SAID-Net: enhancing segment anything model with implicit decoding for echocardiography sequences segmentation.

Abstract

Echocardiography sequence segmentation is vital in modern cardiology. While the Segment Anything Model (SAM) excels in general segmentation, its direct use in echocardiography faces challenges due to complex cardiac anatomy and subtle ultrasound boundaries. We introduce SAID (Segment Anything with Implicit Decoding), a novel framework integrating implicit neural representations (INR) with SAM to enhance accuracy, adaptability, and robustness. SAID employs a Hiera-based encoder for multi-scale feature extraction and a Mask Unit Attention Decoder for fine detail capture, critical for cardiac delineation. Orthogonalization boosts feature diversity, and I $_{}^2$ Net improves handling of misaligned contextual features. Tested on CAMUS and EchoNet-Dynamics datasets, SAID outperforms state-of-the-art methods, achieving a Dice Similarity Coefficient (DSC) of 93.2% and Hausdorff Distance (HD95) of 5.02 mm on CAMUS, and a DSC of 92.3% and HD95 of 4.05 mm on EchoNet-Dynamics, confirming its efficacy and robustness for echocardiography sequence segmentation.