Versatile approximation of the lung field boundaries in chest radiographs in the presence of bacterial pulmonary infections

Iakovidis, Dimitris K.

doi:10.1109/bibe.2008.4696844

Cited by 3 publications

(1 citation statement)

References 19 publications

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“…However, high-resolution (HR) images are not often available without expensive dedicated devices. The task of image segmentation is difficult with respect to the low resolution of X-ray chest radiography and serious tissue interference [7,8]. Existing research can be divided into three stages.…”

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confidence: 99%

An Adaptive Generative Adversarial Network for Cardiac Segmentation from X-ray Chest Radiographs

Tian

2020

Applied Sciences

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Medical image segmentation is a classic challenging problem. The segmentation of parts of interest in cardiac medical images is a basic task for cardiac image diagnosis and guided surgery. The effectiveness of cardiac segmentation directly affects subsequent medical applications. Generative adversarial networks have achieved outstanding success in image segmentation compared with classic neural networks by solving the oversegmentation problem. Cardiac X-ray images are prone to weak edges, artifacts, etc. This paper proposes an adaptive generative adversarial network for cardiac segmentation to improve the segmentation rate of X-ray images by generative adversarial networks. The adaptive generative adversarial network consists of three parts: a feature extractor, a discriminator and a selector. In this method, multiple generators are trained in the feature extractor. The discriminator scores the features of different dimensions. The selector selects the appropriate features and adjusts the network for the next iteration. With the help of the discriminator, this method uses multinetwork joint feature extraction to achieve network adaptivity. This method allows features of multiple dimensions to be combined to perform joint training of the network to enhance its generalization ability. The results of cardiac segmentation experiments on X-ray chest radiographs show that this method has higher segmentation accuracy and less overfitting than other methods. In addition, the proposed network is more stable.

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confidence: 99%