3D segmentation framework for whole-brain segmentation from thick-slice brain MRI

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.

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.

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.

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.

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.