LGAN: Lung segmentation in CT scans using generative adversarial network

Tan, James S.; Jing, Longlong; Huo, Yumei; Li, Lihong; Akın, Oğuz; Tian, Yingli

doi:10.1016/j.compmedimag.2020.101817

Cited by 58 publications

(33 citation statements)

References 11 publications

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“…Apart from various CNN models, GAN models have also been utilized for image segmentation. Tan et al (66) proposed a new schema called LGAN for lung segmentation based on the architecture of GAN. Meanwhile, a novel loss function based on the Earth Mover distance was used in their schema.…”

Section: Oars Segmentationmentioning

confidence: 99%

Review of Deep Learning Based Automatic Segmentation for Lung Cancer Radiotherapy

Liu

Yang

et al. 2021

Front. Oncol.

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Lung cancer is the leading cause of cancer-related mortality for males and females. Radiation therapy (RT) is one of the primary treatment modalities for lung cancer. While delivering the prescribed dose to tumor targets, it is essential to spare the tissues near the targets—the so-called organs-at-risk (OARs). An optimal RT planning benefits from the accurate segmentation of the gross tumor volume and surrounding OARs. Manual segmentation is a time-consuming and tedious task for radiation oncologists. Therefore, it is crucial to develop automatic image segmentation to relieve radiation oncologists of the tedious contouring work. Currently, the atlas-based automatic segmentation technique is commonly used in clinical routines. However, this technique depends heavily on the similarity between the atlas and the image segmented. With significant advances made in computer vision, deep learning as a part of artificial intelligence attracts increasing attention in medical image automatic segmentation. In this article, we reviewed deep learning based automatic segmentation techniques related to lung cancer and compared them with the atlas-based automatic segmentation technique. At present, the auto-segmentation of OARs with relatively large volume such as lung and heart etc. outperforms the organs with small volume such as esophagus. The average Dice similarity coefficient (DSC) of lung, heart and liver are over 0.9, and the best DSC of spinal cord reaches 0.9. However, the DSC of esophagus ranges between 0.71 and 0.87 with a ragged performance. In terms of the gross tumor volume, the average DSC is below 0.8. Although deep learning based automatic segmentation techniques indicate significant superiority in many aspects compared to manual segmentation, various issues still need to be solved. We discussed the potential issues in deep learning based automatic segmentation including low contrast, dataset size, consensus guidelines, and network design. Clinical limitations and future research directions of deep learning based automatic segmentation were discussed as well.

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Section: Oars Segmentationmentioning

confidence: 99%

Review of Deep Learning Based Automatic Segmentation for Lung Cancer Radiotherapy

Liu

Yang

et al. 2021

Front. Oncol.

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“…Deep learning has recently performed well in natural image segmentation [7], medical image segmentation [8], and video segmentation [9][10][11]. In medical imaging, deep learning not only excels in tissue and organ segmentation, such as pancreas segmentation [12][13][14], lung segmentation [15][16][17][18][19], and brain segmentation [20,21] but also exhibits superior performance in the segmentation of lesions, such as brain tumor segmentation [22][23][24], brain glioma segmentation [25], and microcalcification segmentation [26]. However, no studies regarding deep learning for rib segmentation have been reported.…”

Section: Introductionmentioning

confidence: 99%

MDU-Net: A Convolutional Network for Clavicle and Rib Segmentation from a Chest Radiograph

Wang

Feng

et al. 2020

Journal of Healthcare Engineering

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Automatic bone segmentation from a chest radiograph is an important and challenging task in medical image analysis. However, a chest radiograph contains numerous artifacts and tissue shadows, such as trachea, blood vessels, and lung veins, which limit the accuracy of traditional segmentation methods, such as thresholding and contour-related techniques. Deep learning has recently achieved excellent segmentation of some organs, such as the pancreas and the hippocampus. However, the insufficiency of annotated datasets impedes clavicle and rib segmentation from chest X-rays. We have constructed a dataset of chest X-rays with a raw chest radiograph and four annotated images showing the clavicles, anterior ribs, posterior ribs, and all bones (the complete set of ribs and clavicle). On the basis of a sufficient dataset, a multitask dense connection U-Net (MDU-Net) is proposed to address the challenge of bone segmentation from a chest radiograph. We first combine the U-Net multiscale feature fusion method, DenseNet dense connection, and multitasking mechanism to construct the proposed network referred to as MDU-Net. We then present a mask encoding mechanism that can force the network to learn the background features. Transfer learning is ultimately introduced to help the network extract sufficient features. We evaluate the proposed network by fourfold cross validation on 88 chest radiography images. The proposed method achieves the average DSC (Dice similarity coefficient) values of 93.78%, 80.95%, 89.06%, and 88.38% in clavicle segmentation, anterior rib segmentation, posterior rib segmentation, and segmentation of all bones, respectively.

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“…For example, ground truth masks and predicted ones are separated with a classifier in image level [6], patch level [43], or both [60]. Moreover, their consistencies can be supervised with earth‐mover distances in the Wasserstein generative adversarial networks [64, 65]. To enlarge dataset while preserving geometric information in synthetic data, depth images are auxiliary inputs of a generative adversarial network generating colour images in real‐world domain [17].…”

Section: Related Workmentioning

confidence: 99%