3D Segmentation Framework for Whole-Brain Segmentation from Thick-Slice Brain MRI.

BACKGROUND:Accurate whole-brain tissue segmentation from MRI is fundamental in medical image analysis. Although existing 3D segmentation methods perform well on thin-slice MRI data, their performance often decreases on thick-slice images due to low through-plane resolution and missing inter-slice anatomical information. NEW METHOD:We developed a bi-directional super-resolution reconstruction (BSR)-assisted 3D segmentation framework for thick-slice brain MRI. First, a general whole-brain segmentation model based on nnU-Net was trained using thin-slice MRI data to obtain robust cross-dataset segmentation capability. Then, the proposed BSR network was used to recover missing inter-slice information from thick-slice MRI and generate thin-slice-like 3D images with improved anatomical continuity. The reconstructed high-quality slices were inserted into the inter-slice gaps of thick-slice MRI data as supplementary anatomical information for subsequent segmentation. RESULTS:The proposed framework was evaluated on multiple public brain MRI datasets, including IBSR18, LPBA40, and OASIS. Experimental results demonstrated that our method effectively improved whole-brain segmentation performance on thick-slice MRI data. When LPBA40 was used as the training dataset, our framework achieved a 3.6 % improvement over the original nnU-Net on the challenging IBSR18 thick-slice dataset. On another thick-slice dataset, OASIS-3, the proposed method obtained a 1.2 % accuracy improvement. COMPARISON WITH EXISTING METHODS:Compared with conventional nnU-Net-based segmentation, our framework improves robustness on thick-slice MRI by explicitly compensating for missing inter-slice anatomical information before segmentation. CONCLUSION:The proposed BSR-assisted framework provides an effective solution for whole-brain segmentation from thick-slice MRI and may facilitate more reliable brain tissue analysis in clinical scenarios.