Interpretable Model for Clinical Use in Left Atrial Appendage Segmentation via an Optimised Deformable-Attention...

Accurate segmentation of the left atrial appendage (LAA) is essential for device occlusion planning in atrial fibrillation patients who cannot receive anticoagulation. Yet 3D echocardiography suffers from low signal-to-noise ratio, anisotropy and marked morphological variability, increasing overfitting risk and reliance on operator-dependent post-processing. Existing U-Net variants capture local detail but often miss long-range dependencies, while transformers improve context at the cost of boundary precision; semi-automated pipelines still require experts. We present a fully automated, AERO-optimised DAT-DAD U-Net with SE-augmented skip fusion. Deformable attention transformers (DAT) provide content-adaptive global context, while a dual attention with deformable convolution (DAD) block refines rims and addresses shape irregularities; spatial-channel squeeze-and-excitation improves multi-scale fusion. Hyperparameters are selected by AERO, a surrogate- and multi-fidelity-driven optimiser balancing exploration and exploitation under limited data in practice. Validation used a 22-patient 3D echocardiography cohort from King's College Hospital. Volumes were reformatted into axial 2D slices, trained with on-the-fly anatomy-preserving augmentation and evaluated using strict patient-wise splits. The model achieved Dice 0.8925 ± 0.0144, IoU 0.8026 ± 0.0156 and HD95 9.14 ± 1.96 mm. Ablations confirmed additive gains from DAT, DAD and SE, with faster convergence and a lower error floor, supporting operator-light, time-sensitive LAA workflows.