MCCSeg: Morphological embedding causal constraint network for medical image segmentation

Medical image multi-object segmentation aims to accurately extract each object that is great significant for the medical image analysis. Although several methods based on deep learning have been widely developed, it is still challenging due to the similarities of different objects, the significant morphological variability, data heterogeneity, and the unbalanced distribution of samples. To address the above issues, we propose a novel Morphological Embedding Causal Constraint segmentation network (MCCSeg) for medical image multi-object segmentation. We build a hybrid CNN-Transformer encoder module to obtain local and global features. Specifically, a Causal Constraint Module (CCM) is proposed to decorrelate features by sample reweighting, which utilizes weight allocation to train the weight of training samples. The Random Fourier Features (RFF) is employed to solve nonlinear dependency problems and remove correlations between features to mitigate the influence of spurious correlations. The morphological guidance module (MG) is designed to extract the boundary as prior morphological information for enhancing feature representation, which is concatenated into the deep supervision module in the decoder for further optimizing feature extraction. The experiments demonstrate that MCCSeg outperforms other state-of-the-art methods, achieving improvements of 3.76% and 5.41% in terms of DICE and HD95 scores on Synapse dataset, respectively. The ablation experiments further verify the effectiveness of MCCSeg in improving generalization.