LAAVOS: A DeAOT-Based Approach for Medaka Larval Ventricular Video Segmentation

Accurate segmentation of the ventricular region in embryonic heart videos of medaka fish (Oryzias 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.