# Review of the AI-Based Analysis of Abdominal Organs from Routine CT Scans
## 4 Cons and Pros of Each Network for Liver or Liver Lesion Segmentation
Conditional GANs (cGANs) excel in liver lesion segmentation, effectively capturing complex shapes and fine details. They are robust against noise and class imbalance, making them suitable for challenging datasets. Their training complexity, resource requirements, and reliance on paired data are drawbacks. Figure 5 shows an architectural overview for cGAN. Pix2 Pix, with its adversarial training, excels in capturing intricate details and irregular shapes for liver/lesion segmentation. However, its dependency on paired data, training instability, and high resource requirements limit its practicality. Swin-Unet, leveraging attention-based and multiscale feature representation, is highly effective for detecting small and irregular lesions while preserving liver boundaries. Despite its accuracy, it demands significant computational resources and longer training times, making it less ideal for resource-constrained settings. Figure 6 shows the architectural overview for Swin-Unet GAN. MSL-UNet offers an efficient and accurate solution for liver/lesion segmentation, excelling in resource-constrained or real-time scenarios. Its multiscale feature extraction performs well on small, irregular lesions, though it may not match the precision of advanced models like Swin-Unet or Dense V-Net. It is ideal for lightweight deployments and serves as a strong baseline for tasks requiring multiscale representation. Table 3 demonstrates the comparison of different layers of U-Net-based networks. \begin{table}
\begin{tabular}{c c c} \hline
**Pros** & **Method** & **Cons** \\ \hline \multirow{2}{*}{DCNNs} & High accuracy and robustness for liver segmentation. Effective for larger lesions. & Struggles with small or subtle lesions. \\  & & Requires large datasets and significant \\ \hline \multirow{2}{*}{Fully Convolutional Networks (FCNs)} & Simple and computationally efficient for liver segmentation. & Poor performance on small or intricate \\  & & lesions. Requires post-processing for \\  & & fine details. \\ \hline \multirow{2}{*}{Encoder-decoder architectures} & \multirow{2}{*}{Simple, flexible, and efficient.} & Struggles with small or irregular lesions \\  & & and noisy datasets. Needs \\  & & enhancements for lesions. \\ \hline \multirow{2}{*}{S-Net} & Lightweight and efficient. Ideal for resource-constrained or real-time applications. & Reduced accuracy for complex shapes \\  & & applications. \\ \hline \end{tabular}
\end{table} 
Table 3: Pros and cons of networks for liver/liver lesion segmentation. Figure 6: Architectural overview for Swin-Unet. For applications requiring a detection-first approach, a two-stage framework such as RetinaNet combined with U-Net is an effective choice, allowing initial lesion localization followed by high-precision segmentation. However, when simultaneous segmentation and detection are needed, models like RA-UNet and conditional GANs (cGANs) provide an end-to-end solution that captures both global liver structures and local lesion characteristics. In cases where generalization to unseen data is a priority, Swin-Unet--a Transformer-based architecture--offers superior performance by capturing long-range dependencies and fine anatomical details. Finally, for applications focused on lesion quantification, particularly in trauma cases, multiscale deep learning combined with SVM-based classification provides a robust framework for measuring lesion severity and extent Table 4. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Approach** & **Strengths** \\ \hline RetinaNet + U-Net & Detection + segmentation & Two-stage approach for high accuracy \\ \hline cGANs (Conditional GANs) & Joint segmentation and detection & Robust to noise and domain shifts \\ \hline Multiscale DL + SVM & Multi-task learning & Quantifies injury severity \\ \hline RA-UNet & Attention-based segmentation and detection & High accuracy, fewer false positives \\ \hline Swin-Unet & Transformer-based model & Captures global and local details \\ \hline \hline \end{tabular}
\end{table}