A multi-level convolutional LSTM model for the segmentation of left ventricle myocardium in infarcted porcine cine MR images

Zhang, Dongqing; Icke, Ilknur; Dogdas, Belma; Parimal, Sarayu; Sampath, Smita; Forbes, Joseph; Bagchi, Ann D.; Chin, Chih‐Liang; Chen, Antong

doi:10.1109/isbi.2018.8363618

Cited by 22 publications

(13 citation statements)

References 17 publications

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“…Previously, Avendi et al used the CNN to automatically detect the left ventricle chamber in an MRI data set . In addition, Zhang et al combined recurrent neural network with convolutional LSTM for left‐ventricle myocardium segmentation . Xue et al introduced a spatial–temporal circle LSTM model to calculate left‐ventricle myocardial thickness in the short axis scan …”

Section: Discussionmentioning

confidence: 99%

Automated selection of myocardial inversion time with a convolutional neural network: Spatial temporal ensemble myocardium inversion network (STEMI‐NET)

Bahrami

Retson

Blansit

et al. 2019

Magnetic Resonance in Med

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Purpose Delayed enhancement imaging is an essential component of cardiac MRI, which is used widely for the evaluation of myocardial scar and viability. The selection of an optimal inversion time (TI) or null point (TINP) to suppress the background myocardial signal is required. The purpose of this study was to assess the feasibility of automated selection of TINP using a convolutional neural network (CNN). We hypothesized that a CNN may use spatial and temporal imaging characteristics from an inversion‐recovery scout to select TINP, without the aid of a human observer. Methods We retrospectively collected 425 clinically acquired cardiac MRI exams performed at 1.5 T that included inversion‐recovery scout acquisitions. We developed a VGG19 classifier ensembled with long short‐term memory to identify the TINP. We compared the performance of the ensemble CNN in predicting TINP against ground truth, using linear regression analysis. Ground truth was defined as the expert physician annotation of the optimal TI. In a backtrack approach, saliency maps were generated to interpret the classification outcome and to increase the model’s transparency. Results Prediction of TINP from our ensemble VGG19 long short‐term memory closely matched with expert annotation (ρ = 0.88). Ninety‐four percent of the predicted TINP were within ±36 ms, and 83% were at or after expert TI selection. Conclusion In this study, we show that a CNN is capable of automated prediction of myocardial TI from an inversion‐recovery experiment. Merging the spatial and temporal characteristics of the VGG‐19 and long short‐term‐memory CNN structures appears to be sufficient to predict myocardial TI from TI scout.

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Section: Discussionmentioning

confidence: 99%

Automated selection of myocardial inversion time with a convolutional neural network: Spatial temporal ensemble myocardium inversion network (STEMI‐NET)

Bahrami

Retson

Blansit

et al. 2019

Magnetic Resonance in Med

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“…Long short-term memory (LSTM) is a popular RNN [ 37 ] technique for detecting heart motion using spatiotemporal dynamics. Zhang et al [ 38 ] created a multi-level LSTM model for LV segmentation that used low-resolution level features to train one model and high-resolution level features to train another. Additionally, due to the large slice thickness, Baumgartner et al [ 39 ] found that segmentation by 2D CNN performed better than 3D CNN.…”

Section: Related Workmentioning

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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“…The combination of 2D and temporal information compensates the loss of the original data spatial structure of the 2D convolution network by using a time storage network such as long short-term memory (LSTM). Zhang et al [15] proposed a multi-level convolutional long short-term memory (ConvLSTM) model to the segmentation of left ventricle myocardium. LSTM is often used in time series data.…”

Section: E Combining 2d and Temporal Informationmentioning

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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A multi-level convolutional LSTM model for the segmentation of left ventricle myocardium in infarcted porcine cine MR images

Cited by 22 publications

References 17 publications

Automated selection of myocardial inversion time with a convolutional neural network: Spatial temporal ensemble myocardium inversion network (STEMI‐NET)

Automated selection of myocardial inversion time with a convolutional neural network: Spatial temporal ensemble myocardium inversion network (STEMI‐NET)

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

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

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