2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDCOVID-IP-1.2
Paper Title DEEP ACTIVE LEARNING FOR FIBROSIS SEGMENTATION OF CHEST CT SCANS FROM COVID-19 PATIENTS
Authors Xiaohong Liu, Tsinghua University, China; Kai Wang, University of California, San Diego, United States; Ting Chen, Tsinghua University, China
SessionCOVID-IP-1: COVID Related Image Processing 1
LocationArea C
Session Time:Monday, 20 September, 15:30 - 17:00
Presentation Time:Monday, 20 September, 15:30 - 17:00
Presentation Poster
Topic COVID-Related Image Processing: COVID-related image processing
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract During the ongoing COVID-19 outbreak, it is critical to assess patients' disease progression with COVID-19 pneumonia by computed tomography (CT). As most of the works focused on ground-glass opacity and consolidation segmentation of COVID-19 on CT images, lung fibrosis is relatively undervalued and less studied. Automatic segmentation and accurate measurement of lung fibrosis can potentially aid treatment planning for patients of post-COVID-19 pneumonia. However, the lack of sufficient training data hinders the fibrosis segmentation of CT images. Also, redundancy among CT images can reduce annotating efficiency. To address these issues, we propose deep active learning (AL) framework, which consists of a segmentation model called UNet-RGD, and a novel acquisition method named DeepRISS. The segmentation model consists of improved structures of residual blocks, channel gates, and dropout layers. The deep learning-based acquisition method combines uncertainty estimation and clustering for selecting representative and informative samples. Experimental results show that the AL framework can achieve state-of-the-art performance and effectively reduce the number of selected samples, saving the annotation cost by 25% to 44% compared to the non-selective approach.