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 relationships play a critical role in the accurate delineation of pulmonary lobes when the lungs are affected by diseases such as COVID-19 or COPD. In this paper, we propose a relational approach (RTSU-Net) that leverages structured relationships by introducing a novel non-local neural network module. The proposed module learns both visual and geometric relationships among all convolution features to produce self-attention weights. With a limited amount of training data available from COVID-19 subjects, we initially train and validate RTSU-Net on a cohort of 5000 subjects from the COPDGene study (4000 for training and 1000 for evaluation). Using models pre-trained on COPDGene, we apply transfer learning to retrain and evaluate RTSU-Net on 470 COVID-19 suspects (370 for retraining and 100 for evaluation). Experimental results show that RTSU-Net outperforms three baselines and performs robustly on cases with severe lung infection due to COVID-19.

Automated Assessment of COVID-19 Reporting and Data System and Chest CT Severity Scores in Patients Suspected of Having COVID-19 Using Artificial Intelligence

“…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.…”

Joint prediction and time estimation of COVID-19 developing severe symptoms using chest CT scan

“…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.…”

“…( 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.…”

On the Role of Artificial Intelligence in Medical Imaging of COVID-19

“…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 .…”