Left Ventricle Quantification Through Spatio-Temporal CNNs

Debus, Alejandro; Ferrante, Enzo

doi:10.1007/978-3-030-12029-0_50

Cited by 6 publications

(4 citation statements)

References 12 publications

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“…CNN3DST [59] proposed a CNN that consisted of an encoder-CNN (Fig. 3(a)) for feature extraction and a spatialtemporal CNN for temporal dynamics modeling and fusion of spatial and temporal information.…”

Section: B Dr Methodsmentioning

confidence: 99%

Left Ventricle Quantification Challenge: A Comprehensive Comparison and Evaluation of Segmentation and Regression for Mid-Ventricular Short-Axis Cardiac MR Data

Xue

Li²,

Hu³

et al. 2021

IEEE J. Biomed. Health Inform.

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Automatic quantification of the left ventricle (LV) from cardiac magnetic resonance (CMR) images plays an important role in making the diagnosis procedure efficient, reliable, and alleviating the laborious reading work for physicians. Considerable efforts have been devoted to LV quantification using different strategies that include segmentation-based (SG) methods and the recent direct regression (DR) methods. Although both SG and DR methods have obtained great success for the task, a systematic platform to benchmark them remains absent because of differences in label information during model learning.In this paper, we conducted an unbiased evaluation and comparison of cardiac LV quantification methods that were submitted to the Left Ventricle Quantification (LVQuan) challenge, which was held in conjunction with the Statistical Atlases and Computational Modeling of the Heart (STACOM) workshop at the MICCAI 2018. The challenge was targeted at the quantification

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Section: B Dr Methodsmentioning

confidence: 99%

Left Ventricle Quantification Challenge: A Comprehensive Comparison and Evaluation of Segmentation and Regression for Mid-Ventricular Short-Axis Cardiac MR Data

Xue

Li²,

Hu³

et al. 2021

IEEE J. Biomed. Health Inform.

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“…As we know, manual segmentation is very time-consuming, subjective, and not very efficient. To tackle these limitations, several automatic segmentation algorithms [21], [22], [23], [24] have been proposed. Wang et al [25] considered a Bayesian method for twoventricular volume estimation that used a likelihood function for exploiting appearance features and a probability model to incorporate the area correlation between the cavities.…”

Section: Related Workmentioning

confidence: 99%

“…However, this method only computes the LV indices and not the cardiac phase cycle. Another study [22] processes stacks of adjacent slices(k = 5) using 3D convolutional kernels to incorporate temporal information within the learned model. Tao et al [12] in a more clinically adapted approach tested three different CNNs for fully automated quantification of LV on multi-centers and multi-vendors study.…”

Section: Related Workmentioning

confidence: 99%

Spatio-Temporal Multi-Task Learning for Cardiac MRI Left Ventricle Quantification

Vesal

Maier

et al. 2021

IEEE J. Biomed. Health Inform.

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Quantitative assessment of cardiac left ventricle (LV) morphology is essential to assess cardiac function and improve the diagnosis of different cardiovascular diseases. In current clinical practice, LV quantification depends on the measurement of myocardial shape indices, which is usually achieved by manual contouring of the endo-and epicardial. However, this process subjected to inter and intra-observer variability, and it is a time-consuming and tedious task. In this paper, we propose a spatio-temporal multi-task learning approach to obtain a complete set of measurements quantifying cardiac LV morphology, regional-wall thickness (RWT), and additionally detecting the cardiac phase cycle (systole and diastole) for a given 3D Cine-magnetic resonance (MR) image sequence. We first segment cardiac LVs using an encoder-decoder network and then introduce a multitask framework to regress 11 LV indices and classify the cardiac phase, as parallel tasks during model optimization. The proposed deep learning model is based on the 3D spatio-temporal convolutions, which extract spatial and temporal features from MR images. We demonstrate the efficacy of the proposed method using cine-MR sequences of 145 subjects and comparing the performance with other state-ofthe-art quantification methods. The proposed method obtained high prediction accuracy, with an average mean absolute error (MAE) of 129 mm 2 , 1.23 mm, 1.76 mm, Pearson correlation coefficient (PCC) of 96.4%, 87.2%, and 97.5% for LV and myocardium (Myo) cavity regions, 6 RWTs, 3 LV dimensions, and an error rate of 9.0% for phase classification. The experimental results highlight the robustness of the proposed method, despite varying degrees of cardiac morphology, image appearance, and low contrast in the cardiac MR sequences.

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“…For instance, Xu et al directly estimate the cardiac cavity, the myocardial area, and the regional wall thickness in cine-MRI sequences [11,10]. Debus and Ferrante make use of a 3D spatio-temporal CNN for regressing left ventricle volumes from MRI scans [2]. More recently, Zhang et al proposed a model for the direct estimation of coronary artery diameter in X-ray angiograhpy images [12].…”

Section: Introductionmentioning

confidence: 99%

Segmentation-free Estimation of Aortic Diameters from MRI Using Deep Learning

Aguerreberry¹,

Rosa²,

Lalande³

et al. 2020

Preprint

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Accurate and reproducible measurements of the aortic diameters are crucial for the diagnosis of cardiovascular diseases and for therapeutic decision making. Currently, these measurements are manually performed by healthcare professionals, being time consuming, highly variable, and suffering from lack of reproducibility. In this work we propose a supervised deep-learning method for the direct estimation of aortic diameters. The approach is devised and tested over 100 magnetic resonance angiography scans without contrast agent. All data was expertannotated at six aortic locations typically used in clinical practice. Our approach makes use of a 3D+2D convolutional neural network (CNN) that takes as input a 3D scan and outputs the aortic diameter at a given location. In a 5-fold cross-validation comparison against a fully 3D CNN and against a 3D multiresolution CNN, our approach was consistently superior in predicting the aortic diameters. Overall, the 3D+2D CNN achieved a mean absolute error between 2.2-2.4 mm depending on the considered aortic location. These errors are less than 1 mm higher than the inter-observer variability. Thus, suggesting that our method makes predictions almost reaching the expert's performance. We conclude that the work allows to further explore automatic algorithms for direct estimation of anatomical structures without the necessity of a segmentation step. It also opens possibilities for the automation of cardiovascular measurements in clinical settings.

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Left Ventricle Quantification Through Spatio-Temporal CNNs

Cited by 6 publications

References 12 publications

Left Ventricle Quantification Challenge: A Comprehensive Comparison and Evaluation of Segmentation and Regression for Mid-Ventricular Short-Axis Cardiac MR Data

Left Ventricle Quantification Challenge: A Comprehensive Comparison and Evaluation of Segmentation and Regression for Mid-Ventricular Short-Axis Cardiac MR Data

Spatio-Temporal Multi-Task Learning for Cardiac MRI Left Ventricle Quantification

Segmentation-free Estimation of Aortic Diameters from MRI Using Deep Learning

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