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

arXiv: https://arxiv.org/abs/2004.07443 · HTML: https://mlsanity.com/paper/2004.07443/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Apr 2020.

## Built on by (most cited first, 14 of 14)

- [Fuzzy Attention Neural Network to Tackle Discontinuity in Airway Segmentation](https://mlsanity.com/paper/2209.02048/) (2022)
- [Semi-Supervised Segmentation of Radiation-Induced Pulmonary Fibrosis from Lung CT Scans with Multi-Scale Guided Dense Attention](https://mlsanity.com/paper/2109.14172/) (2021)
- [Is attention all you need in medical image analysis? A review](https://mlsanity.com/paper/2307.12775/) (2023)
- [RCoNet: Deformable Mutual Information Maximization and High-order Uncertainty-aware Learning for Robust COVID-19 Detection](https://mlsanity.com/paper/2102.11099/) (2021)
- [Structure and position-aware graph neural network for airway labeling](https://mlsanity.com/paper/2201.04532/) (2022)
- [D2A U-Net: Automatic Segmentation of COVID-19 Lesions from CT Slices with Dilated Convolution and Dual Attention Mechanism](https://mlsanity.com/paper/2102.05210/) (2021)
- [Dual Multi-scale Mean Teacher Network for Semi-supervised Infection Segmentation in Chest CT Volume for COVID-19](https://mlsanity.com/paper/2211.05548/) (2022)
- [Automated Estimation of Total Lung Volume using Chest Radiographs and Deep Learning](https://mlsanity.com/paper/2105.01181/) (2021)
- [Automatic lobe segmentation using attentive cross entropy and end-to-end fissure generation](https://mlsanity.com/paper/2307.12634/) (2023)
- [PAM: A Propagation-Based Model for Segmenting Any 3D Objects across Multi-Modal Medical Images](https://mlsanity.com/paper/2408.13836/) (2024)
- [Bronchovascular Tree-Guided Weakly Supervised Learning Method for Pulmonary Segment Segmentation](https://mlsanity.com/paper/2505.13911/) (2025)
- [Quantification of pulmonary involvement in COVID-19 pneumonia by means of a cascade oftwo U-nets: training and assessment on multipledatasets using different annotation criteria](https://mlsanity.com/paper/2105.02566/) (2021)
- [The STOIC2021 COVID-19 AI challenge: applying reusable training methodologies to private data](https://mlsanity.com/paper/2306.10484/) (2023)
- [PnPNet: Pull-and-Push Networks for Volumetric Segmentation with Boundary Confusion](https://mlsanity.com/paper/2312.08323/) (2023)

## Abstract (arXiv)

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.