Korean J Radiol.  2023 Apr;24(4):294-304. 10.3348/kjr.2022.0588.

Deep Learning-Based Computed Tomography Image Standardization to Improve Generalizability of Deep Learning-Based Hepatic Segmentation

• Lee SB
• Hong Y
• Cho YJ
• Jeong D
• Lee J
• Yoon SH
• Lee S
• Choi YH
• Cheon JE

• KMID: 2541017
• DOI: http://doi.org/10.3348/kjr.2022.0588

Abstract

Objective
We aimed to investigate whether image standardization using deep learning-based computed tomography (CT) image conversion would improve the performance of deep learning-based automated hepatic segmentation across various reconstruction methods.
Materials and Methods
We collected contrast-enhanced dual-energy CT of the abdomen that was obtained using various reconstruction methods, including filtered back projection, iterative reconstruction, optimum contrast, and monoenergetic images with 40, 60, and 80 keV. A deep learning based image conversion algorithm was developed to standardize the CT images using 142 CT examinations (128 for training and 14 for tuning). A separate set of 43 CT examinations from 42 patients (mean age, 10.1 years) was used as the test data. A commercial software program (MEDIP PRO v2.0.0.0, MEDICALIP Co. Ltd.) based on 2D U-NET was used to create liver segmentation masks with liver volume. The original 80 keV images were used as the ground truth. We used the paired t-test to compare the segmentation performance in the Dice similarity coefficient (DSC) and difference ratio of the liver volume relative to the ground truth volume before and after image standardization. The concordance correlation coefficient (CCC) was used to assess the agreement between the segmented liver volume and ground-truth volume.
Results
The original CT images showed variable and poor segmentation performances. The standardized images achieved significantly higher DSCs for liver segmentation than the original images (DSC [original, 5.40%–91.27%] vs. [standardized, 93.16%–96.74%], all P < 0.001). The difference ratio of liver volume also decreased significantly after image conversion (original, 9.84%–91.37% vs. standardized, 1.99%–4.41%). In all protocols, CCCs improved after image conversion (original, -0.006–0.964 vs. standardized, 0.990–0.998).
Conclusion
Deep learning-based CT image standardization can improve the performance of automated hepatic segmentation using CT images reconstructed using various methods. Deep learning-based CT image conversion may have the potential to improve the generalizability of the segmentation network.

Keyword

Artificial intelligence; Automated segmentation; Image conversion; Quality control; Reproducibility