2020
DOI: 10.1109/tmi.2020.2995108
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Relational Modeling for Robust and Efficient Pulmonary Lobe Segmentation in CT Scans

Abstract: Pulmonary lobe segmentation in computed tomography scans is essential for regional assessment of pulmonary diseases. Recent works based on convolution neural networks have achieved good performance for this task. However, they are still limited in capturing structured relationships due to the nature of convolution. The shape of the pulmonary lobes affect each other and their borders relate to the appearance of other structures, such as vessels, airways, and the pleural wall. We argue that such structural relat… Show more

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Cited by 110 publications
(93 citation statements)
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References 34 publications
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“…For lobe segmentation and labeling, we used a relational two-stage U-Net architecture specifically developed for robust pulmonary lobe segmentation (20). The model was pretrained on 4000 chest CT scans from the COPDGene study (21) and fine-tuned with 400 scans from the present study.…”
Section: Automated Ct Scoringmentioning
confidence: 99%
“…For lobe segmentation and labeling, we used a relational two-stage U-Net architecture specifically developed for robust pulmonary lobe segmentation (20). The model was pretrained on 4000 chest CT scans from the COPDGene study (21) and fine-tuned with 400 scans from the present study.…”
Section: Automated Ct Scoringmentioning
confidence: 99%
“…AI technologies have widely been applied in the study of COVID-19 disease with the medical imaging data including X-ray and Computed Tomography (CT), in the applications of segmentation ( Fan, Zhou, Ji, Zhou, Chen, Fu, Shen, Shao, 2020 , Xie, Jacobs, Charbonnier, van Ginneken, 2020 ) and diagnosis ( Kang, Xia, Yan, Wan, Shi, Yuan, Jiang, Wu, Sui, Zhang, et al., 2020 , Ouyang, Huo, Xia, Shan, Liu, Mo, Yan, Ding, Yang, Song, et al., 2020 ). In this study, we give a brief review of the COVID-19 diagnosis and prognosis.…”
Section: Related Workmentioning
confidence: 99%
“…( Fan et al., 2020 ) proposed to first employ a deep segmentation network to automatically identify infected regions in chest CT scans, and then conduct the COVID-19 diagnosis by a semi-supervised way. ( Xie et al., 2020b ) designed two diverse relational U-networks to segment pulmonary lobes with CT images to output the high-level convolution features for COVID-19 diagnosis.…”
Section: Related Workmentioning
confidence: 99%
“…The manual delineation of radiologists necessary for segmentation tasks inherently introduces some inter-rater variability which necessitates segmentation techniques that can deal with uncertainty in annotations 46 . Others have proposed topological modelling techniques that explore structural relationships between vessels, airways and the pleural wall and break up with the common strategy of utilizing fully local modules such as convolutions 47 .…”
Section: Resultsmentioning
confidence: 99%