# NaviAirway: a Bronchiole-sensitive Deep Learning-based Airway Segmentation Pipeline

arXiv: https://arxiv.org/abs/2203.04294 · HTML: https://mlsanity.com/paper/2203.04294/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 63 references that are in the map.
- First posted on arXiv 8 Mar 2022.

## Builds on (most cited first, 18 of 18)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [Self-training with Noisy Student improves ImageNet classification](https://mlsanity.com/paper/1911.04252/) (2019)
- [Attention Gated Networks: Learning to Leverage Salient Regions in Medical Images](https://mlsanity.com/paper/1808.08114/) (2018)
- [Theoretical Analysis of Self-Training with Deep Networks on Unlabeled Data](https://mlsanity.com/paper/2010.03622/) (2020)
- [TorchIO: A Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning](https://mlsanity.com/paper/2003.04696/) (2020)
- [Shape-Aware Organ Segmentation by Predicting Signed Distance Maps](https://mlsanity.com/paper/1912.03849/) (2019)
- [Alleviating Class-wise Gradient Imbalance for Pulmonary Airway Segmentation](https://mlsanity.com/paper/2011.11952/) (2020)
- [AirwayNet: A Voxel-Connectivity Aware Approach for Accurate Airway Segmentation Using Convolutional Neural Networks](https://mlsanity.com/paper/1907.06852/) (2019)
- [Learning Tubule-Sensitive CNNs for Pulmonary Airway and Artery-Vein Segmentation in CT](https://mlsanity.com/paper/2012.05767/) (2020)
- [A joint 3D UNet-Graph Neural Network-based method for Airway Segmentation from chest CTs](https://mlsanity.com/paper/1908.08588/) (2019)
- [Automatic Airway Segmentation in chest CT using Convolutional Neural Networks](https://mlsanity.com/paper/1808.04576/) (2018)
- [Automatic airway segmentation from Computed Tomography using robust and efficient 3-D convolutional neural networks](https://mlsanity.com/paper/2103.16328/) (2021)
- [BREAK: Bronchi Reconstruction by gEodesic transformation And sKeleton embedding](https://mlsanity.com/paper/2202.00002/) (2022)
- [FDA: Feature Decomposition and Aggregation for Robust Airway Segmentation](https://mlsanity.com/paper/2109.02920/) (2021)
- [LTSP: Long-Term Slice Propagation for Accurate Airway Segmentation](https://mlsanity.com/paper/2202.06260/) (2022)

## Built on by (most cited first, 17 of 17)

- [MedSyn: Text-guided Anatomy-aware Synthesis of High-Fidelity 3D CT Images](https://mlsanity.com/paper/2310.03559/) (2023)
- [Fuzzy Attention Neural Network to Tackle Discontinuity in Airway Segmentation](https://mlsanity.com/paper/2209.02048/) (2022)
- [Two-stage Contextual Transformer-based Convolutional Neural Network for Airway Extraction from CT Images](https://mlsanity.com/paper/2212.07651/) (2022)
- [AeroPath: An airway segmentation benchmark dataset with challenging pathology](https://mlsanity.com/paper/2311.01138/) (2023)
- [Topology Repairing of Disconnected Pulmonary Airways and Vessels: Baselines and a Dataset](https://mlsanity.com/paper/2306.07089/) (2023)
- [Adversarial Transformer for Repairing Human Airway Segmentation](https://mlsanity.com/paper/2210.12029/) (2022)
- [Anatomical feature-prioritized loss for enhanced MR to CT translation](https://mlsanity.com/paper/2410.10328/) (2024)
- [GDDS: Pulmonary Bronchioles Segmentation with Group Deep Dense Supervision](https://mlsanity.com/paper/2303.09212/) (2023)
- [Extraction of Pulmonary Airway in CT Scans Using Deep Fully Convolutional Networks](https://mlsanity.com/paper/2208.07202/) (2022)
- [Interpolation-Split: a data-centric deep learning approach with big interpolated data to boost airway segmentation performance](https://mlsanity.com/paper/2308.00008/) (2023)
- [Skeleton Supervised Airway Segmentation](https://mlsanity.com/paper/2403.06510/) (2024)
- [Fuzzy Attention-based Border Rendering Network for Lung Organ Segmentation](https://mlsanity.com/paper/2406.16189/) (2024)
- [Prompt Mechanisms in Medical Imaging: A Comprehensive Survey](https://mlsanity.com/paper/2507.01055/) (2025)
- [Airway Segmentation Network for Enhanced Tubular Feature Extraction](https://mlsanity.com/paper/2507.06581/) (2025)
- [A Self-training Framework for Semi-supervised Pulmonary Vessel Segmentation and Its Application in COPD](https://mlsanity.com/paper/2507.19074/) (2025)
- [RepAir: A Framework for Airway Segmentation and Discontinuity Correction in CT](https://mlsanity.com/paper/2511.14649/) (2025)
- [Anatomy-Aware Prediction of Bronchoscopic Accessibility from 3D CT](https://mlsanity.com/paper/2609.37386/) (2026)

## Abstract (arXiv)

Airway segmentation is essential for chest CT image analysis. Different from natural image segmentation, which pursues high pixel-wise accuracy, airway segmentation focuses on topology. The task is challenging not only because of its complex tree-like structure but also the severe pixel imbalance among airway branches of different generations. To tackle the problems, we present a NaviAirway method which consists of a bronchiole-sensitive loss function for airway topology preservation and an iterative training strategy for accurate model learning across different airway generations. To supplement the features of airway branches learned by the model, we distill the knowledge from numerous unlabeled chest CT images in a teacher-student manner. Experimental results show that NaviAirway outperforms existing methods, particularly in the identification of higher-generation bronchioles and robustness to new CT scans. Moreover, NaviAirway is general enough to be combined with different backbone models to significantly improve their performance. NaviAirway can generate an airway roadmap for Navigation Bronchoscopy and can also be applied to other scenarios when segmenting fine and long tubular structures in biomedical images. The code is publicly available on https://github.com/AntonotnaWang/NaviAirway.