Pulmonary Airway Segmentation

Pulmonary airway segmentation aims to automatically identify and delineate the airways in computed tomography (CT) scans of the lungs, facilitating quantitative analysis of airway morphology for disease diagnosis and monitoring. Current research emphasizes improving the accuracy and robustness of deep learning models, particularly 3D U-Net architectures and their variants, often incorporating uncertainty estimation and topological constraints to address the challenges posed by the complex, tree-like structure of the airways and limited annotated data. These advancements are driving the development of automated pipelines for airway quantification, offering potential for improved diagnostic biomarkers and personalized treatment strategies in diseases like idiopathic pulmonary fibrosis. The creation of large, publicly available datasets is also crucial for benchmarking and advancing the field.

Papers

July 3, 2024

Probing Perfection: The Relentless Art of Meddling for Pulmonary Airway Segmentation from HRCT via a Human-AI Collaboration Based Active Learning Method
Shiyi Wang, Yang Nan, Sheng Zhang, Federico Felder, Xiaodan Xing, Yingying Fang, Javier Del Ser, Simon L F Walsh, Guang Yang
Deep Learning Linear Probing Medical Segmentation Jazz Improvisation Human Computer Pulmonary Airway Segmentation

February 12, 2024

Make it more specific: A novel uncertainty based airway segmentation application on 3D U-Net and its variants
Shiyi Wang, Yang Nan, Felder Federico N, Sheng Zhang, Walsh Simon L F, Guang Yang
Segmentation Accuracy High Uncertainty Value Medical Segmentation Natural Language Oriented Variant Airway Segmentation 3D U Net Pulmonary Airway Segmentation

March 10, 2023

Multi-site, Multi-domain Airway Tree Modeling (ATM'22): A Public Benchmark for Pulmonary Airway Segmentation
Minghui Zhang, Yangqian Wu, Hanxiao Zhang, Yulei Qin, Hao Zheng, Wen Tang, Corey Arnold, Chenhao Pei, Pengxin Yu, Yang Nan, Guang Yang, Simon Walsh, Dominic C. Marshall, Matthieu Komorowski, Puyang Wang, Dazhou Guo, Dakai Jin, Ya'nan Wu, Shuiqing Zhao, Runsheng Chang, Boyu Zhang, Xing Lv, Abdul Qayyum, Moona Mazher, Qi Su, Yonghuang Wu, Ying'ao Liu, Yufei Zhu, Jiancheng Yang, Ashkan Pakzad, Bojidar Rangelov, Raul San Jose Estepar, Carlos Cano Espinosa, Jiayuan Sun, Guang-Zhong Yang, Yun Gu
Computer Vision Airway Tree Multi Site Public Benchmark Pulmonary Airway Segmentation

December 15, 2022

Two-stage Contextual Transformer-based Convolutional Neural Network for Airway Extraction from CT Images
Yanan Wu, Shuiqing Zhao, Shouliang Qi, Jie Feng, Haowen Pang, Runsheng Chang, Long Bai, Mengqi Li, Shuyue Xia, Wei Qian, Hongliang Ren
CT Image Multi Stage Airway Segmentation Airway Tree Context Aware Transformer Pulmonary Airway Segmentation

September 17, 2022

Differentiable Topology-Preserved Distance Transform for Pulmonary Airway Segmentation
Minghui Zhang, Guang-Zhong Yang, Yun Gu
Closed Form Differentiable Expression Airway Segmentation Preserving Transformation Efficient Surrogate Model Pulmonary Airway Segmentation Topology Preserving

August 30, 2022

Airway measurement by refinement of synthetic images improves mortality prediction in idiopathic pulmonary fibrosis
Ashkan Pakzad, Mou-Cheng Xu, Wing Keung Cheung, Marie Vermant, Tinne Goos, Laurens J De Sadeleer, Stijn E Verleden, Wim A Wuyts, John R Hurst, Joseph Jacob
Synthetic Image Mortality Prediction Airway Tree Idiopathic Pulmonary Fibrosis Pulmonary Airway Segmentation

November 19, 2021

Evaluation of automated airway morphological quantification for assessing fibrosing lung disease
Ashkan Pakzad, Wing Keung Cheung, Kin Quan, Nesrin Mogulkoc, Coline H. M. Van Moorsel, Brian J. Bartholmai, Hendrik W. Van Es, Alper Ezircan, Frouke Van Beek, Marcel Veltkamp, Ronald Karwoski, Tobias Peikert, Ryan D. Clay, Finbar Foley, Cassandra Braun, Recep Savas, Carole Sudre, Tom Doel, Daniel C. Alexander, Peter Wijeratne, David Hawkes, Yipeng Hu, John R Hurst, Joseph Jacob
Global Evaluation Airway Segmentation Lung Disease Morphological Profiling Idiopathic Pulmonary Fibrosis Pulmonary Airway Segmentation

Pulmonary Airway Segmentation

Papers

Probing Perfection: The Relentless Art of Meddling for Pulmonary Airway Segmentation from HRCT via a Human-AI Collaboration Based Active Learning Method

Make it more specific: A novel uncertainty based airway segmentation application on 3D U-Net and its variants

Multi-site, Multi-domain Airway Tree Modeling (ATM'22): A Public Benchmark for Pulmonary Airway Segmentation

Two-stage Contextual Transformer-based Convolutional Neural Network for Airway Extraction from CT Images

Differentiable Topology-Preserved Distance Transform for Pulmonary Airway Segmentation

Airway measurement by refinement of synthetic images improves mortality prediction in idiopathic pulmonary fibrosis

Evaluation of automated airway morphological quantification for assessing fibrosing lung disease