Automatic lung tumor segmentation from CT images using improved 3D densely connected UNet

Zhang, Guobin; Yang, Zhiyong; Jiang, Shan

doi:10.1007/s11517-022-02667-0

Cited by 16 publications

(7 citation statements)

References 36 publications

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“…Private datasets: Some studies have utilized private datasets, the sources of which are not publicly disclosed, to evaluate the performance of deep learning techniques [ 70 ].…”

Section: Dataset Discussionmentioning

confidence: 99%

A Review of Deep Learning Techniques for Lung Cancer Screening and Diagnosis Based on CT Images

et al. 2023

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One of the most common and deadly diseases in the world is lung cancer. Only early identification of lung cancer can increase a patient’s probability of survival. A frequently used modality for the screening and diagnosis of lung cancer is computed tomography (CT) imaging, which provides a detailed scan of the lung. In line with the advancement of computer-assisted systems, deep learning techniques have been extensively explored to help in interpreting the CT images for lung cancer identification. Hence, the goal of this review is to provide a detailed review of the deep learning techniques that were developed for screening and diagnosing lung cancer. This review covers an overview of deep learning (DL) techniques, the suggested DL techniques for lung cancer applications, and the novelties of the reviewed methods. This review focuses on two main methodologies of deep learning in screening and diagnosing lung cancer, which are classification and segmentation methodologies. The advantages and shortcomings of current deep learning models will also be discussed. The resultant analysis demonstrates that there is a significant potential for deep learning methods to provide precise and effective computer-assisted lung cancer screening and diagnosis using CT scans. At the end of this review, a list of potential future works regarding improving the application of deep learning is provided to spearhead the advancement of computer-assisted lung cancer diagnosis systems.

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“…Private datasets: Some studies have utilized private datasets, the sources of which are not publicly disclosed, to evaluate the performance of deep learning techniques [ 70 ].…”

Section: Dataset Discussionmentioning

confidence: 99%

A Review of Deep Learning Techniques for Lung Cancer Screening and Diagnosis Based on CT Images

et al. 2023

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“…Table 3 shows that the segmentation performance of the proposed method is better. [21] 0.921 -Gan et al [22] 0.720 0.420 Wang et al [23] 0.750 -Jiang et al [24] 0.710 -Zhang et al [25] 0.831 -Cui et al [26] 0.881 -Anshad et al [27] 0.912 0.161 Ours 0.936 0.114…”

Section: Discussionmentioning

confidence: 99%

“…Afshar et al [16] Snake algorithm + Gustafson-Kessel clustering Unpublished Semi-automatic Dlamini et al [21] Region-based active contour LICD-IDRI Automatic Gan et al [22] Hybrid convolution neural network Unpublished Automatic Wang et al [23] Deep learning Unpublished Automatic Jiang et al [24] Deep learning LICD-IDRI + NSCLC Automatic Zhang et al [25] Deep learning LICD-IDRI Automatic Cui et al [26] Topo-poly graph model NSCLC Semi-automatic Anshad et al [27] Modified region growing algorithm Unpublished Semi-automatic…”

Section: Methods Dataset Automationmentioning

confidence: 99%

“…Jiang et al [ 24 ] proposed a multi-scale CNN method, which is composed of incremental multiple resolution residually connected network formula and dense multiple resolutions residually connected network formula and can automatically recognize and segment lung tumors from CT images. Zhang et al [ 25 ] proposed an improved 3D dense connected UNet, which can segment lung tumors of an arbitrary shape from CT images. Cui et al [ 26 ] proposed a segmentation method based on a ‘topo-poly’ graph, which is composed of an intensity graph and a topology graph and can accurately segment lung tumors.…”

Section: Related Workmentioning

confidence: 99%

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An Automatic Segmentation Method for Lung Tumor Based on Improved Region Growing Algorithm

Wang

2022

Diagnostics

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In medical image processing, accurate segmentation of lung tumors is very important. Computer-aided accurate segmentation can effectively assist doctors in surgery planning and treatment decisions. Although the accurate segmentation results of lung tumors can provide a reliable basis for clinical treatment, the key to obtaining accurate segmentation results is how to improve the segmentation performance of the algorithm. We propose an automatic segmentation method for lung tumors based on an improved region growing algorithm, which uses the prior information on lung tumors to achieve an automatic selection of the initial seed point. The proposed method includes a seed point expansion mechanism and an automatic threshold update mechanism and takes the combination of multiple segmentation results as the final segmentation result. In the lung image database consortium (LIDC-IDRI) dataset, we designed 10 experiments to test the proposed method and compare it with 4 popular segmentation methods. The experimental results show that the average dice coefficient obtained by the proposed method is 0.936 ± 0.027, and the average Jaccard distance is 0.114 ± 0.049. The average dice coefficient obtained by the proposed method is 0.107, 0.053, 0.040, and 0.156, higher than that of the other four methods, respectively. This study proves that the proposed method can automatically segment lung tumors in CT slices and has suitable segmentation performance.

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“…However, the lung nodules and the tissues such as blood vessels in lung tumor CT images are usually misclassified as tumors (Byun et al 2020). In particular, when a tumor is adhered to the surrounding tissue including the chest wall and the mediastinum, the contrast between the tumor and the tissue is low and they have similar gray-level distributions (Zhang et al 2022). It results in the unclear boundaries between them and makes the lung tumor segmentation more challenging.…”

Section: Introductionmentioning

confidence: 99%

Mutually enhanced multi-view information learning for segmentation of lung tumor in CT images

Xuan,

Xu,

Cui

et al. 2024

Phys. Med. Biol.

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The accurate automatic segmentation of tumors from computed tomography (CT) volumes facilitates early diagnosis and treatment of patients. A significant challenge in tumor segmentation is the integration of the spatial correlations among multiple parts of the CT volume and the context relationship across multiple channels. We proposed a mutually enhanced multi-view information model (MEMI) to propagate and fuse the spatial correlations and the context relationship and then apply it to lung tumor segmentation from CT volumes. First, an attention mechanism from the region node perspective was presented to determine the impact of all the other nodes on a specific node, which aims to enhance the node attribute embedding. A gated convolution-based strategy was also designed to integrate the enhanced attributes and the original node features. Second, transformer across multiple channels was constructed to learn the context relationship between these channels and to fuse the information across the channels. Third, since the encoded node attributes from the gated convolution view and those from the channel transformer view were complementary, an interaction attention mechanism was proposed to propagate and fuse the mutual information from multiple views. Finally, the node embeddings with the spatial correlations, the channel context embeddings, and the original features of region nodes were adaptively integrated before sending through the segmentation decoder for final output. The segmentation performance was evaluated on both public lung tumor dataset and private dataset collected from a hospital. The experimental results demonstrated that MEMI was superior to other compared segmentation methods. Ablation studies showed the contributions of node correlation learning, channel context relationship learning, and mutual information interaction across multiple views to the improved segmentation performance. The experimental results of using MEMI on multiple segmentation backbones also demonstrated MEMI’s generalization ability.

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Automatic lung tumor segmentation from CT images using improved 3D densely connected UNet

Cited by 16 publications

References 36 publications

A Review of Deep Learning Techniques for Lung Cancer Screening and Diagnosis Based on CT Images

A Review of Deep Learning Techniques for Lung Cancer Screening and Diagnosis Based on CT Images

An Automatic Segmentation Method for Lung Tumor Based on Improved Region Growing Algorithm

Mutually enhanced multi-view information learning for segmentation of lung tumor in CT images

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