Leveraging Anatomical Constraints with Uncertainty for Pneumothorax Segmentation

• URL: http://arxiv.org/abs/2311.15213v1
• Date: Sun, 26 Nov 2023 07:03:17 GMT
• Title: Leveraging Anatomical Constraints with Uncertainty for Pneumothorax Segmentation
• Authors: Han Yuan, Chuan Hong, Nguyen Tuan Anh Tran, Xinxing Xu, Nan Liu
• Abstract summary: Pneumothorax is a medical emergency caused by abnormal accumulation of air in the pleural space. Deep learning (DL) has increasingly been utilized to segment pneumothorax lesions in chest radiographs. We propose a novel approach that incorporates the lung+ space as a constraint during DL model training for pneumothorax segmentation on 2D chest radiographs.
• Score: 8.198854439726853
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Pneumothorax is a medical emergency caused by abnormal accumulation of air in the pleural space - the potential space between the lungs and chest wall. On 2D chest radiographs, pneumothorax occurs within the thoracic cavity and outside of the mediastinum and we refer to this area as "lung+ space". While deep learning (DL) has increasingly been utilized to segment pneumothorax lesions in chest radiographs, many existing DL models employ an end-to-end approach. These models directly map chest radiographs to clinician-annotated lesion areas, often neglecting the vital domain knowledge that pneumothorax is inherently location-sensitive. We propose a novel approach that incorporates the lung+ space as a constraint during DL model training for pneumothorax segmentation on 2D chest radiographs. To circumvent the need for additional annotations and to prevent potential label leakage on the target task, our method utilizes external datasets and an auxiliary task of lung segmentation. This approach generates a specific constraint of lung+ space for each chest radiograph. Furthermore, we have incorporated a discriminator to eliminate unreliable constraints caused by the domain shift between the auxiliary and target datasets. Our results demonstrated significant improvements, with average performance gains of 4.6%, 3.6%, and 3.3% regarding Intersection over Union (IoU), Dice Similarity Coefficient (DSC), and Hausdorff Distance (HD). Our research underscores the significance of incorporating medical domain knowledge about the location-specific nature of pneumothorax to enhance DL-based lesion segmentation.

Related papers

• Accurate Fine-Grained Segmentation of Human Anatomy in Radiographs via Volumetric Pseudo-Labeling [66.75096111651062]
  We created a large-scale dataset of 10,021 thoracic CTs with 157 labels. We applied an ensemble of 3D anatomy segmentation models to extract anatomical pseudo-labels. Our resulting segmentation models demonstrated remarkable performance on CXR.
  arXiv  Detail & Related papers  (2023-06-06T18:01:08Z)
• SGDA: Towards 3D Universal Pulmonary Nodule Detection via Slice Grouped Domain Attention [47.44114201293201]
  Lung cancer is the leading cause of cancer death worldwide. Current pulmonary nodule detection methods are usually domain-specific. We propose a slice grouped domain attention (SGDA) module to enhance the generalization capability of the pulmonary nodule detection networks.
  arXiv  Detail & Related papers  (2023-03-07T03:17:49Z)
• An Efficient and Robust Method for Chest X-Ray Rib Suppression that Improves Pulmonary Abnormality Diagnosis [0.49998148477760956]
  Suppression of thoracic bone shadows on chest X-rays (CXRs) has been indicated to improve the diagnosis of pulmonary disease. Previous approaches can be categorized as unsupervised physical and supervised deep learning models. We propose a generalizable yet efficient workflow of two stages: (1) training pairs generation with GT bone shadows eliminated in minimization by a physical model in spatially transformed gradient fields. (2) fully supervised image denoising network training on stage-one datasets for fast rib removal on incoming CXRs.
  arXiv  Detail & Related papers  (2023-02-19T23:47:02Z)
• 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)
• 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)
• Development of a Multi-Task Learning V-Net for Pulmonary Lobar Segmentation on Computed Tomography and Application to Diseased Lungs [0.19573380763700707]
  Diseased lung regions often produce high-density zones on CT images, limiting an algorithm's execution to specify damaged lobes. This impact motivated developing an improved machine learning method to segment lung lobes. The approach can be readily adopted in the clinical setting as a robust tool for radiologists.
  arXiv  Detail & Related papers  (2021-05-11T17:10:25Z)
• Deep LF-Net: Semantic Lung Segmentation from Indian Chest Radiographs Including Severely Unhealthy Images [5.826056983051642]
  A chest radiograph, commonly called chest x-ray (CxR), plays a vital role in the diagnosis of various lung diseases. Precise lung segmentation is extremely challenging because of variance in the shape of the lung caused by health issues, age, and gender. The proposed work investigates the use of an efficient deep convolutional neural network for accurate segmentation of lungs from CxR.
  arXiv  Detail & Related papers  (2020-11-19T07:21:02Z)
• Pneumothorax and chest tube classification on chest x-rays for detection of missed pneumothorax [1.181048306817215]
  We present an image classification pipeline which detects pneumothorax and the various types of chest tubes that are commonly used to treat pneumothorax. Our multi-stage algorithm is based on lung segmentation followed by pneumothorax classification, including classification of patches that are most likely to contain pneumothorax.
  arXiv  Detail & Related papers  (2020-11-14T18:06:06Z)
• Contralaterally Enhanced Networks for Thoracic Disease Detection [120.60868136876599]
  There exist many similar structures in the left and right parts of the chest, such as ribs, lung fields and bronchial tubes. This kind of similarities can be used to identify diseases in chest X-rays, according to the experience of broad-certificated radiologists. We propose a deep end-to-end module to exploit the contralateral context information for enhancing feature representations of disease proposals.
  arXiv  Detail & Related papers  (2020-10-09T10:15:26Z)
• 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)

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.