Zebrafish is a commonly used model organism for heart disease research, and its larval cardiac is transparent and able to be directly observed under the microscope, and no mature and effective algorithm has yet been developed to automatically identify the zebrafish cardiac. In order to improve the accuracy and real-time efficiency of automatic segmentation of zebrafish ventricular images, and to solve the problem of weak boundaries of the ventricular region, insufficient feature extraction capability, and insufficient utilization of inter-frame correlation details, this thesis proposes EGAFB, a method for zebrafish ventricular images segmentation based on an edge-guided adaptive feature bank. The encoder is improved by embedding a rectangular self-calibration module, which extracts the global context information and enhances the ventricular feature extraction; meanwhile, the edge-guided attention mechanism is introduced to direct the model to focus on more boundary detail information, which strengthens the ventricular boundary identification ability; mean square error loss and classification confidence loss functions are introduced to optimise the local refinement segmentation mechanism, which improves the zebrafish ventricle recognition accuracy. The results show that the mean Intersection over Union (mIoU) of the EGAFB reaches 94.7%. Compared with the existing method Unet, the mIoU is improved by 4.6% and the inference time is reduced by 22.9%; compared with the original model, the mIoU is improved by 1.3% and the inference time is reduced by 6.4%. This thesis shows that the EGAFB method has high accuracy and real-time segmentation efficiency, which provides an effective solution for automatic segmentation of zebrafish ventricular images, as well as some more efficient technical support for the research of zebrafish cardiac disease models.