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- W4283695255 abstract "Abstract Aim : The aim of this study was to develop dual segmentation models for poorly and well-differentiated hepatocellular carcinoma (HCC), using two-step transfer learning (TSTL) based on dynamic contrast-enhanced (DCE) computed tomography (CT) images. Methods : From 2013 to 2019, DCE CT images of 128 patients with 80 poorly differentiated and 48 well-differentiated HCCs were selected at our hospital. In the first transfer learning (TL) step, a pre-trained segmentation model with 192 CT images of lung cancer patients was retrained as a poorly differentiated HCC model. In the second TL step, a well-differentiated HCC model was built from a poorly differentiated HCC model. The average 3D Dice’s similarity coefficient (3D-DSC) and 95th-percentile of the Hausdorff distance (95% HD) were employed to evaluate the segmentation accuracy, based on a nested 4-fold cross-validation test. The DSC denotes the degree of regional similarity between the HCC reference regions and the regions estimated using the proposed models. The 95% HD is defined as the 95th-percentile of the maximum measures of how far two subsets of a metric space are from each other. Results : The average 3D-DSC and 95% HD were 0.849 ± 0.078 and 1.98 ± 0.71 mm, respectively, for poorly differentiated HCC regions, and 0.811 ± 0.089 and 2.01 ± 0.84 mm, respectively, for well-differentiated HCC regions. The average 3D-DSC for both regions was 1.2 times superior to that calculated without the TSTL. Conclusion : The proposed model using TSTL from the lung cancer dataset showed the potential to segment poorly and well-differentiated HCC regions on DCE CT images." @default.
- W4283695255 created "2022-06-30" @default.
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- W4283695255 date "2022-06-29" @default.
- W4283695255 modified "2023-09-25" @default.
- W4283695255 title "Dual segmentation models for poorly and well-differentiated hepatocellular carcinoma using two-step transfer deep learning on dynamic contrast-enhanced CT images" @default.
- W4283695255 doi "https://doi.org/10.21203/rs.3.rs-1749800/v1" @default.
- W4283695255 hasPublicationYear "2022" @default.
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