UC Irvine

Deep learning-based models have revolutionized biomedical image and video segmentation, enabling precise and automated analysis of complex structures. This advancement is particularly critical in the study of zebrafish cardiovascular videos, where accurate segmentation of the heart is essential for understanding cardiac function and development. In this work, we focus on the Zebrafish Automated Cardiac Analysis Framework (ZACAF), utilizing the U-net architecture to achieve high-accuracy segmentation of the zebrafish heart from video frames to calculate important cardiovascular parameters.After multiple collaborations with researchers studying different phenotypes in zf using ZACAF, to enhance the generalizability of our model across varying datasets and conditions, we employed transfer learning techniques, leveraging pre-trained models to adapt to new data efficiently. Additionally, we incorporated Test-Time Augmentation (TTA) to further improve model robustness and accuracy by applying various transformations to the input data during inference. This approach proposed a systematic solution for adopting ZACAF in broader genetic studies using deep learning algorithms. Recognizing the importance of temporal dynamics in video data, we extended our work to integrate temporal features into the segmentation model. By analyzing changes between consecutive frames, we aim to capture the heartbeat dynamics more effectively, providing a comprehensive tool for cardiac analysis in zebrafish embryos. Our approach not only advances the field of biomedical video segmentation but also contributes to the broader understanding of cardiac function in developmental biology.