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DW-UNet: Loss Balance under Local-Patch for 3D Infection Segmentation from COVID-19 CT Images.
Chen, Cheng; Zhou, Jiancang; Zhou, Kangneng; Wang, Zhiliang; Xiao, Ruoxiu.
  • Chen C; School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China.
  • Zhou J; Department of Critical Care Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, China.
  • Zhou K; School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China.
  • Wang Z; School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China.
  • Xiao R; School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China.
Diagnostics (Basel) ; 11(11)2021 Oct 20.
Article in English | MEDLINE | ID: covidwho-1480630
ABSTRACT
(1)

Background:

COVID-19 has been global epidemic. This work aims to extract 3D infection from COVID-19 CT images; (2)

Methods:

Firstly, COVID-19 CT images are processed with lung region extraction and data enhancement. In this strategy, gradient changes of voxels in different directions respond to geometric characteristics. Due to the complexity of tubular tissues in lung region, they are clustered to the lung parenchyma center based on their filtered possibility. Thus, infection is improved after data enhancement. Then, deep weighted UNet is established to refining 3D infection texture, and weighted loss function is introduced. It changes cost calculation of different samples, causing target samples to dominate convergence direction. Finally, the trained network effectively extracts 3D infection from CT images by adjusting driving strategy of different samples. (3)

Results:

Using Accuracy, Precision, Recall and Coincidence rate, 20 subjects from a private dataset and eight subjects from Kaggle Competition COVID-19 CT dataset tested this method in hold-out validation framework. This work achieved good performance both in the private dataset (99.94-00.02%, 60.42-11.25%, 70.79-09.35% and 63.15-08.35%) and public dataset (99.73-00.12%, 77.02-06.06%, 41.23-08.61% and 52.50-08.18%). We also applied some extra indicators to test data augmentation and different models. The statistical tests have verified the significant difference of different models. (4)

Conclusions:

This study provides a COVID-19 infection segmentation technology, which provides an important prerequisite for the quantitative analysis of COVID-19 CT images.
Keywords

Full text: Available Collection: International databases Database: MEDLINE Type of study: Prognostic study Language: English Year: 2021 Document Type: Article Affiliation country: Diagnostics11111942

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Prognostic study Language: English Year: 2021 Document Type: Article Affiliation country: Diagnostics11111942