STAVOS: A Medaka Larval Cardiac Video Segmentation Method Based on Deep Learning

Abstract

Medaka (Oryzias latipes), as a crucial model organism in biomedical research, holds significant importance in fields such as cardiovascular diseases. Currently, the analysis of the medaka ventricle relies primarily on visual observation under a microscope, involving labor-intensive manual operations and visual assessments that are cumbersome and inefficient for biologists. Despite attempts by some scholars to employ machine learning methods, limited datasets and challenges posed by the blurred edges of the medaka ventricle have constrained research to relatively simple tasks such as ventricle localization and heart rate statistics, lacking precise segmentation of the medaka ventricle edges. To address these issues, we initially constructed a video object segmentation dataset comprising over 7000 microscopic images of medaka ventricles. Subsequently, we proposed a semi-supervised video object segmentation model named STAVOS, incorporating a spatial-temporal attention mechanism. Additionally, we developed an automated system capable of calculating various parameters and visualizing results for a medaka ventricle using the provided video. The experimental results demonstrate that STAVOS has successfully achieved precise segmentation of medaka ventricle contours. In comparison to the conventional U-Net model, where a mean accuracy improvement of 0.392 was achieved, our model demonstrates significant progress. Furthermore, when compared to the state-of-the-art Tackling Background Distraction (TBD) model, there is an additional enhancement of 0.038.