Abnormal structures in multi-modality medical images often lead to heterogeneous heavy-tailed distributions. However, traditional models, especially those relying on Gaussian distributions, struggle to effectively capture these outliers. To address this, we propose BayeSMM, a novel framework that leverages Student’s t distribution mixture models (SMM) to simultaneously perform registration and segmentation for misaligned multi-modality medical images. Specifically, we construct a Bayesian Student’s t mixture model incorporating the heavy-tailed nature of the Student’s t distribution and develop variational inference to optimize the model. Guided by variational inference, we design a novel deep learning architecture that performs registration and segmentation jointly. We demonstrate the effectiveness of BayeSMM with experiments on the MS-CMR dataset, where the results show superior performance compared to existing combined computing methods, and yield enhanced robustness under the simulated heavy-tailed setting. The code is available at https://github.com/HenryLau7/BayeSMM .