Pulmonary nodules segmentation method based on auto-encoder

Zhang, Guodong; Guo, Mao; Gong, Zhaoxuan; Bi, Jing; Kim, Yoohwan; Guo, Wei

doi:10.1117/12.2502835

Cited by 3 publications

(3 citation statements)

References 5 publications

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“…In this table, three parameters, sensitivity, specificity, and accuracy, are used for evaluation purposes. The quantitative results showed that the purposed technique outperformed the U-Net [37], AWEU-Net [38], 2D U-Net [9], 2D Seg U Det [39], 3D FCN [40], 3D nodule R-CNN [41], 2D AE [42], 2D CNN [43], 2D LGAN [44], and 2D encoder-decoder [45]. The accuracy of purposed method was 99%, which is much better than the other listed methods, as shown in Table 2.…”

Section: Image Segmentationmentioning

confidence: 91%

Efficient Pre-Processing and Segmentation for Lung Cancer Detection Using Fused CT Images

et al. 2021

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Over the last two decades, radiologists have been using multi-view images to detect tumors. Computer Tomography (CT) imaging is considered as one of the reliable imaging techniques. Many medical-image-processing techniques have been developed to diagnoses lung cancer at early or later stages through CT images; however, it is still a big challenge to improve the accuracy and sensitivity of the algorithms. In this paper, we propose an algorithm based on image fusion for lung segmentation to optimize lung cancer diagnosis. The image fusion technique was developed through Laplacian Pyramid (LP) decomposition along with Adaptive Sparse Representation (ASR). The suggested fusion technique fragments medical images into different sizes using the LP. After that, the LP is used to fuse the four decomposed layers. For the evaluation purposes of the proposed technique, the Lungs Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) was used. The results showed that the Dice Similarity Coefficient (DSC) index of our proposed method was 0.9929, which is better than recently published results. Furthermore, the values of other evaluation parameters such as the sensitivity, specificity, and accuracy were 89%, 98% and 99%, respectively, which are also competitive with the recently published results.

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Section: Image Segmentationmentioning

confidence: 91%

Efficient Pre-Processing and Segmentation for Lung Cancer Detection Using Fused CT Images

et al. 2021

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“…Therefore, there is a growing demand for segmentation of pulmonary nodules. 2 When using computed tomography to diagnose lung cancer, computer-aided diagnostic schemes would be useful for radiologists to distinguish between benign and malignant nodules. 3 Computer-aided diagnosis (CAD) systems typically involve the steps of segmentation, feature extraction, and classification.…”

Section: Introductionmentioning

confidence: 99%

“…Accordingly, the premise of knowing the exact growth rate for pulmonary nodules is to obtain the area or volume of the pulmonary nodules. Therefore, there is a growing demand for segmentation of pulmonary nodules 2 . When using computed tomography to diagnose lung cancer, computer‐aided diagnostic schemes would be useful for radiologists to distinguish between benign and malignant nodules 3 .…”

Section: Introductionmentioning

confidence: 99%

Segmentation of pulmonary nodules in CT images based on 3D‐UNET combined with three‐dimensional conditional random field optimization

Gao

Duan

et al. 2020

Medical Physics

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Purpose: Pulmonary nodules are a potential manifestation of lung cancer. In computer-aided diagnosis (CAD) of lung cancer, it is of great significance to extract the complete boundary of the pulmonary nodules in the computed tomography (CT) scans accurately. It can provide doctors with important information such as tumor size and density, which assist doctors in subsequent diagnosis and treatment. In addition to this, in the molecular subtype and radiomics of lung cancer, segmentation of lung nodules also plays a pivotal role. Existing methods are difficult to use only one model to simultaneously treat the boundaries of multiple types of lung nodules in CT images. Method: In order to solve the problem, this paper proposed a three-dimensional (3D)-UNET network model optimized by a 3D conditional random field (3D-CRF) to segment pulmonary nodules. On the basis of 3D-UNET, the 3D-CRF is used to optimize the sample output of the training set, so as to update the network weights in training process, reduce the model training time, and reduce the loss rate of the model. We selected 936 sets of pulmonary nodule data for the lung image database consortium and image database resource initiative (LIDC-IDRI) 1 database to train and test the model. What's more, we used clinical data from partner hospitals for additional validation. Results and conclusions: The results show that our method is accurate and effective. Particularly, it shows more significance for the optimization of the segmentation of adhesive pulmonary nodules (the juxta-pleural and juxta-vascular nodules) and ground glass pulmonary nodules (GGNs).

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A survey of computer-aided diagnosis of lung nodules from CT scans using deep learning

Chi

Liu

et al. 2021

Computers in Biology and Medicine

112

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Pulmonary nodules segmentation method based on auto-encoder

Cited by 3 publications

References 5 publications

Efficient Pre-Processing and Segmentation for Lung Cancer Detection Using Fused CT Images

Efficient Pre-Processing and Segmentation for Lung Cancer Detection Using Fused CT Images

Segmentation of pulmonary nodules in CT images based on 3D‐UNET combined with three‐dimensional conditional random field optimization

A survey of computer-aided diagnosis of lung nodules from CT scans using deep learning

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