FV-Seg-Net: Fully Volumetric Network for Accurate Segmentation of COVID-19 Lesions from Chest CT Scans


Automated and precise pneumonia segmentation of COVID-19 extends the view of medical supply chains and offers crucial medical supplies to fight the COVID-19 pandemic. Deep learning plays a vital role in improving the COVID-19 segmentation from computed tomography (CT) scans. However, the literature lacks a precise segmentation approach on small-size lesions because they often split the CT scan into 2-D slices or 3-D patches, leading to the loss of contextual and/or global information. In order to address this, this article proposes a novel fully volumetric segmentation network, called FV-Seg-Net, that effectively exploits the local and global spatial information and enables the entire CT volume processing at once. The decoder is designed using a computationally efficient recalibrated anisotropic convolution module that can acquire the 3-D semantic representation of the CT volumes with anisotropic resolution. To avoid losing information during down-sampling, we reconstruct the skip-connection using a multilevel multiscale pyramid aggregation module and ensure more effective context fusion that improves the reconstruction capability of the decoder. Finally, stacked data augmentation (StackAug) is presented to magnify the training data and improve the generalizability of FV-Seg-Net. Proof of concept experiments on two public datasets demonstrates that the FV-Seg-Net achieves excellent segmentation performance (Dice score: 85.69 and a surface-dice: 84.79%), outperforming the current cutting-edge studies.

Publication DOI: https://doi.org/10.1109/tii.2022.3146175
Divisions: College of Business and Social Sciences > Aston Business School
College of Business and Social Sciences > Aston Business School > Operations & Information Management
Additional Information: © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Uncontrolled Keywords: 3D CT scans,Anisotropic Convolution,COVID-19,Computed tomography,Convolution,Deep Learning,Image segmentation,Lesions,Medical S upply chain,Solid modeling,Three-dimensional displays,Volumetric S egmentation,Control and Systems Engineering,Information Systems,Computer Science Applications,Electrical and Electronic Engineering
Publication ISSN: 1551-3203
Last Modified: 22 May 2024 16:01
Date Deposited: 14 Oct 2022 07:32
Full Text Link:
Related URLs: https://ieeexpl ... ocument/9894038 (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2023-03-01
Published Online Date: 2022-09-16
Accepted Date: 2022-01-02
Authors: Abdel-Basset, Mohamed
Hawash, Hossam
Chang, Victor (ORCID Profile 0000-0002-8012-5852)



Version: Accepted Version

| Preview

Export / Share Citation


Additional statistics for this record