# Review of the AI-Based Analysis of Abdominal Organs from Routine CT Scans
## 2 Historical Evolution
### Deep Learning Revolution (2018-2024)
#### 2.3.12 Conditional GAN  
In 2021, [PERSON] et al. developed an advanced deep learning model for fully automated multi-organ segmentation from abdominal CT images, based on a conditional generative adversarial network (cGAN) framework. The model employs a discriminator to refine organ boundaries and a generator with cascaded, partially pre-trained convolutional encoder-decoder networks. By fine-tuning non-medical images, the model addresses data scarcity and achieves multi-level segmentation refinements using auto-context. It demonstrated exceptional performance, earning first place in liver CT, liver MR, and multi-organ MR segmentation at the CHAOS challenge, with Dice scores of 97.95%, 89.67%, 90.56%, and 84.70% for liver, spleen, right kidney, and left kidney segmentation, respectively [23]. 2.3.13. RetinaNet and U-Net  
In 2021, [PERSON] et al. presented a comprehensive framework combining two CNN models: RetinaNet for initial lesion detection and U-Net for detailed segmentation. The method incorporates image acquisition and pre-processing in segmentation refinement, achieving a Dice coefficient of 82.99% and a Matthews correlation coefficient (MCC) of 83.62%. This demonstrates the method's capability to handle the variability in lesion shapes and sizes, providing accurate and reliable segmentation results [24]. 2.3.14. Multiscale DL and Support Vector Machine (SVM)  
In 2021, [PERSON] et al. evaluated a multiscale deep learning algorithm aimed at the quantitative visualization and measurement of traumatic hemoperitoneum, comparing its diagnostic performance against categorical estimation methods. They employed Dice similarity coefficients (DSCs) to assess the performance of a three-dimensional (3D) U-Net and a coarse-to-fine deep learning method. Additionally, an SVM with a radial basis function was used to derive an optimal cutoff. The results showed that the mean DSC for the multiscale algorithm was 0.61, significantly outperforming the 0.32 achieved by the 3D U-Net and the 0.52 by the coarse-to-fine method, indicating a superior accuracy of the multiscale approach in detecting and quantifying traumatic hemoperitoneum [25]. 2.3.15. Recursive Cascaded Network (RCN)  
In 2021, [PERSON] et al. developed an enhanced unsupervised-learning-based framework for multi-organ registration on 3D abdominal CT images. They incorporated a coarse-to-fine RCN within a standard U-Net architecture to achieve precise 3D abdominal CT image multi-organ registration results. To ensure the integrity of the registered images, they integrated a topology-preserving loss into the total loss function, which helped to prevent distortion in the predicted transformations. The efficacy of their method was validated using four public databases, demonstrating high-precision clinical multi-organ registration results suitable for real-time applications. Their experimental results indicate a Dice score of 97.75% for multi-organ (liver, spleen, right kidney, and left kidney) segmentation [26]. 2.3.16. CNN  
In 2022, [PERSON] et al. developed an end-to-end pipeline for quantifying liver parenchymal disruption due to trauma using three-dimensional contrast-enhanced CT scans. The process involved generating segmentation masks for both normal and trauma-affected liver regions, leveraging expert knowledge to reduce false positives by incorporating typical liver trauma patterns. Volumes were then quantified to calculate the extent of parenchymal disruption. Utilizing deep convolutional neural networks trained and validated on a dataset from the University of Michigan Health System, the model achieved high accuracy for liver parenchyma (96.13%, 96.00%, 96.35%) and moderate accuracy for trauma regions (51.21%, 53.20%, 56.76%) [27]. 2.3.17. Swin-Unet  
In 2023, [PERSON] et al. proposed Swin-Unet, a Transformer-based U-shaped encoder-decoder architecture for medical image segmentation.