Registration of brain MRI images requires to solve a deformation field, which is extremely difficult in aligning intricate brain tissues, e.g., subcortical nuclei, etc. Existing efforts resort to decomposing the target deformation field into intermediate sub-fields with either tiny motions, i.e., progressive registration stage by stage, or lower resolutions, i.e., coarse-to-fine estimation of the full-size deformation field. In this paper, we argue that those efforts are not mutually exclusive, and propose a unified framework for robust brain MRI registration in both progressive and coarse-to-fine manners simultaneously. Specifically, building on a dual-encoder U-Net, the fixed-moving MRI pair is encoded and decoded into multi-scale sub-fields from coarse to fine. Each decoding block contains two proposed novel modules: i) in Deformation Field Integration (DFI), a single integrated deformation sub-field is calculated, warping by which is equivalent to warping progressively by sub-fields from all previous decoding blocks, and ii) in Non-rigid Feature Fusion (NFF), features of the fixed-moving pair are aligned by DFI-integrated deformation field, and then fused to predict a finer sub-field. Leveraging both DFI and NFF, the target deformation field is factorized into multi-scale sub-fields, where the coarser fields alleviate the estimate of a finer one and the finer field learns to make up those misalignments insolvable by previous coarser ones. The extensive and comprehensive experimental results on both private and two public datasets demonstrate a superior registration performance of brain MRI images over progressive registration only and coarse-to-fine estimation only, with an increase by at most 8% in the average Dice.

3D reconstruction of stereo endoscope image, as an enabling technique for varied surgical systems, e.g., medical droids, navigations, etc., suffers from severe overfitting problems due to scarce labels. Semi-supervised learning based on Teacher-Student Network (TSN) is a potential solution, which utilizes a supervised teacher model trained on available labeled data to teach a student model on all images via assigning them pseudo labels. However, TSN often faces a dilemma: if given only few labeled endoscope images, the teacher model will be trained to be defective and induce high-noised pseudo labels, degrading the student model significantly. To solve this, we propose an improved TSN for a robust 3D reconstruction of stereo endoscope image. Specifically, two novel modules are introduced: 1) a semi-supervised teacher model based on adversarial learning to produce mostly correct pseudo labels by forcing a consistency in predictions for both labeled and unlabeled data, and 2) a confidence network to further filter out noisy pseudo labels by estimating a confidence for each prediction of the teacher model. By doing so, the student model is able to distill knowledge from more accurate and noiseless pseudo labels, thus achieving improved performance. Experimental results on two public datasets show that our improved TSN achieves a superior performance than the state-of-the-arts by reducing the averaged disparity error by at least 13.5%.