# Dynamic Image-Text Alignment (DyITA)

P2Echo folds multi-view echocardiography segmentation into a single network that takes a structured text prompt naming the view and target structure and uses it to modulate decoder features. Previously each standard acoustic window typically needed its own specialist model, or relied on implicit priors that were hard to control explicitly. On internal data, P2Echo improves over EchoONE, the other multi-view model, by 5.3, 1.1 and 2.8 Dice on 2CH, 3CH and 4CH, is comparable on PSAX, and holds competitive results on three external datasets at 6.90 GFLOPs.

P2Echo folds multi-view echocardiography segmentation into a single network that takes a structured text prompt naming the view and target structure and uses it to modulate decoder features. Previously each standard acoustic window typically needed its own specialist model, or relied on implicit priors that were hard to control explicitly. On internal data, P2Echo improves over EchoONE, the other multi-view model, by 5.3, 1.1 and 2.8 Dice on 2CH, 3CH and 4CH, is comparable on PSAX, and holds competitive results on three external datasets at 6.90 GFLOPs.