# LAAVOS: A DeAOT-Based Approach for Medaka Larval Ventricular Video Segmentation
###### Abstract  
Accurate segmentation of the ventricular region in embryonic heart videos of medaka fish (_Oryzaias latipes_) holds significant scientific value for research on heart development mechanisms. However, existing medaka ventricular datasets are overly simplistic and fail to meet practical application requirements. And the video frames contain multiple complex interfering factors, including optical interference from the filming environment, dynamic color changes caused by blood flow, significant diversity in ventricular scales, image blurring in certain video frames, high similarity in organ structures, and indistinct boundaries between the ventricles and atria. These challenges mean existing methods still face notable technical difficulties in medaka embryonic ventricular segmentation tasks. To address these challenges, this study first constructs a medaka embryonic ventricular video dataset containing 4200 frames with pixel-level annotations. Building upon this, we propose a semi-supervised video segmentation model based on the hierarchical propagation feature decoupling framework (DeAOT) and innovatively design an architecture that combines the LA-ResNet encoder with the AFPViS decoder, significantly improving the accuracy of medaka ventricular segmentation. Experimental results demonstrate that, compared to the traditional U-Net model, our method achieves a 13.48% improvement in the mean Intersection over Union (mIoU) metric. Additionally, compared to the state-of-the-art DeAOT method, it achieves a notable 4.83% enhancement in the comprehensive evaluation metric Jaccard and F-measure (J&F), providing reliable technical support for research on embryonic heart development. v 2025 15 May 2025 4 June 2025 15 May 2025 4 June  
treatment responses. EF, in particular, is a well-established predictor of mortality and major adverse cardiac events. In preclinical models such as medaka and zebrafish, EF serves as a critical quantitative measure in both developmental biology and pharmacological screening. Therefore, high-throughput and reproducible segmentation of the ventricle is vital for supporting large-scale analysis and drug discovery. To address this need, [PERSON] et al. [3] explored traditional image processing techniques such as edge detection, background subtraction, color filtering, and histogram-based segmentation to identify ventricular contours in zebrafish videos. However, these approaches lack robustness when applied to medaka data, especially under conditions of poor image quality or indistinct ventricular boundaries. In recent years, deep learning methods have shown tremendous promise in biomedical image segmentation. For instance, [PERSON] et al. [4] developed the CFIN framework based on SegNet, which automatically segments cardiac regions and estimates functional parameters such as ventricular volume using high-resolution fluorescent microscopy. The U-Net [5] architecture has also demonstrated excellent segmentation performance. [PERSON] et al. [6] employed U-Net in the HeartSeg framework for ventricular segmentation and size quantification in medaka. Similarly, [PERSON] et al. [3] introduced the ZACAF (Zebrafish Automatic Cardiovascular Assessment Framework) framework, which applies U-Net to grayscale videos acquired through standard microscopy for automatic segmentation and evaluation of EF and fractional shortening (FS) in zebrafish embryos. Nevertheless, these models do not explicitly incorporate temporal dependencies between frames, resulting in segmentation inconsistencies during dynamic cardiac cycles. Moreover, they typically rely on high-quality data captured under controlled laboratory conditions, limiting their generalizability to diverse imaging environments. [PERSON] et al. [7] were the first to apply video segmentation techniques [8] to medaka ventricular analysis.