Alleviating Low-Level Information Loss in Multi-Scale Blocks for Effective Posterior Extraocular Muscles Segmentation

Accurate segmentation of the posterior extraocular muscles is important for diagnosing thyroid-associated orbitopathy. Given that this region occupies a small area in computed tomography images, effectively leveraging low-level information within high-resolution feature maps is crucial. However, such information is often lost in conventional networks because of the excessive expansion of the receptive field in multi-scale blocks, leading to less accurate pixel-wise classification. To address these challenges, we propose a layer-wise residual-learning approach for multi-scale blocks. The residual connections are integrated between each layer within the stacked convolutional layers of the multi-scale block, thereby preserving low-level information and enhancing the high-level feature extraction for small objects. The proposed model was evaluated using computed tomography images from 232 patients at Chung-Ang University Hospital (2009–2022) and compared with 11 state-of-the-art networks. Four datasets corresponding to the medial, lateral, superior, and inferior rectus muscles of the left eye, using six measures, were used for this evaluation. The results showed that proposed model achieved the highest performance in 15 of the 24 cases and the second-highest performance in eight cases. Our ablation study and qualitative analysis demonstrate the efficacy of the proposed model in segmenting small muscle regions.