# LAAVOS: A DeAOT-Based Approach for Medaka Larval Ventricular Video Segmentation
## 2 The LAAVOS Model
### The LA-ResNet Encoder  
In the task of medaka heart video segmentation, segmentation models are prone to being influenced by various factors. For instance, the lighting and background vary across different videos. Additionally, the color changes as blood flows through blood vessels, atria, and ventricles. Moreover, organs and tissues similar to the ventricle, as well as overlapping organs in some video frames, further complicate the task. The close proximity of the ventricle and atrium in medaka leads to unclear boundaries. Additionally, some video frames suffer from blurring due to imaging issues or the fish's movement. These factors significantly interfere with the model's feature extraction and interpretation. To address these challenges, the model requires stronger feature extraction capabilities. Therefore, we embedded an attention-enhanced encoder into the model's architecture. This enables the network to prioritize and concentrate on key target regions during feature extraction while reducing the impact of various types of noise. This attention-enhanced encoder incorporates the local importance attention mechanism into the bottleneck module of ResNet-50 [10]. We refer to the bottleneck module embedded with the LA attention mechanism [11] as LA-BTNK and the improved encoder as LA-ResNet. As illustrated in Figure 2, the encoding process begins with a stem module consisting of a 7 \(\times\) 7 convolution with stride 2, followed by batch normalization, ReLU activation, and a 3 \(\times\) 3 max pooling layer, effectively reducing the spatial resolution while retaining essential low-level features. Following this, the layers module comprises a sequence of Local Attention Bottleneck Blocks (LA-BTNKs), each of which contains a dimensionality-reducing 1 \(\times\) 1 convolution, a 3 \(\times\) 3 convolution applied to the reduced channels, and a final 1 \(\times\) 1 convolution to restore the original channel dimensions. A Local Attention module is embedded within each LA-BTNK to generate spatial importance maps by emphasizing task-relevant regions and suppressing noise and background clutter. The attention-enhanced features are then reintegrated into the residual path via a skip connection and non-linear activation. Through this hierarchical structure, the LA-ResNet encoder effectively captures semantic representations, which serve as robust feature maps for subsequent feature propagation and decoding stages. The Local Importance Attention (LA) Module  
The core of LA attention mechanisms is to adaptively enhance useful information while suppressing irrelevant information based on the importance of input correlations. In existing research, importance maps are calculated using subnetworks or matrix multiplications. Examples include ESA (first order: element-wise multiplication) [12] and self-attention (second order: two matrix multiplications) [13]. While ESA suffers from weak performance, self-attention has high spatial complexity. Other approaches, such as the LA-layer [14], adopt deformable local attention with learnable offsets and Q-K-V-based aggregation, aiming to enhance flexibility and receptive field adaptation. However, this design introduces additional computational and memory overheads, along with parameter tuning, which still impacts real-time performance. In contrast, our LA attention mechanism adopts a simplified second-order spatial attention strategy that achieves a better balance between efficiency and segmentation accuracy. The Local Attention module adopts a three-branch architecture that integrates the main feature path, the attention generation path, and the guided gating path. This design enhances the model's ability to focus on informative regions while suppressing irrelevant background responses. As illustrated in Figure 3, the left part of the diagram presents the overall structure composed of three key computational branches, while the right part highlights the SoftPool operation used in the attention generation path.