BOTM: Echocardiography Segmentation via Bi-directional Optimal Token Matching
This addresses segmentation challenges in echocardiography for medical imaging applications, offering incremental improvements in anatomical consistency.
The paper tackles anatomical inconsistency in echocardiography segmentation by proposing BOTM, a framework that performs segmentation and optimal anatomy transportation simultaneously, achieving stable and accurate results with improvements like -1.917 HD on CAMUS2H LV and +1.9% Dice on TED.
Existed echocardiography segmentation methods often suffer from anatomical inconsistency challenge caused by shape variation, partial observation and region ambiguity with similar intensity across 2D echocardiographic sequences, resulting in false positive segmentation with anatomical defeated structures in challenging low signal-to-noise ratio conditions. To provide a strong anatomical guarantee across different echocardiographic frames, we propose a novel segmentation framework named BOTM (Bi-directional Optimal Token Matching) that performs echocardiography segmentation and optimal anatomy transportation simultaneously. Given paired echocardiographic images, BOTM learns to match two sets of discrete image tokens by finding optimal correspondences from a novel anatomical transportation perspective. We further extend the token matching into a bi-directional cross-transport attention proxy to regulate the preserved anatomical consistency within the cardiac cyclic deformation in temporal domain. Extensive experimental results show that BOTM can generate stable and accurate segmentation outcomes (e.g. -1.917 HD on CAMUS2H LV, +1.9% Dice on TED), and provide a better matching interpretation with anatomical consistency guarantee.