Automatic Cardiothoracic Ratio Calculation With Deep Learning

Li, Zhennan; Hou, Zhiyong; Chen, Chen; Hao, Zhi Feng; An, Yunqiang; Lü, Bin

doi:10.1109/access.2019.2900053

Cited by 51 publications

(64 citation statements)

References 20 publications

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“…Que et al [Que et al 2018] used the NIH ChestX-Ray14 dataset yielded an AUC of 0.935 on the classification task. Li et al [Li et al 2019] used images from a local repository for the experiments and obtained an accuracy on a testing set of 0.953. Other works, like the proposed by Candemir et al [Candemir et al 2018] using a CNN model and combined the NIH ChestX-Ray14 for training and OpenI for both training and testing, achieved an AUC of 0.95.…”

Section: Discussionmentioning

confidence: 99%

“…An abnormal heart can be evaluated with a medical imaging exam, like a chest radiograph (Figure 1). The clinical evaluation of cardiomegaly is based on the calculation of the cardiothoracic ratio (CTR), a widely used radiographic index to assess cardiac size and provide prognostic information in both congenital and acquired heart diseases [Li et al 2019]. The CTR on a chest x-ray (CXR) image is basically the relationship between cardiac and thoracic diameters.…”

Section: Introductionmentioning

confidence: 99%

“…Que et al also developed an algorithm based on the U-Net to measure the CTR from CXRs and used the NIH ChestX-Ray14 dataset for experiments [Que et al 2018]. Li et al proposed a customized U-Net model to segment the cardiac region on the CXR for the CTR calculation and the heart enlargement [Li et al 2019]. The authors used images from a local repository for the experiments.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Multicenter Validation of Convolutional Neural Networks for Automated Detection of Cardiomegaly on Chest Radiographs

Cárdenas¹,

Ferreira²,

Moreno³

et al. 2020

Anais Principais Do Simpósio Brasileiro De Computação Aplicada À Saúde (SBCAS 2020)

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This work focused on validating five convolutional neural network models to detect automatically cardiomegaly, a health complication that causes heart enlargement, which may lead to cardiac arrest. To do that, we trained the models with a customized multilayer perceptron. Radiographs from two public datasets were used in experiments, one of them only for external validation. Images were pre-processed to contain just the chest cavity. The EfficientNet model yielded the highest area under the curve (AUC) of 0.91 on the test set. However, the Inception-based model obtained the best generalization performance with AUC of 0.88 on the independent multicentric dataset. Therefore, this work accurately validated radiographic models to identify patients with cardiomegaly.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multicenter Validation of Convolutional Neural Networks for Automated Detection of Cardiomegaly on Chest Radiographs

Cárdenas¹,

Ferreira²,

Moreno³

et al. 2020

Anais Principais Do Simpósio Brasileiro De Computação Aplicada À Saúde (SBCAS 2020)

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“…One of the best current methods for computer vision (CV) tasks are the convolutional neural networks (CNN) [7]. A lot of techniques have been used in the medical field to assist CAD tasks, such as lesion segmentation [8], brain tumor segmentation [9,10], automatic size calculation of the heart [11], and classification among several thorax diseases [12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Fast COVID-19 and Pneumonia Classification Using Chest X-ray Images

et al. 2020

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As of the end of 2019, the world suffered from a disease caused by the SARS-CoV-2 virus, which has become the pandemic COVID-19. This aggressive disease deteriorates the human respiratory system. Patients with COVID-19 can develop symptoms that belong to the common flu, pneumonia, and other respiratory diseases in the first four to ten days after they have been infected. As a result, it can cause misdiagnosis between patients with COVID-19 and typical pneumonia. Some deep-learning techniques can help physicians to obtain an effective pre-diagnosis. The content of this article consists of a deep-learning model, specifically a convolutional neural network with pre-trained weights, which allows us to use transfer learning to obtain new retrained models to classify COVID-19, pneumonia, and healthy patients. One of the main findings of this article is that the following relevant result was obtained in the dataset that we used for the experiments: all the patients infected with SARS-CoV-2 and all the patients infected with pneumonia were correctly classified. These results allow us to conclude that the proposed method in this article may be useful to help physicians decide the diagnoses related to COVID-19 and typical pneumonia.

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“…The latter allows to classify image pixels individually and eventually obtain the locations and boundaries of the objects within an image. DL-based image segmentation was shown to be a core technique in assessing CTR from chest X-rays [9,23]. However, none of the existing CTR assessment or chest X-ray segmentation methods allow to obtain the model uncertainty which is crucial in clinical practice.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Feature Pyramid Networks for Automatic Multi-label Segmentation of Chest X-rays and Assessment of Cardio-Thoratic Ratio

Solovyev

Melekhov

Lesonen

et al. 2020

Lecture Notes in Computer Science

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Cardiothoratic ratio (CTR) estimated from chest radiographs is a marker indicative of cardiomegaly, the presence of which is in the criteria for heart failure diagnosis. Existing methods for automatic assessment of CTR are driven by Deep Learning-based segmentation. However, these techniques produce only point estimates of CTR but clinical decision making typically assumes the uncertainty. In this paper, we propose a novel method for chest X-ray segmentation and CTR assessment in an automatic manner. In contrast to the previous art, we, for the first time, propose to estimate CTR with uncertainty bounds. Our method is based on Deep Convolutional Neural Network with Feature Pyramid Network (FPN) decoder. We propose two modifications of FPN: replace the batch normalization with instance normalization and inject the dropout which allows to obtain the Monte-Carlo estimates of the segmentation maps at test time. Finally, using the predicted segmentation mask samples, we estimate CTR with uncertainty. In our experiments we demonstrate that the proposed method generalizes well to three different test sets. Finally, we make the annotations produced by two radiologists for all our datasets publicly available.

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Automatic Cardiothoracic Ratio Calculation With Deep Learning

Cited by 51 publications

References 20 publications

Multicenter Validation of Convolutional Neural Networks for Automated Detection of Cardiomegaly on Chest Radiographs

Multicenter Validation of Convolutional Neural Networks for Automated Detection of Cardiomegaly on Chest Radiographs

Fast COVID-19 and Pneumonia Classification Using Chest X-ray Images

Bayesian Feature Pyramid Networks for Automatic Multi-label Segmentation of Chest X-rays and Assessment of Cardio-Thoratic Ratio

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