Deep Belief Network and Closed Polygonal Line for Lung Segmentation in Chest Radiographs

Peng, Tao; Xu, Thomas Canhao; Wang, Yihuai; Li, Fanzhang

doi:10.1093/comjnl/bxaa148

Cited by 25 publications

(14 citation statements)

References 52 publications

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“…However, these approaches were not trained in an end to end manner. In addition, Peng et al 39 proposed a twostage approach for the lung segmentation, where in the first step a Deep Belief Network and K-Nearest Neighbor is utilized to segment the lungs and then and improved principal curve and machine learning method is used for the refinement of the segmentation result. However, these types of techniques are computationally costly and necessitate a post-processing step to improve the output.…”

Section: Deep Learning Methodsmentioning

confidence: 99%

A deep learning based dual encoder–decoder framework for anatomical structure segmentation in chest X-ray images

Ullah

Ali

Shah

et al. 2023

Sci Rep

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Automated multi-organ segmentation plays an essential part in the computer-aided diagnostic (CAD) of chest X-ray fluoroscopy. However, developing a CAD system for the anatomical structure segmentation remains challenging due to several indistinct structures, variations in the anatomical structure shape among different individuals, the presence of medical tools, such as pacemakers and catheters, and various artifacts in the chest radiographic images. In this paper, we propose a robust deep learning segmentation framework for the anatomical structure in chest radiographs that utilizes a dual encoder–decoder convolutional neural network (CNN). The first network in the dual encoder–decoder structure effectively utilizes a pre-trained VGG19 as an encoder for the segmentation task. The pre-trained encoder output is fed into the squeeze-and-excitation (SE) to boost the network’s representation power, which enables it to perform dynamic channel-wise feature calibrations. The calibrated features are efficiently passed into the first decoder to generate the mask. We integrated the generated mask with the input image and passed it through a second encoder–decoder network with the recurrent residual blocks and an attention the gate module to capture the additional contextual features and improve the segmentation of the smaller regions. Three public chest X-ray datasets are used to evaluate the proposed method for multi-organs segmentation, such as the heart, lungs, and clavicles, and single-organ segmentation, which include only lungs. The results from the experiment show that our proposed technique outperformed the existing multi-class and single-class segmentation methods.

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Section: Deep Learning Methodsmentioning

confidence: 99%

A deep learning based dual encoder–decoder framework for anatomical structure segmentation in chest X-ray images

Ullah

Ali

Shah

et al. 2023

Sci Rep

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“…Additionally, we used 704 chest X-ray images from the Montgomery County Chest X-ray database [ 43 , 44 ] as ground truth for lung field segmentation, and 247 chest X-ray images from JSRT [ 45 , 46 ] as those for the heart. Several lung segmentation studies using these databases have been reported [ 47 – 49 ]. These images were resized to 224 × 224 to input the classification network.…”

Section: Methodsmentioning

confidence: 99%

Classification of chest X-ray images by incorporation of medical domain knowledge into operation branch networks

et al. 2023

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Background This study was conducted to alleviate a common difficulty in chest X-ray image diagnosis: The attention region in a convolutional neural network (CNN) does not often match the doctor’s point of focus. The method presented herein, which guides the area of attention in CNN to a medically plausible region, can thereby improve diagnostic capabilities. Methods The model is based on an attention branch network, which has excellent interpretability of the classification model. This model has an additional new operation branch that guides the attention region to the lung field and heart in chest X-ray images. We also used three chest X-ray image datasets (Teikyo, Tokushima, and ChestX-ray14) to evaluate the CNN attention area of interest in these fields. Additionally, after devising a quantitative method of evaluating improvement of a CNN’s region of interest, we applied it to evaluation of the proposed model. Results Operation branch networks maintain or improve the area under the curve to a greater degree than conventional CNNs do. Furthermore, the network better emphasizes reasonable anatomical parts in chest X-ray images. Conclusions The proposed network better emphasizes the reasonable anatomical parts in chest X-ray images. This method can enhance capabilities for image interpretation based on judgment.

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“…Based on our previous research ( Peng et al, 2018b ), we set 10 hidden neurons and 1,000 epochs for the FBNNL to simplify the complexity of the NN model and prevent overfitting. We used the DSC, Jaccard similarity coefficient (OMG), and accuracy (ACC) ( Peng et al, 2020 ) as the evaluation metrics. All of the ground truths were labeled and verified by three physicians.…”

Section: Methodsmentioning

confidence: 99%

Intelligent contour extraction approach for accurate segmentation of medical ultrasound images

Peng,

Wu,

et al. 2023

Front. Physiol.

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Introduction: Accurate contour extraction in ultrasound images is of great interest for image-guided organ interventions and disease diagnosis. Nevertheless, it remains a problematic issue owing to the missing or ambiguous outline between organs (i.e., prostate and kidney) and surrounding tissues, the appearance of shadow artifacts, and the large variability in the shape of organs.Methods: To address these issues, we devised a method that includes four stages. In the first stage, the data sequence is acquired using an improved adaptive selection principal curve method, in which a limited number of radiologist defined data points are adopted as the prior. The second stage then uses an enhanced quantum evolution network to help acquire the optimal neural network. The third stage involves increasing the precision of the experimental outcomes after training the neural network, while using the data sequence as the input. In the final stage, the contour is smoothed using an explicable mathematical formula explained by the model parameters of the neural network.Results: Our experiments showed that our approach outperformed other current methods, including hybrid and Transformer-based deep-learning methods, achieving an average Dice similarity coefficient, Jaccard similarity coefficient, and accuracy of 95.7 ± 2.4%, 94.6 ± 2.6%, and 95.3 ± 2.6%, respectively.Discussion: This work develops an intelligent contour extraction approach on ultrasound images. Our approach obtained more satisfactory outcome compared with recent state-of-the-art approaches . The knowledge of precise boundaries of the organ is significant for the conservation of risk structures. Our developed approach has the potential to enhance disease diagnosis and therapeutic outcomes.

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Deep Belief Network and Closed Polygonal Line for Lung Segmentation in Chest Radiographs

Cited by 25 publications

References 52 publications

A deep learning based dual encoder–decoder framework for anatomical structure segmentation in chest X-ray images

A deep learning based dual encoder–decoder framework for anatomical structure segmentation in chest X-ray images

Classification of chest X-ray images by incorporation of medical domain knowledge into operation branch networks

Intelligent contour extraction approach for accurate segmentation of medical ultrasound images

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