Evaluation of automated airway morphological quantification for assessing fibrosing lung disease

• URL: http://arxiv.org/abs/2111.10443v1
• Date: Fri, 19 Nov 2021 21:30:42 GMT
• Title: Evaluation of automated airway morphological quantification for assessing fibrosing lung disease
• Authors: 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
• Abstract summary: Abnormal airway dilatation, termed traction bronchiectasis, is a typical feature of idiopathic pulmonary fibrosis (IPF) We propose AirQuant, an automated pipeline that parcellates the airway tree into its lobes and generational branches from a deep learning based airway segmentation.
• Score: 7.027000487683603
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Abnormal airway dilatation, termed traction bronchiectasis, is a typical feature of idiopathic pulmonary fibrosis (IPF). Volumetric computed tomography (CT) imaging captures the loss of normal airway tapering in IPF. We postulated that automated quantification of airway abnormalities could provide estimates of IPF disease extent and severity. We propose AirQuant, an automated computational pipeline that systematically parcellates the airway tree into its lobes and generational branches from a deep learning based airway segmentation, deriving airway structural measures from chest CT. Importantly, AirQuant prevents the occurrence of spurious airway branches by thick wave propagation and removes loops in the airway-tree by graph search, overcoming limitations of existing airway skeletonisation algorithms. Tapering between airway segments (intertapering) and airway tortuosity computed by AirQuant were compared between 14 healthy participants and 14 IPF patients. Airway intertapering was significantly reduced in IPF patients, and airway tortuosity was significantly increased when compared to healthy controls. Differences were most marked in the lower lobes, conforming to the typical distribution of IPF-related damage. AirQuant is an open-source pipeline that avoids limitations of existing airway quantification algorithms and has clinical interpretability. Automated airway measurements may have potential as novel imaging biomarkers of IPF severity and disease extent.

Related papers

• AeroPath: An airway segmentation benchmark dataset with challenging pathology [0.0]
  We introduce a new public benchmark dataset (AeroPath) consisting of 27 CT images from patients with pathologies ranging from emphysema to large tumors. We present a multiscale fusion design for automatic airway segmentation. Models were trained on the ATM'22 dataset, tested on the AeroPath dataset, and further evaluated against competitive open-source methods. The results demonstrated that our proposed architecture predicted topologically correct segmentations for all the patients included in the AeroPath dataset.
  arXiv  Detail & Related papers  (2023-11-02T10:41:42Z)
• Fuzzy Attention Neural Network to Tackle Discontinuity in Airway Segmentation [67.19443246236048]
  Airway segmentation is crucial for the examination, diagnosis, and prognosis of lung diseases. Some small-sized airway branches (e.g., bronchus and terminaloles) significantly aggravate the difficulty of automatic segmentation. This paper presents an efficient method for airway segmentation, comprising a novel fuzzy attention neural network and a comprehensive loss function.
  arXiv  Detail & Related papers  (2022-09-05T16:38:13Z)
• Airway measurement by refinement of synthetic images improves mortality prediction in idiopathic pulmonary fibrosis [1.3290985445255554]
  We propose synthesising airways by style transfer using perceptual losses to train our model, Airway Transfer Network (ATN) ATN was shown to be quicker and easier to train than state-of-the-art GAN-based network (simGAN) ATN-based airway measurements were found to be consistently stronger predictors of mortality than simGAN-derived airway metrics on IPF CTs.
  arXiv  Detail & Related papers  (2022-08-30T10:48:48Z)
• Extraction of Pulmonary Airway in CT Scans Using Deep Fully Convolutional Networks [0.0]
  We use two-stage fully convolutional networks (FCNs) to automatically segment pulmonary airway in thoracic CT scans from multi-sites. Specifically, we adopt a 3D FCN with U-shape network architecture to segment pulmonary airway in a coarse resolution. The reported method was evaluated on the public training set of 300 cases and independent private validation set of 50 cases.
  arXiv  Detail & Related papers  (2022-08-12T15:56:21Z)
• What Makes for Automatic Reconstruction of Pulmonary Segments [50.216231776343115]
  3D reconstruction of pulmonary segments plays an important role in surgical treatment planning of lung cancer. However, automatic reconstruction of pulmonary segments remains unexplored in the era of deep learning. We propose ImPulSe, a deep implicit surface model designed for pulmonary segment reconstruction.
  arXiv  Detail & Related papers  (2022-07-07T04:24:17Z)
• Machine Learning for Mechanical Ventilation Control (Extended Abstract) [52.65490904484772]
  Mechanical ventilation is one of the most widely used therapies in the ICU. We frame these as a control problem: ventilators must let air in and out of the patient's lungs according to a prescribed trajectory of airway pressure. Our data-driven approach learns to control an invasive ventilator by training on a simulator itself trained on data collected from the ventilator. This method outperforms popular reinforcement learning algorithms and even controls the physical ventilator more accurately and robustly than PID.
  arXiv  Detail & Related papers  (2021-11-19T20:54:41Z)
• Lung Cancer Lesion Detection in Histopathology Images Using Graph-Based Sparse PCA Network [93.22587316229954]
  We propose a graph-based sparse principal component analysis (GS-PCA) network, for automated detection of cancerous lesions on histological lung slides stained by hematoxylin and eosin (H&E) We evaluate the performance of the proposed algorithm on H&E slides obtained from an SVM K-rasG12D lung cancer mouse model using precision/recall rates, F-score, Tanimoto coefficient, and area under the curve (AUC) of the receiver operator characteristic (ROC)
  arXiv  Detail & Related papers  (2021-10-27T19:28:36Z)
• 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 [83.83783947027392]
  This study aims at exploiting Artificial intelligence (AI) for the identification, segmentation and quantification of COVID-19 pulmonary lesions. We developed an automated analysis pipeline, the LungQuant system, based on a cascade of two U-nets. The accuracy in predicting the CT-Severity Score (CT-SS) of the LungQuant system has been also evaluated.
  arXiv  Detail & Related papers  (2021-05-06T10:21:28Z)
• 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)
• Generative-based Airway and Vessel Morphology Quantification on Chest CT Images [8.414072468546875]
  We propose a Convolutional Neural Regressor (CNR) that provides cross-sectional measurement of airway lumens, airway wall thickness, and vessel radius. CNR is trained with data created by a generative model of synthetic structures which is used in combination with Simulated and Unversa Generative Adrial Network (SimGAN) For vessels, we assess the correlation between our estimate of the small-vessel blood volume and the lungs' diffusing capacity for carbon monoxide (DLCO)
  arXiv  Detail & Related papers  (2020-02-13T18:45:31Z)

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.