FreeEcho: Learning Prompt-Free Echocardiography Video Segmentation via Distillation

Echocardiography video segmentation delineates cardiac structures across frames to support quantitative assessment. Prompt-free models enable automated segmentation but remain limited in accuracy and generalization across data sources, whereas prompt-based models achieve stronger performance by relying on external localization inputs that hinder scalable inference. We present FreeEcho, a knowledge distillation framework that converts existing prompt-based echocardiography video segmentation models into prompt-free counterparts. FreeEcho adapts the model prediction pipeline, combines ground-truth supervision with teacher-derived distillation signals, and employs a prompt removal curriculum during training. Experiments across multiple models and public datasets show that the distilled students consistently approach or achieve their teachers' performance while substantially outperforming direct prompt removal. They also outperform existing prompt-free models, with clear advantages in generalization across different data sources. Ablation studies clarify the effects of distillation design choices.