Residual U-Structure Nested Conditional Adversarial Nets Colorized CT Improves Deep Learning Based Abdominal Multi-Organ Segmentation

Segmentation of abdominal organs such as the liver, pancreas, spleen, and kidneys plays an essential role in diagnosing and treating abdominal diseases. Although numerous deeply learned segmentation methods work well, they still suffer from partial volume effects, image noise, and data imbalance. This study aims to colorize CT images to boost or augment these segmentation approaches. We propose new residual U-structure nested generative adversarial nets that use residual U-blocks and spectral normalization for CT image colorization. Generated color CT images were introduced to train and validate V-Net and DenseV-Net for multiple abdominal organ segmentation. The experimental results demonstrate that colorized CT images can improve the dice similarity coefficient and reduce the Hausdorff distance from (0.32, 302.7) to (0.67, 78.2), significantly boosting the performance of V-Net and Dense V-Net for multiple abdominal organ segmentation.