Hunting imaging biomarkers in pulmonary fibrosis: Benchmarks of the AIIB23 challenge

• URL: http://arxiv.org/abs/2312.13752v2
• Date: Tue, 16 Apr 2024 17:55:53 GMT
• Title: Hunting imaging biomarkers in pulmonary fibrosis: Benchmarks of the AIIB23 challenge
• Authors: Yang Nan, Xiaodan Xing, Shiyi Wang, Zeyu Tang, Federico N Felder, Sheng Zhang, Roberta Eufrasia Ledda, Xiaoliu Ding, Ruiqi Yu, Weiping Liu, Feng Shi, Tianyang Sun, Zehong Cao, Minghui Zhang, Yun Gu, Hanxiao Zhang, Jian Gao, Pingyu Wang, Wen Tang, Pengxin Yu, Han Kang, Junqiang Chen, Xing Lu, Boyu Zhang, Michail Mamalakis, Francesco Prinzi, Gianluca Carlini, Lisa Cuneo, Abhirup Banerjee, Zhaohu Xing, Lei Zhu, Zacharia Mesbah, Dhruv Jain, Tsiry Mayet, Hongyu Yuan, Qing Lyu, Abdul Qayyum, Moona Mazher, Athol Wells, Simon LF Walsh, Guang Yang,
• Abstract summary: The 'Airway-Informed Quantitative CT Imaging Biomarker for Fibrotic Lung Disease 2023' competition was organized in conjunction with the official 2023 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 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
• Score: 29.973814271192744
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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>1.5, p<0.0001) was revealed for survival prognostication compared with existing clinical measurements, clinician assessment and AI-based biomarkers.

Related papers

• Variational Autoencoders for Feature Exploration and Malignancy Prediction of Lung Lesions [0.0]
  Lung cancer is responsible for 21% of cancer deaths in the UK. Recent studies have demonstrated the capability of AI methods for accurate and early diagnosis of lung cancer from routine scans. This study investigates the application Variational Autoencoders (VAEs), a type of generative AI model, to lung cancer lesions.
  arXiv  Detail & Related papers  (2023-11-27T11:12:33Z)
• Deep learning automated quantification of lung disease in pulmonary hypertension on CT pulmonary angiography: A preliminary clinical study with external validation [0.0]
  This study aims to develop an artificial intelligence (AI) deep learning model for lung texture classification in CT Pulmonary Angiography (CTPA) "Normal", "Ground glass", "Ground glass with reticulation", "Honeycombing", and "Emphysema" were classified as per the Fleishner Society glossary of terms. Proportion of lung volume for each texture was calculated by classifying patches throughout the entire lung volume to generate a coarse texture classification mapping throughout the lung parenchyma.
  arXiv  Detail & Related papers  (2023-03-20T14:06:32Z)
• Multi-site, Multi-domain Airway Tree Modeling (ATM'22): A Public Benchmark for Pulmonary Airway Segmentation [36.07818905920447]
  Open international challenges are becoming the de facto standard for assessing computer vision and image analysis algorithms. To provide a benchmark for the medical imaging community, we organized the Multi-site, Multi-domain Airway Tree Modeling (ATM'22) ATM'22 provides large-scale CT scans with detailed pulmonary airway annotation, including 500 CT scans (300 for training, 50 for validation, and 150 for testing) Twenty-three teams participated in the entire phase of the challenge and the algorithms for the top ten teams are reviewed in this paper.
  arXiv  Detail & Related papers  (2023-03-10T07:08:25Z)
• CoRSAI: A System for Robust Interpretation of CT Scans of COVID-19 Patients Using Deep Learning [133.87426554801252]
  We adopted an approach based on using an ensemble of deep convolutionalneural networks for segmentation of lung CT scans. Using our models we are able to segment the lesions, evaluatepatients dynamics, estimate relative volume of lungs affected by lesions and evaluate the lung damage stage.
  arXiv  Detail & Related papers  (2021-05-25T12:06:55Z)
• COVIDx-US -- An open-access benchmark dataset of ultrasound imaging data for AI-driven COVID-19 analytics [116.6248556979572]
  COVIDx-US is an open-access benchmark dataset of COVID-19 related ultrasound imaging data. It consists of 93 lung ultrasound videos and 10,774 processed images of patients infected with SARS-CoV-2 pneumonia, non-SARS-CoV-2 pneumonia, as well as healthy control cases.
  arXiv  Detail & Related papers  (2021-03-18T03:31:33Z)
• Fibrosis-Net: A Tailored Deep Convolutional Neural Network Design for Prediction of Pulmonary Fibrosis Progression from Chest CT Images [59.622239796473885]
  Pulmonary fibrosis is a chronic lung disease that causes irreparable lung tissue scarring and damage, resulting in progressive loss in lung capacity and no known cure. We introduce Fibrosis-Net, a deep convolutional neural network design tailored for the prediction of pulmonary fibrosis progression from chest CT images.
  arXiv  Detail & Related papers  (2021-03-06T02:16:41Z)
• CT Image Segmentation for Inflamed and Fibrotic Lungs Using a Multi-Resolution Convolutional Neural Network [6.177921466996229]
  The purpose of this study was to develop a fully-automated segmentation algorithm, robust to various density enhancing lung abnormalities. A polymorphic training approach is proposed, in which both specifically labeled left and right lungs of humans with COPD, and nonspecifically labeled lungs of animals with acute lung injury, were incorporated into training a single neural network. The resulting network is intended for predicting left and right lung regions in humans with or without diffuse opacification and consolidation.
  arXiv  Detail & Related papers  (2020-10-16T18:25:59Z)
• M3Lung-Sys: A Deep Learning System for Multi-Class Lung Pneumonia Screening from CT Imaging [85.00066186644466]
  We propose a Multi-task Multi-slice Deep Learning System (M3Lung-Sys) for multi-class lung pneumonia screening from CT imaging. In addition to distinguish COVID-19 from Healthy, H1N1, and CAP cases, our M 3 Lung-Sys also be able to locate the areas of relevant lesions.
  arXiv  Detail & Related papers  (2020-10-07T06:22:24Z)
• A New Screening Method for COVID-19 based on Ocular Feature Recognition by Machine Learning Tools [66.20818586629278]
  Coronavirus disease 2019 (COVID-19) has affected several million people. New screening method of analyzing the eye-region images, captured by common CCD and CMOS cameras, could reliably make a rapid risk screening of COVID-19.
  arXiv  Detail & Related papers  (2020-09-04T00:50:27Z)
• Integrative Analysis for COVID-19 Patient Outcome Prediction [53.11258640541513]
  We combine radiomics of lung opacities and non-imaging features from demographic data, vital signs, and laboratory findings to predict need for intensive care unit admission. Our methods may also be applied to other lung diseases including but not limited to community acquired pneumonia.
  arXiv  Detail & Related papers  (2020-07-20T19:08:50Z)
• AI-Driven CT-based quantification, staging and short-term outcome prediction of COVID-19 pneumonia [3.672093204122992]
  Chest computed tomography (CT) is widely used for the management of Coronavirus disease 2019 (COVID-19) pneumonia. CT has a prognostic role by allowing visually evaluating the extent of COVID-19 lung abnormalities.
  arXiv  Detail & Related papers  (2020-04-20T12:24:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.