A label-aware diffusion model for weakly supervised deformable registration of multimodal MRI-TRUS in prostate cancer

Prostate cancer is a prevalent malignant tumor in men, and accurate diagnosis and personalized treatment rely on multimodal imaging, such as MRI and TRUS. However, differences in imaging mechanisms and prostate deformation due to ultrasound probe compression pose significant challenges for high-quality registration between the two modalities.

In this study, we propose a label-aware weakly supervised diffusion model for MRI-TRUS multimodal image registration. First, we align label centroid positions by maximizing the Dice coefficient to correct initial biases. Second, we combine label supervision with a diffusion model to generate high-quality deformation fields. Finally, we incorporate a feature-guided module to better preserve edge structures and improve registration smoothness.

Experiments conducted on the µ-RegPro dataset demonstrate that our method outperforms current state-of-the-art (SOTA) approaches across multiple evaluation metrics. Specifically, it achieves a Dice coefficient of 0.880 and reduces the target registration error (TRE) to 0.940, significantly surpassing unsupervised methods such as VoxelMorph, FSDiffReg, and supervised methods like LocalNet and AutoFuse. The results show that preliminary label centroid alignment effectively enhances the performance of the diffusion-based deformation registration model, reducing the TRE from 3.084 to 0.940. The ablation study demonstrates that the feature-guided diffusion module effectively suppresses deformation field folding, while the label-aware module enhances label alignment. When combined, the proposed framework achieves a favorable balance, substantially improving registration accuracy (Dice = 0.880, TRE = 0.940) with reduced folding (|J|_≤0 = 0.134). This method exhibits strong robustness and generalizability in handling large deformations in target regions while preserving details in nontarget regions.

The proposed label-aware weakly supervised diffusion model enables accurate and efficient MRI-TRUS multimodal image registration, offering strong potential for clinical applications such as prostate cancer diagnosis, targeted biopsy, and image-guided navigation.