Relational Modeling for Robust and Efficient Pulmonary Lobe Segmentation in CT Scans

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.

Errataetall:

UpdateIn: IEEE Trans Med Imaging. 2020 Aug;39(8):2664-2675. - PMID 32730216

Media Type:

Electronic Article

Year of Publication:

2020

Contained In:

ArXiv - (16.04.2020)

Language:

English

Contributors:

Xie, Weiyi
Jacobs, Colin
Charbonnier, Jean-Paul
van Ginneken, Bram

Keywords:

Preprint

Notes:

Date Revised 28.09.2020

published: Electronic

UpdateIn: IEEE Trans Med Imaging. 2020 Aug;39(8):2664-2675. - PMID 32730216

Citation Status PubMed-not-MEDLINE

Copyright: From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine

Physical Description:

Online-Ressource

PMID:

32550251

PPN (Catalogue-ID):

NLM312191693