# 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
The design features symmetric paths, forming a U-shaped architecture. Enhancing the network's depth, adding more skip connections, and incorporating dropout layers can further improve results. Additionally, it significantly shortens the training duration by decreasing the number of filters in each convolutional block, which in turn reduces the overall count of trainable parameters. U-Net is a reliable baseline for liver segmentation due to its simplicity, efficiency, and effectiveness. For liver lesion segmentation, which often involves small or complex structures, enhancements such as attention mechanisms, modified loss functions, or other architectures' integration with modern architectures are required [44]. Figure 3 shows an architectural overview of U-Net. U-Net++ builds on the traditional U-Net by introducing nested and dense skip connections, which improve accuracy and robustness for liver/liver lesion segmentation tasks. This architecture is particularly effective for detecting small and complex lesions due to its refined feature extraction and multiscale representation capabilities. However, its added complexity increases computational demands and training times. U-Net++ performs exceptionally well for liver segmentation and excels in capturing irregular shapes and small details in lesion segmentation with careful optimization. UNet3+'s increased complexity and resource demands necessitate careful planning and optimization. With large and diverse datasets, U-Net 3+ delivers accurate results, while smaller datasets require regularization techniques to mitigate overfitting. Table 2 demonstrates the comparison of U-Net, U-Net++, and U-Net3+ layers: \begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Feature/Layer** & **U-Net** & **U-Net++** & **U-Net 3+** \\ \hline \multirow{3}{*}{Convolutional Layers} & Encoder and decoder use \(3\times 3\) convolutions with ReLU activation & Similar to U-Net with added \(3\times 3\) convolutions in nested skip connections & Similar to U-Net; used in both encoder and decoder for feature fusion. \\ \hline Activation Function & ReLU & ReLU & ReLU \\ \hline \multirow{3}{*}{Pooling} & \(2\times 2\) max-pooling in the encoder for downsampling & \(2\times 2\) max-pooling in the encoder for downsampling & \(2\times 2\) max-pooling in the encoder for downsampling \\ \hline \multirow{3}{*}{Upsampling} & Transposed convolutions for spatial resolution recovery & Transposed convolutions for spatial resolution recovery & Bilinear upsampling or transposed convolutions \\ \hline \multirow{3}{*}{Skip Connections} & Direct connections between corresponding encoder and decoder levels & Nested skip connections with intermediate convolutional layers & Full-scale skip connections integrating features from all encoder and decoder levels \\ \cline{1-1} \cline{2-4}  & & & \\ \cline{1-1} \cline{2-4}  & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Layer summary of U-Net, U-Net++, and U-Net 3+ architectures. Figure 3: Architectural overview for U-Net. The contracting path captures spatial and contextual information through convolutional layers and max-pooling. Skip connections link corresponding layers in the contracting and expanding paths, preserving fine-grained spatial details for better segmentation accuracy. The expanding path reconstructs the segmentation map with up-convolutions. The bottom block represents the network’s bottleneck, extracting high-level features. The output layer generates a binary mask or multi-class segmentation map for the liver region. RA-UNet excels in detailed liver/lesion segmentation, using residual connections and attention mechanisms for high accuracy, especially with small or irregularly shaped lesions. While computationally demanding, it performs exceptionally well in resource-rich environments with high-quality datasets, delivering excellent results with appropriate tuning and preprocessing. Figure 4 shows the architectural overview for RA-UNet.