Residual Attention U-Net for Automated Multi-Class Segmentation of COVID-19 Chest CT Images

• URL: http://arxiv.org/abs/2004.05645v1
• Date: Sun, 12 Apr 2020 16:24:59 GMT
• Title: Residual Attention U-Net for Automated Multi-Class Segmentation of COVID-19 Chest CT Images
• Authors: Xiaocong Chen, Lina Yao, Yu Zhang
• Abstract summary: coronavirus disease 2019 (COVID-19) has been spreading rapidly around the world and caused significant impact on the public health and economy. There is still lack of studies on effectively quantifying the lung infection caused by COVID-19. We propose a novel deep learning algorithm for automated segmentation of multiple COVID-19 infection regions.
• Score: 46.844349956057776
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The novel coronavirus disease 2019 (COVID-19) has been spreading rapidly around the world and caused significant impact on the public health and economy. However, there is still lack of studies on effectively quantifying the lung infection caused by COVID-19. As a basic but challenging task of the diagnostic framework, segmentation plays a crucial role in accurate quantification of COVID-19 infection measured by computed tomography (CT) images. To this end, we proposed a novel deep learning algorithm for automated segmentation of multiple COVID-19 infection regions. Specifically, we use the Aggregated Residual Transformations to learn a robust and expressive feature representation and apply the soft attention mechanism to improve the capability of the model to distinguish a variety of symptoms of the COVID-19. With a public CT image dataset, we validate the efficacy of the proposed algorithm in comparison with other competing methods. Experimental results demonstrate the outstanding performance of our algorithm for automated segmentation of COVID-19 Chest CT images. Our study provides a promising deep leaning-based segmentation tool to lay a foundation to quantitative diagnosis of COVID-19 lung infection in CT images.

Related papers

• Enhancing COVID-19 Severity Analysis through Ensemble Methods [13.792760290422185]
  This paper presents a domain knowledge-based pipeline for extracting regions of infection in COVID-19 patients. The severity of the infection is then classified into different categories using an ensemble of three machine-learning models. The proposed system was evaluated on a validation dataset in the AI-Enabled Medical Image Analysis Workshop and COVID-19 Diagnosis Competition.
  arXiv  Detail & Related papers  (2023-03-13T13:59:47Z)
• Optimising Chest X-Rays for Image Analysis by Identifying and Removing Confounding Factors [49.005337470305584]
  During the COVID-19 pandemic, the sheer volume of imaging performed in an emergency setting for COVID-19 diagnosis has resulted in a wide variability of clinical CXR acquisitions. The variable quality of clinically-acquired CXRs within publicly available datasets could have a profound effect on algorithm performance. We propose a simple and effective step-wise approach to pre-processing a COVID-19 chest X-ray dataset to remove undesired biases.
  arXiv  Detail & Related papers  (2022-08-22T13:57:04Z)
• Calibrated Bagging Deep Learning for Image Semantic Segmentation: A Case Study on COVID-19 Chest X-ray Image [3.135883872525168]
  Imaging tests such as chest X-ray (CXR) and computed tomography (CT) can provide useful information to clinical staff. Deep learning has been applied to perform COVID-19 infection region segmentation and disease classification. We propose a novel ensemble deep learning model through integrating bagging deep learning and model calibration.
  arXiv  Detail & Related papers  (2022-05-27T20:06:45Z)
• Automatic segmentation of novel coronavirus pneumonia lesions in CT images utilizing deep-supervised ensemble learning network [3.110938126026385]
  The structure features of COVID-19 lesions are complicated and varied greatly in different cases. A transfer learning strategy is employed to make up for the shortcoming and alleviate the overfitting problem. A deep-supervised ensemble learning network is presented to combine local and global features for COVID-19 lesion segmentation.
  arXiv  Detail & Related papers  (2021-10-25T11:49:20Z)
• COVIDNet-CT: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest CT Images [75.74756992992147]
  We introduce COVIDNet-CT, a deep convolutional neural network architecture that is tailored for detection of COVID-19 cases from chest CT images. We also introduce COVIDx-CT, a benchmark CT image dataset derived from CT imaging data collected by the China National Center for Bioinformation.
  arXiv  Detail & Related papers  (2020-09-08T15:49:55Z)
• Synergistic Learning of Lung Lobe Segmentation and Hierarchical Multi-Instance Classification for Automated Severity Assessment of COVID-19 in CT Images [61.862364277007934]
  We propose a synergistic learning framework for automated severity assessment of COVID-19 in 3D CT images. A multi-task deep network (called M$2$UNet) is then developed to assess the severity of COVID-19 patients. Our M$2$UNet consists of a patch-level encoder, a segmentation sub-network for lung lobe segmentation, and a classification sub-network for severity assessment.
  arXiv  Detail & Related papers  (2020-05-08T03:16:15Z)
• Diagnosis of Coronavirus Disease 2019 (COVID-19) with Structured Latent Multi-View Representation Learning [48.05232274463484]
  Recently, the outbreak of Coronavirus Disease 2019 (COVID-19) has spread rapidly across the world. Due to the large number of affected patients and heavy labor for doctors, computer-aided diagnosis with machine learning algorithm is urgently needed. In this study, we propose to conduct the diagnosis of COVID-19 with a series of features extracted from CT images.
  arXiv  Detail & Related papers  (2020-05-06T15:19:15Z)
• COVID-19 Chest CT Image Segmentation -- A Deep Convolutional Neural Network Solution [34.08284037107891]
  We establish a new deep convolutional neural network tailored for segmenting the chest CT images with COVID-19 infections. Inspired by the observation that the boundary of the infected lung can be enhanced by adjusting the global intensity, in the proposed deep CNN, we introduce a feature variation block. The proposed FV block can enhance the capability of feature representation effectively and adaptively for diverse cases.
  arXiv  Detail & Related papers  (2020-04-23T06:09:16Z)
• Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Images [152.34988415258988]
  Automated detection of lung infections from computed tomography (CT) images offers a great potential to augment the traditional healthcare strategy for tackling COVID-19. segmenting infected regions from CT slices faces several challenges, including high variation in infection characteristics, and low intensity contrast between infections and normal tissues. To address these challenges, a novel COVID-19 Deep Lung Infection Network (Inf-Net) is proposed to automatically identify infected regions from chest CT slices.
  arXiv  Detail & Related papers  (2020-04-22T07:30:56Z)

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.