Multi‐Indices Quantification for Left Ventricle via DenseNet and GRU‐Based Encoder‐Decoder with Attention

Li, Zhi; Lu, Yunhua; Zhang, Xiaochuan; Wang, Sen; Li, Shuo; Chen, Bin

doi:10.1155/2021/3260259

Cited by 3 publications

(4 citation statements)

References 27 publications

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“…A decoder network then needs to be used to employ this internal representation to produce forecasts Bappy et al [5]. The described scheme is called an encoder-decoder network and was applied, for example, by Habler and Shabtai [24] together with LSTM, by Chen et al [10] with convolutional LSTM, and by Yang et al [60] with GRU.…”

Section: Related Work On Time Series Forecastingmentioning

confidence: 99%

Long short-term cognitive networks

Nápoles

Grau

Jastrzębska

et al. 2022

Neural Comput & Applic

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In this paper, we present a recurrent neural system named long short-term cognitive networks (LSTCNs) as a generalization of the short-term cognitive network (STCN) model. Such a generalization is motivated by the difficulty of forecasting very long time series efficiently. The LSTCN model can be defined as a collection of STCN blocks, each processing a specific time patch of the (multivariate) time series being modeled. In this neural ensemble, each block passes information to the subsequent one in the form of weight matrices representing the prior knowledge. As a second contribution, we propose a deterministic learning algorithm to compute the learnable weights while preserving the prior knowledge resulting from previous learning processes. As a third contribution, we introduce a feature influence score as a proxy to explain the forecasting process in multivariate time series. The simulations using three case studies show that our neural system reports small forecasting errors while being significantly faster than state-of-the-art recurrent models.

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Section: Related Work On Time Series Forecastingmentioning

confidence: 99%

Long short-term cognitive networks

Nápoles

Grau

Jastrzębska

et al. 2022

Neural Comput & Applic

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“…Direct regression of multiple LV parameters simultaneously using custom CNNs has been performed by Xue et al (2018) and Liu et al (2021). However, several studies have shown that LV parameter regression benefits from a joint segmentation (Xu et al (2019); Tilborghs and Maes (2020)).…”

Section: Introductionmentioning

confidence: 99%

Shape constrained CNN for segmentation guided prediction of myocardial shape and pose parameters in cardiac MRI

Tilborghs

Bogaert²,

Maes³

2022

Medical Image Analysis

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“…Since we address the segmentation problem in this paper as a regression problem predicting both shape and pose parameters in order to automatically extract global and regional shape properties, this work is closely related to literature focusing on LV parameter estimation in cardiac MRI. We distinguish three categories of automated methods for this task: (1) methods that extract the parameters from a prior segmentation (Acero et al (2020); Khened et al (2019); Zheng et al (2019)), (2) methods that perform direct parameter regression (Xue et al (2018); Liu et al (2021)) and (3) methods that perform both segmentation and parameter regression (Dangi et al (2019b); Yan et al (2019); Xue et al (2017); Gessert and Schlaefer (2020); Tilborghs and Maes (2020)). Khened et al (2019) extract global cardiac parameters from a segmentation of LV cavity, myocardium and right ventricle (RV) automatically generated with a CNN.…”

Section: Introductionmentioning

confidence: 99%

“…The pose-normalized segmentations then allowed to extract both global and regional LV parameters. Direct regression of multiple LV parameters simultaneously using custom CNNs has been performed by Xue et al (2018) and Liu et al (2021). However, several studies have shown that LV parameter regression benefits from a joint segmentation (Xu et al (2019); Tilborghs and Maes (2020)).…”

Section: Introductionmentioning

confidence: 99%

Shape constrained CNN for segmentation guided prediction of myocardial shape and pose parameters in cardiac MRI

Tilborghs,

Bogaert,

Maes

2022

Preprint

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Semantic segmentation using convolutional neural networks (CNNs) is the state-of-the-art for many medical image segmentation tasks including myocardial segmentation in cardiac MR images. However, the predicted segmentation maps obtained from such standard CNN do not allow direct quantification of regional shape properties such as regional wall thickness. Furthermore, the CNNs lack explicit shape constraints, occasionally resulting in unrealistic segmentations. In this paper, we use a CNN to predict shape parameters of an underlying statistical shape model of the myocardium learned from a training set of images. Additionally, the cardiac pose is predicted, which allows to reconstruct the myocardial contours. The integrated shape model regularizes the predicted contours and guarantees realistic shapes. We enforce robustness of shape and pose prediction by simultaneously performing pixel-wise semantic segmentation during training and define two loss functions to impose consistency between the two predicted representations: one distance-based loss and one overlap-based loss. We evaluated the proposed method in a 5-fold cross validation on an in-house clinical dataset with 75 subjects and on the ACDC and LVQuan19 public datasets. We show the benefits of simultaneous semantic segmentation and the two newly defined loss functions for the prediction of shape parameters. Our method achieved a correlation of 99% for left ventricular (LV) area on the three datasets, between 91% and 97% for myocardial area, 98-99% for LV dimensions and between 80% and 92% for regional wall thickness.

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Multi‐Indices Quantification for Left Ventricle via DenseNet and GRU‐Based Encoder‐Decoder with Attention

Cited by 3 publications

References 27 publications

Long short-term cognitive networks

Long short-term cognitive networks

Shape constrained CNN for segmentation guided prediction of myocardial shape and pose parameters in cardiac MRI

Shape constrained CNN for segmentation guided prediction of myocardial shape and pose parameters in cardiac MRI

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