Hunting imaging biomarkers in pulmonary fibrosis: Benchmarks of the AIIB23 challenge
Creators: name: Nan, Yang
name: Xing, Xiaodan
name: Wang, Shiyi
name: Tang, Zeyu
name: Felder, Federico N
name: Zhang, Sheng
name: Ledda, Roberta Eufrasia
name: Ding, Xiaoliu
name: Yu, Ruiqi
name: Liu, Weiping
name: Shi, Feng
name: Sun, Tianyang
name: Cao, Zehong
name: Zhang, Minghui
name: Gu, Yun
name: Zhang, Hanxiao
name: Gao, Jian
name: Wang, Pingyu
name: Tang, Wen
name: Yu, Pengxin
name: Kang, Han
name: Chen, Junqiang
name: Lu, Xing
name: Zhang, Boyu
name: Mamalakis, Michail
name: Prinzi, Francesco
name: Carlini, Gianluca
name: Cuneo, Lisa
name: Banerjee, Abhirup
name: Xing, Zhaohu
name: Zhu, Lei
name: Mesbah, Zacharia
name: Jain, Dhruv
name: Mayet, Tsiry
name: Yuan, Hongyu
name: Lyu, Qing
name: Qayyum, Abdul
name: Mazher, Moona
name: Wells, Athol
name: Walsh, Simon LF
name: Yang, Guang
Publisher: name: arXiv
Year: 2023
Subjects: subject: Image and Video Processing (eess.IV)
subject: Artificial Intelligence (cs.AI)
subject: Computer Vision and Pattern Recognition (cs.CV)
subject: FOS: Electrical engineering, electronic engineering, information engineering
subject: FOS: Electrical engineering, electronic engineering, information engineering
subject: FOS: Computer and information sciences
subject: FOS: Computer and information sciences

description: Airway-related quantitative imaging biomarkers are crucial for examination, diagnosis, and prognosis in pulmonary diseases. However, the manual delineation of airway trees remains prohibitively time-consuming. While significant efforts have been made towards enhancing airway modelling, current public-available datasets concentrate on lung diseases with moderate morphological variations. The intricate honeycombing patterns present in the lung tissues of fibrotic lung disease patients exacerbate the challenges, often leading to various prediction errors. To address this issue, the 'Airway-Informed Quantitative CT Imaging Biomarker for Fibrotic Lung Disease 2023' (AIIB23) competition was organized in conjunction with the official 2023 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). The airway structures were meticulously annotated by three experienced radiologists. Competitors were encouraged to develop automatic airway segmentation models with high robustness and generalization abilities, followed by exploring the most correlated QIB of mortality prediction. A training set of 120 high-resolution computerised tomography (HRCT) scans were publicly released with expert annotations and mortality status. The online validation set incorporated 52 HRCT scans from patients with fibrotic lung disease and the offline test set included 140 cases from fibrosis and COVID-19 patients. The results have shown that the capacity of extracting airway trees from patients with fibrotic lung disease could be enhanced by introducing voxel-wise weighted general union loss and continuity loss. In addition to the competitive image biomarkers for prognosis, a strong airway-derived biomarker (Hazard ratio&gt;1.5, p&lt;0.0001) was revealed for survival prognostication compared with existing clinical measurements, clinician assessment and AI-based biomarkers.
description: 19 pages