EISC: Enforcing Consistency Across Different Scales for Metallographic Image Segmentation

Metallographic image segmentation underpins automated metallographic analysis, yet pixel-level annotation is costly. Metallographic microstructures exhibit dramatic size differences, including cross-scale structures such as large-scale matrix phases, grain boundary cementite networks, and acicular Widmanstätten structures. Existing feature extraction modules cannot balance the semantic integrity of large regions and the fine details of microstructures, causing missed small microstructures and blurred segmentation boundaries. Current semi-supervised methods only impose consistency constraints on perturbed input images at the final prediction output, ignoring multi-scale semantic features from decoder upsampling stages. This leads to noisy supervision signals, low-quality pseudo-labels, and poor generalization. To address these issues, this paper proposes a semi-supervised metallographic image segmentation model, EISC, integrating MT cross-network scale feature extraction and decoder multi-scale consistency constraints. It adaptively fuses multi-receptive-field features and introduces a regularization term to maintain the semantic consistency of multi-scale decoder outputs, improving pseudo-label quality. Comparative and ablation experiments verify that EISC effectively enhances the segmentation accuracy and robustness of metallographic images.