Abstract
Assessing embryo quality through segmentation of blastocyst components is crucial, as embryo morphology directly correlates with its potential for implantation. However, automatic blastocyst segmentation remains a challenging task due to factors such as poor contrast, noise, and ambiguous boundaries between different tissue structures. In this study, we introduce a novel transformer-based architecture, termed BTFormer (Blastocyst Transformer), designed to effectively segment blastocyst components. Firstly, we use an axial-free attention mechanism with lower computational resources, which catches non-local feature maps with long-range cues to alleviate the mistake of local structure. Secondly, to enjoy the rotation consistency of the embryo images, we propose an axial-free attention block with a soft aggregation operation to embed features extracted by axial-free attention with different angles, which collect global cues and broadcast a diversified receptive field. We validated our method on a typical public dataset and achieved the state-of-the-art segmentation performance with accuracy, precision, recall, Dice coefficient, and Jaccard index of 93.86%, 91.81%, 92.25%, 92.02% and 85.45%. Extensive qualitative experimental results demonstrate the effectiveness of our proposed method.