Cross-Vendor CT Image Data Harmonization Using CVH-CT
- URL: http://arxiv.org/abs/2110.09693v1
- Date: Tue, 19 Oct 2021 02:15:26 GMT
- Title: Cross-Vendor CT Image Data Harmonization Using CVH-CT
- Authors: Md Selim, Jie Zhang, Baowei Fei, Guo-Qiang Zhang, Gary Yeeming Ge, Jin
Chen
- Abstract summary: How to harmonize CT image data captured using different scanners is vital in cross-center large-scale radiomics studies.
We propose a novel deep learning approach called CVH-CT for harmonizing CT images captured using scanners from different vendors.
- Score: 9.920558110069221
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: While remarkable advances have been made in Computed Tomography (CT), most of
the existing efforts focus on imaging enhancement while reducing radiation
dose. How to harmonize CT image data captured using different scanners is vital
in cross-center large-scale radiomics studies but remains the boundary to
explore. Furthermore, the lack of paired training image problem makes it
computationally challenging to adopt existing deep learning models. %developed
for CT image standardization. %this problem more challenging. We propose a
novel deep learning approach called CVH-CT for harmonizing CT images captured
using scanners from different vendors. The generator of CVH-CT uses a
self-attention mechanism to learn the scanner-related information. We also
propose a VGG feature-based domain loss to effectively extract texture
properties from unpaired image data to learn the scanner-based texture
distributions. The experimental results show that CVH-CT is clearly better than
the baselines because of the use of the proposed domain loss, and CVH-CT can
effectively reduce the scanner-related variability in terms of radiomic
features.
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