions from 500 patients with reference measurements from one cardiologist on the full dataset and from three cardiologists on a fold of 50 patients. Results show that encoder-decoder based architectures outperform state-of-the-art non-deep learning methods and faithfully reproduce the expert analysis for the end-diastolic and endsystolic left ventricular volumes, with a mean correlation of 0.95 and an absolute mean error of 9.5 ml. Concerning the ejection fraction of the left ventricle, results are more contrasted with a mean correlation coefficient of 0.80 and an absolute mean error of 5.6%. Although these results are below the inter-observer scores, they remain slightly worse than the intra-observer's ones. Based on this observation, areas for improvement are defined, which open the door for accurate and fully-automatic analysis of 2D echocardiographic images.Index Terms-Cardiac segmentation and diagnosis, deep learning, ultrasound, left ventricle, myocardium, left atrium.

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Section: Non-deep Learning Methodsmentioning

confidence: 99%

Deep Learning for Segmentation Using an Open Large-Scale Dataset in 2D Echocardiography

Leclerc

Smistad²,

Pedrosa³

et al. 2019

IEEE Trans. Med. Imaging

405

306

View full text Add to dashboard Cite

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TC-SegNet: robust deep learning network for fully automatic two-chamber segmentation of two-dimensional echocardiography

Lal

2023

Multimed Tools Appl

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Heart chamber quantification is an essential clinical task to analyze heart abnormalities by evaluating the heart volume estimated through the endocardial border of the chambers. A precise heart chamber segmentation algorithm using echocardiography is essential for improving the diagnosis of cardiac disease. This paper proposes a robust two chamber segmentation network (TC-SegNet) for echocardiography which follows a U-Net architecture and effectively incorporates the proposed modified skip connection, Atrous Spatial Pyramid Pooling (ASPP) modules and squeeze and excitation modules. The TC-SegNet is evaluated on the open-source fully annotated dataset of cardiac acquisitions for multi-structure ultrasound segmentation (CAMUS). The proposed TC-SegNet obtained an average value of F1-score of 0.91, an average Dice score of 0.9284 and an IoU score of 0.8322 which are higher than the reference models used here for comparison. Further, Pixel error (PE) of 1.5109 which are significantly less than the comparison models. The segmentation results and metrics show that the proposed model outperforms the state-of-the-art segmentation methods.

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

Bernard¹,

Bosch²,

Heyde³

et al. 2016

IEEE Trans. Med. Imaging

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Real-time 3D Echocardiography (RT3DE) has been proven to be an accurate tool for left ventricular (LV) volume assessment. However, identification of the LV endocardium remains a challenging task, mainly because of the low tissue/blood contrast of the images combined with typical artifacts. Several semi and fully automatic algorithms have been proposed for segmenting the endocardium in RT3DE data in order to extract relevant clinical indices, but a systematic and fair comparison between such methods has so far been impossible due to the lack of a publicly available common database. Here, we introduce a standardized evaluation framework to reliably evaluate and compare the performance of the algorithms developed to segment the LV border in RT3DE. A database consisting of 45 multivendor cardiac ultrasound recordings acquired at different centers with corresponding reference measurements from three experts are made available. The algorithms from nine research groups were quantitatively evaluated and compared using the proposed online platform. The results showed that the best methods produce promising results with respect to the experts' measurements for the extraction of clinical indices, and that they offer good segmentation precision in terms of mean distance error in the context of the experts' variability range. The platform remains open for new submissions.

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Segmentation of Multi-Center 3D Left Ventricular Echocardiograms by Active Appearance Models

Cited by 4 publications

References 10 publications

Deep Learning for Segmentation Using an Open Large-Scale Dataset in 2D Echocardiography

Deep Learning for Segmentation Using an Open Large-Scale Dataset in 2D Echocardiography

TC-SegNet: robust deep learning network for fully automatic two-chamber segmentation of two-dimensional echocardiography

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

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