This paper presents an adversarial deep learning approach to improve abdominal organ segmentation in CT scans. The goal is to enhance segmentation techniques by using adversarial training for deep neural networks, specifically U-Net-generative adversarial networks. The study addresses challenges such as partial volume effects, image noise, and data asymmetry in deep learning segmentation methods. The proposed method uses advanced preprocessing techniques and model fusion to achieve multi-organ segmentation. The research demonstrates that even with limited computational resources, high-quality segmentation results can be achieved. The study also shows that the proposed design outperforms state-of-the-art approaches from a public competition. The paper highlights the importance of deep learning in medical imaging, particularly for organ segmentation, and addresses challenges such as the scarcity of annotated data, the need for accurate delineation for radiotherapy planning, and the difficulties in segmenting organs with varying sizes and geometries. The research aims to encourage more scientists to explore deep neural networks for medical imaging tasks, especially in low-resource settings. The study emphasizes the potential of adversarial learning in improving segmentation accuracy and the importance of developing robust, automated methods for organ delineation in medical imaging. The findings suggest that deep learning techniques can significantly enhance the accuracy and efficiency of abdominal organ segmentation in CT scans.This paper presents an adversarial deep learning approach to improve abdominal organ segmentation in CT scans. The goal is to enhance segmentation techniques by using adversarial training for deep neural networks, specifically U-Net-generative adversarial networks. The study addresses challenges such as partial volume effects, image noise, and data asymmetry in deep learning segmentation methods. The proposed method uses advanced preprocessing techniques and model fusion to achieve multi-organ segmentation. The research demonstrates that even with limited computational resources, high-quality segmentation results can be achieved. The study also shows that the proposed design outperforms state-of-the-art approaches from a public competition. The paper highlights the importance of deep learning in medical imaging, particularly for organ segmentation, and addresses challenges such as the scarcity of annotated data, the need for accurate delineation for radiotherapy planning, and the difficulties in segmenting organs with varying sizes and geometries. The research aims to encourage more scientists to explore deep neural networks for medical imaging tasks, especially in low-resource settings. The study emphasizes the potential of adversarial learning in improving segmentation accuracy and the importance of developing robust, automated methods for organ delineation in medical imaging. The findings suggest that deep learning techniques can significantly enhance the accuracy and efficiency of abdominal organ segmentation in CT scans.