Automatic segmentation of left ventricle from cardiac MRI via deep learning and region constrained dynamic programming

Hu, Huaifei; Pan, Ning; Wang, Jiayu; Yin, Tailang; Ye, Renzhen

doi:10.1016/j.neucom.2019.02.008

Cited by 43 publications

(24 citation statements)

References 30 publications

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“…It is important to notice that even though some approaches use methods from diferent categories for the ROI extraction and the segmentation stages, they are only classiied as hybrid if the combination is used in the segmentation stage. For example, in [39], although DL is used alongside IB methods, since it is applied to only extract the ROI through an initial contour, this study is not classiied as hybrid.…”

Section: Processing Stagesmentioning

confidence: 99%

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Left Ventricle Segmentation in Cardiac MR: A Systematic Mapping of the Past Decade

Ribeiro

Nunes

2022

ACM Comput. Surv.

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Left ventricle segmentation in short-axis cardiac magnetic resonance images is important to diagnose heart disease. However, the repetitive manual segmentation of these images requires considerable human effort and can decrease diagnostic accuracy. In recent years, several fully and semi-automatic approaches have been proposed, mainly using image-based, atlas, graphs, deformable models, and artificial intelligence methods. This paper presents a systematic mapping on the left ventricle segmentation, considering 74 studies published in the last decade. The main contributions of this review are: definition of the main segmentation challenges in these images; proposal of a new schematization, dividing the segmentation process into stages; categorization and analysis of the segmentation methods, including hybrid combinations; and analysis of the evaluation process, metrics, and databases. The performance of the methods in the most used public database is assessed, and the main limitations, weaknesses, and strengths of each method category are presented. Finally, trends, challenges, and research opportunities are discussed. The analysis indicates that methods from all categories can achieve good performance, and hybrid methods combining deep learning and deformable models obtain the best results. Methods still fail in specific slices, segment wrong regions, and produce anatomically impossible segmentations.

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Section: Processing Stagesmentioning

confidence: 99%

“…Regression is also used to obtain the center of the LV [98]. The model can also be trained to classify possible sub-images [56] or to classify pixels that belong or not to the object of interest, in which the sub-image is obtained around the classiied region [39,100,128].…”

Section: Automatic Roi Extractionmentioning

confidence: 99%

Left Ventricle Segmentation in Cardiac MR: A Systematic Mapping of the Past Decade

Ribeiro

Nunes

2022

ACM Comput. Surv.

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“…Baumgartner et al [6] used 2D U-Net to segment the CMR data, they processed the 3D data in a slice-by-slice fashion, and there is no correlation between each 2D slice, as the result this method lose the spatial structure information in the original data. In general, 2D medical image segmentation methods [7], [8] have the problem of losing spatial information.…”

Section: A 2d Convolution On Planesmentioning

confidence: 99%

Residual Convolutional Neural Network for Cardiac Image Segmentation and Heart Disease Diagnosis

et al. 2020

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Deep learning (DL) has been widely used in biomedical image segmentation and automatic disease diagnosis, leading to state-of-the-art performance. However, automated cardiac disease diagnosis heavily relies on cardiac segmentation maps from cardiac magnetic resonance (CMR), most current DL segmentation methods, such as 2D convolution on planes, 3D convolution, are not fully applicable to CMR due to loss of spatial structure information or large gap between slices. To make better exploit spatial aspects of the CMR data to improve cardiac segmentation accuracy, we propose a new DL segmentation structure, which consists of a residual convolution neural network for compressing the intra-slice information, and a bidirectional-convolutional long short term memory (Bi-CLSTM) for leveraging the inter-slice contexts. Moreover, automatic disease diagnosis has been conducted using the segmentation maps. Experimental results of the automatic cardiac diagnosis challenge (ACDC) show that our cardiac segmentation structure and disease diagnosis methods have achieved promising results and it can be widely extended to computer-aided diagnosis. INDEX TERMS Image segmentation, cardiac disease diagnosis, deep learning.

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“…Convolutional neural networks (CNNs), a standard deep learning-based method, have recently achieved excellent results in various computer vision fields, including object detection [ 5 ], image classification [ 6 ], and image segmentation [ 7 ]. Following this trend, several CNN-based techniques for LV segmentation have been proposed [ 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 ] and have shown promising results in clinical practice. However, accurate segmentation of the LV and myocardium from cardiac MRI remains a challenge in clinical practice for several reasons, including changes in the LV morphology across slices, an imbalance in pixels between the LV area and the background, and incorrect pixel representation for the target area.…”

Section: Introductionmentioning

confidence: 99%

Automatic Left Ventricle Segmentation from Short-Axis Cardiac MRI Images Based on Fully Convolutional Neural Network

et al. 2022

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Background: Left ventricle (LV) segmentation using a cardiac magnetic resonance imaging (MRI) dataset is critical for evaluating global and regional cardiac functions and diagnosing cardiovascular diseases. LV clinical metrics such as LV volume, LV mass and ejection fraction (EF) are frequently extracted based on the LV segmentation from short-axis MRI images. Manual segmentation to assess such functions is tedious and time-consuming for medical experts to diagnose cardiac pathologies. Therefore, a fully automated LV segmentation technique is required to assist medical experts in working more efficiently. Method: This paper proposes a fully convolutional network (FCN) architecture for automatic LV segmentation from short-axis MRI images. Several experiments were conducted in the training phase to compare the performance of the network and the U-Net model with various hyper-parameters, including optimization algorithms, epochs, learning rate, and mini-batch size. In addition, a class weighting method was introduced to avoid having a high imbalance of pixels in the classes of image’s labels since the number of background pixels was significantly higher than the number of LV and myocardium pixels. Furthermore, effective image conversion with pixel normalization was applied to obtain exact features representing target organs (LV and myocardium). The segmentation models were trained and tested on a public dataset, namely the evaluation of myocardial infarction from the delayed-enhancement cardiac MRI (EMIDEC) dataset. Results: The dice metric, Jaccard index, sensitivity, and specificity were used to evaluate the network’s performance, with values of 0.93, 0.87, 0.98, and 0.94, respectively. Based on the experimental results, the proposed network outperforms the standard U-Net model and is an advanced fully automated method in terms of segmentation performance. Conclusion: This proposed method is applicable in clinical practice for doctors to diagnose cardiac diseases from short-axis MRI images.

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Automatic segmentation of left ventricle from cardiac MRI via deep learning and region constrained dynamic programming

Cited by 43 publications

References 30 publications

Left Ventricle Segmentation in Cardiac MR: A Systematic Mapping of the Past Decade

Left Ventricle Segmentation in Cardiac MR: A Systematic Mapping of the Past Decade

Residual Convolutional Neural Network for Cardiac Image Segmentation and Heart Disease Diagnosis

Automatic Left Ventricle Segmentation from Short-Axis Cardiac MRI Images Based on Fully Convolutional Neural Network

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