Thermal barrier coatings (TBCs) serve as the critical protective layer for gas turbine components. In complex service environments, the thermally grown oxide (TGO) layer formed between the top coat (TC) and the bond coat (BC), along with the evolution of its microscopic morphology, is a key factor determining the service life of TBCs. To address the complex morphology of TGO layers that challenges conventional segmentation methods, this paper proposes MSF-SA ResU-Net, a multi-scale feature fusion self-attention model based on the ResU-Net architecture, for the image segmentation of thermally grown oxide layers. Building upon residual connections, this model incorporates two key enhancement modules at the bottleneck layer: Multi-Scale Fusion (MSF) and Self-Attention (SA). During the feature processing stage, the MSF module effectively captures the multi-scale morphological features of the TGO layer through receptive fields of different sizes, solving the difficulty of extracting long-strip and irregular morphologies. Subsequently, the SA module recalibrates the fused features by calculating the global dependencies between them, enhancing the network's attention to TGO boundaries and micro-defects. Comprehensive experiments on an in-house dataset of 3,000 SEM images demonstrate that MSF-SA ResU-Net outperforms standard U-Net and contemporary segmentation architectures. Furthermore, due to the feature extraction capability of the ResNet backbone network, the model maintains high accuracy and exhibiting good convergence performance. This makes the architecture an effective solution for the microscopic analysis of thermal barrier coatings.