# 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  
This section specifically compares different networks in terms of liver/liver lesion segmentation. Deep convolutional neural networks (DCNNs) are robust and accurate for liver and large lesion segmentation but require architectural enhancements for subtle anomalies. They depend on large datasets and high computational resources, making them less suitable for resource-limited or small-dataset scenarios. FCNs efficiently classify individual pixels with a simple downsampling and upsampling structure. While effective for liver segmentation, they struggle with intricate or small lesions and often require post-processing for finer details. Encoder-decoder architectures are versatile for liver segmentation but need refinements, such as skip connections and advanced loss functions, to handle small, irregular lesions and address issues with boundary preservation, noisy datasets, and class imbalances. S-Net (Figure 1) is a lightweight, computationally efficient model suitable for liver segmentation and real-time applications, particularly in resource-constrained settings. However, its simplicity reduces accuracy in handling complex shapes, small lesions, or noisy datasets. While effective as a baseline for simpler tasks, S-Net is less competitive for detailed liver lesion segmentation. Enhancements like advanced loss functions and preprocessing can improve its performance. It excels in feature blending and low-level detail preservation but faces limitations in complex scenarios due to its shallow encoder and fixed structural design [42]. Dense V-Net (Figure 2) is a robust architecture for liver/liver lesion segmentation, excelling in 3D volumetric analysis. Its densely connected layers enhance feature extraction, gradient flow, and fine detail preservation, making it highly effective for complex and irregular liver lesions. Its reliance on extensive computational resources, large annotated datasets, and sensitivity to noise are its drawbacks faced in resource-limited scenarios. Proper preprocessing, data augmentation, and loss function customization can help Dense V-Net achieve strong performance. The network utilizes convolutions to both extract features from the data and reduce its resolution at the end of each stage by applying suitable strides. The structure features a compression path on the left side and a decompression path on the right, which restores the signal to its original size. All convolutions are performed with proper padding. The compression path on the left is organized into distinct stages, each operating at a specific resolution. These stages consist of one to three convolutional layers per stage [43]. UNet operates effectively with a small number of training samples and produces highly accurate segmentation outcomes. This network is stru  
Figure 1: Architectural overview for SNet. The encoder path extracts hierarchical features, while skip connections preserve spatial details and ensure information flow between layers. The bridge incorporates the convolutional block attention module. The decoder path reconstructs segmentation maps using up-convolution layers, progressively combining feature maps from corresponding encoder layers. Attention mechanisms enhance focus on relevant liver regions, ensuring precise segmentation. The output layer generates the final segmentation map, delineating the liver from surrounding tissues [22]. Figure 2: Architectural overview of Dense VNet. The downsampling path extracts multiscale features using dense blocks with batch normalization (BN) and ReLU activations, while the upsampling path reconstructs the segmentation map with feature refinement. Skip connections preserve spatial details by directly linking encoder and decoder features. The output layer generates the final segmented liver mask [43]. path that captures semantic or contextual details from the image, and an expansive path that reintegrates location information for each pixel, pinpointing their specific positions.