# LAAVOS: A DeAOT-Based Approach for Medaka Larval Ventricular Video Segmentation
## 2 The LAAVOS Model
### The AFPVis Decoder  
#### 2.2.1 Progressive Multi-Scale Feature Fusion  
In medaka videos, the size of the ventricular target exhibits significant dynamic variation. Specifically, differences in the shooting environments of different videos lead to notable diversity in the scale of the ventricular target. Within the same video, the size of the ventricular target changes regularly due to the periodic beating of the medaka heart. This multi-scale dynamic characteristic introduces additional challenges for the video segmentation task, demanding that the model possess stronger scale adaptability and temporal modeling capabilities.  
In applications such as object localization, semantic segmentation, and video object segmentation, feature pyramid structures are commonly employed to address scale variations. Among these, FPN [18] is the most widely used feature pyramid structure. FPN employs a top-down pathway to propagate semantic-rich features from higher levels to lower levels, facilitating multi-level feature integration. However, this process often fails to achieve true fusion between high-level and low-level features, leading to the degradation and loss of semantic information. This ultimately results in blurred edges in the segmenta  
Figure 3: Structure of the LA module. The red, black, and blue arrows represent the main feature path, attention generation path, and guided gating path, respectively. The right part illustrates the implementation of SoftPool, where Exp denotes exponential mapping, GAP7 indicates local average pooling (stride = 3), and Inverse refers to inverse normalization.  
tion of the medaka ventricle, sometimes causing the model to fail to distinguish between the ventricle and adjacent tissues.  
To resolve this limitation, the proposed approach introduces a progressive feature pyramid network (AFPViS) decoder in the decoder part of the LAAVOS network. Inspired by AFPN [19], the AFPViS structure introduces a denser feature fusion mechanism. As illustrated in Figure 4, the feature propagation layer of LAAVOS generates a collection of multi-scale features, encompassing low-level, mid-level, and high-level representations, denoted as [F1, F2, F3]. These features are then progressively integrated by AFPViS, which first fuses the low-level and mid-level features before incorporating the high-level features. Through this fusion mechanism, the semantic disparity across multi-level features is progressively minimized, preventing the degradation and loss of semantic information.  
After the feature fusion step, the enhanced multi-scale features [P3, P4, P5] are progressively integrated through a three-level feature refinement process. First, the high-level feature P5 is compressed in channels via a 1 \(\times\) 1 convolution and added to the global semantic feature. The semantic information is then refined through a 3 \(\times\) 3 convolution. Next, bilinear interpolation is used to up-sample to the resolution of P4, aligning channels and performing spatial-weighted fusion with the mid-level features from the adapter to restore detailed features. Finally, another up-sampling to the resolution of P4 is performed, and the result is added to the low-level high-resolution features for further refinement. A 3 \(\times\) 3 convolution improves the local detail representation, and a 1 \(\times\) 1 convolution outputs the pixel-wise classification prediction map.