One-shot neuroanatomy segmentation through online data augmentation and confidence aware pseudo label.

Recently, deep learning-based brain segmentation methods have achieved great success. However, most approaches focus on supervised segmentation, which requires many high-quality labeled images. In this paper, we pay attention to one-shot segmentation, aiming to learn from one labeled image and a few unlabeled images. We propose an end-to-end unified network that joints deformation modeling and segmentation tasks. Our network consists of a shared encoder, a deformation modeling head, and a segmentation head. In the training phase, the atlas and unlabeled images are input to the encoder to get multi-scale features. The features are then fed to the multi-scale deformation modeling module to estimate the atlas-to-image deformation field. The deformation modeling module implements the estimation at the feature level in a coarse-to-fine manner. Then, we employ the field to generate the augmented image pair through online data augmentation. We do not apply any appearance transformations cause the shared encoder could capture appearance variations. Finally, we adopt supervised segmentation loss for the augmented image. Considering that the unlabeled images still contain rich information, we introduce confidence aware pseudo label for them to further boost the segmentation performance. We validate our network on three benchmark datasets. Experimental results demonstrate that our network significantly outperforms other deep single-atlas-based and traditional multi-atlas-based segmentation methods. Notably, the second dataset is collected from multi-center, and our network still achieves promising segmentation performance on both the seen and unseen test sets, revealing its robustness. The source code will be available at https://github.com/zhangliutong/brainseg.

Symmetric pyramid network for medical image inverse consistent diffeomorphic registration.

Over the past few years, deep learning-based image registration methods have achieved remarkable performance in medical image analysis. However, many existing methods struggle to ensure accurate registration while preserving the desired diffeomorphic properties and inverse consistency of the final deformation field. To address the problem, this paper presents a novel symmetric pyramid network for medical image inverse consistent diffeomorphic registration. Specifically, we first encode the multi-scale images to the feature pyramids via a shared-weights encoder network and then progressively conduct the feature-level diffeomorphic registration. The feature-level registration is implemented symmetrically to ensure inverse consistency. We independently carry out the forward and backward feature-level registration and average the estimated bidirectional velocity fields for more robust estimation. Finally, we employ symmetric multi-scale similarity loss to train the network. Experimental results on three public datasets, including Mindboggle101, CANDI, and OAI, show that our method significantly outperforms others, demonstrating that the proposed network can achieve accurate alignment and generate the deformation fields with expected properties. Our code will be available at https://github.com/zhangliutong/SPnet.

Effects of Zuogui Wan on neurocyte apoptosis and down-regulation of TGF-beta1 expression in nuclei of arcuate hypothalamus of monosodium glutamate -liver regeneration rats.

AIM: To inquire into the effects and mechanism of Zuogui Wan (Pills for Kidney Yin) on neurocyte apoptosis in nuclei of arcuate hypothalamus (ARN) of monosodium glutamate (MSG)-liver regeneration rats, and the mechanism of liver regeneration by using optic microscope, electron microscope and in situ end labeling technology to adjust nerve-endocrine-immunity network. METHODS: Neurocyte apoptosis in ARN of the experiment rats was observed by using optic microscope, electron microscope and in situ end labeling technology. Expression of TGF-beta1 in ARN was observed by using immunohistochemistry method. RESULTS: The expression of TGF-beta1 in rats of model group was increased with the increase of ARN neurocyte apoptosis index (AI) (t = 8.3097, 12.9884, P<0.01). As compared with the rats of model group, the expression of TGF-beta1 in rats of Zuogui Wan treatment group was decreased with the significant decrease of ARN neurocyte apoptosis (t = 4.5624, 11.1420, P<0.01). CONCLUSION: Brain neurocyte calcium ion overexertion and TGF-beta1 protein participate in the adjustment and control of ARN neurocyte apoptosis in MSG-liver regeneration-rats. Zuogui Wan can prevent ARN neurocyte apoptosis of MSG-liver regeneration in rats by down-regulating the expression of TGF-beta1, and influence liver regeneration through adjusting nerve-endocrine-immune network.