Accurate segmentation of anatomical structures in Computed Tomography (CT) images is crucial for clinical diagnosis, treatment planning, and disease monitoring. The present deep learning segmentation methods are hindered by factors such as data scale and model size. Inspired by how doctors identify tissues, we propose a novel approach, the Prior Category Network (PCNet), that boosts segmentation performance by leveraging prior knowledge between different categories of anatomical structures. Our PCNet comprises three key components: prior category prompt (PCP), hierarchy category system (HCS), and hierarchy category loss (HCL). PCP utilizes Contrastive Language-Image Pretraining (CLIP), along with attention modules, to systematically define the relationships between anatomical categories as identified by clinicians. HCS guides the segmentation model in distinguishing between specific organs, anatomical structures, and functional systems through hierarchical relationships. HCL serves as a consistency constraint, fortifying the directional guidance provided by HCS to enhance the segmentation model's accuracy and robustness. We conducted extensive experiments to validate the effectiveness of our approach, and the results indicate that PCNet can generate a high-performance, universal model for CT segmentation. The PCNet framework also demonstrates a significant transferability on multiple downstream tasks. The ablation experiments show that the methodology employed in constructing the HCS is of critical importance. The prompt and HCS can be accessed at https://github.com/PKU-MIPET/PCNet.

In multi-site studies of Alzheimer's disease (AD), the difference of data in multi-site datasets leads to the degraded performance of models in the target sites. The traditional domain adaptation method requires sharing data from both source and target domains, which will lead to data privacy issue. To solve it, federated learning is adopted as it can allow models to be trained with multi-site data in a privacy-protected manner. In this paper, we propose a multi-site federated domain adaptation framework via Transformer (FedDAvT), which not only protects data privacy, but also eliminates data heterogeneity. The Transformer network is used as the backbone network to extract the correlation between the multi-template region of interest features, which can capture the brain abundant information. The self-attention maps in the source and target domains are aligned by applying mean squared error for subdomain adaptation. Finally, we evaluate our method on the multi-site databases based on three AD datasets. The experimental results show that the proposed FedDAvT is quite effective, achieving accuracy rates of 88.75%, 69.51%, and 69.88% on the AD vs. NC, MCI vs. NC, and AD vs. MCI two-way classification tasks, respectively.

Electroencephalograph (EEG) emotion recognition plays an important role in the brain-computer interface (BCI) field. However, most of recent methods adopted shallow graph neural networks using a single temporal feature, leading to the limited emotion classification performance. Furthermore, the existing methods generally ignore the individual divergence between different subjects, resulting in poor transfer performance. To address these deficiencies, we propose a dual-branch dynamic graph convolution based adaptive transformer feature fusion network with adapter-finetuned transfer learning (DBGC-ATFFNet-AFTL) for EEG emotion recognition. Specifically, a dual-branch graph convolution network (DBGCN) is firstly designed to effectively capture the temporal and spectral characterizations of EEG simultaneously. Second, the adaptive Transformer feature fusion network (ATFFNet) is conducted by integrating the obtained feature maps with the channel-weight unit, leading to significant difference between different channels. Finally, the adapter-finetuned transfer learning method (AFTL) is applied in cross-subject emotion recognition, which proves to be parameter-efficient with few samples of the target subject. The competitive experimental results on three datasets have shown that our proposed method achieves the promising emotion classification performance compared with the state-of-the-art methods. The code of our proposed method will be available at:https://github.com/smy17/DANet.

Medical imaging denoising faces great challenges, yet is in great demand. With its distinctive characteristics, medical imaging denoising in the image domain requires innovative deep learning strategies. In this study, we propose a simple yet effective strategy, the content-noise complementary learning (CNCL) strategy, in which two deep learning predictors are used to learn the respective content and noise of the image dataset complementarily. A medical image denoising pipeline based on the CNCL strategy is presented, and is implemented as a generative adversarial network, where various representative networks (including U-Net, DnCNN, and SRDenseNet) are investigated as the predictors. The performance of these implemented models has been validated on medical imaging datasets including CT, MR, and PET. The results show that this strategy outperforms state-of-the-art denoising algorithms in terms of visual quality and quantitative metrics, and the strategy demonstrates a robust generalization capability. These findings validate that this simple yet effective strategy demonstrates promising potential for medical image denoising tasks, which could exert a clinical impact in the future. Code is available at: https://github.com/gengmufeng/CNCL-denoising.

We proposed a novel MRI tracer-based method for the determination of water diffusion in the brain extracellular space (ECS). The measuring system was validated in 32 Sprague Dawley rats. The rats were randomly divided into four groups with different injection sites: 1) caudate nucleus (Cn.); 2) thalamus (T.); 3) cortex (Cor.); and 4) substantia nigra (Sn.). The spin-lattice relaxation time of hydrogen nuclei in water molecules were shortened, which presented as high signal on MRI after the injection of gadolinium-diethylene triamine pentaacetic acid (Gd-DTPA) into the rat brain ECS. The enhancement on MRI decreased over time due to the water diffusion and clearance process within the brain ECS. The process was dynamically recorded on a series of magnetic resonance (MR) images. As the increment in signal intensity (ΔSI) could be converted to local Gd-DTPA concentration, the water diffusion parameters were further calculated voxel by voxel based on a modified diffusion model. The most tortuous ECS (λ = 1.77 ± 0.71) was found in Sn. with D∗(Sn) of (2.06 ± 1.01) × 10(-4) mm(2)·s(-1) ( P < 0.05). No statistical difference was demonstrated among D∗(Cn), D∗(T.), and D∗(Cor). with an average D∗ values of (3.28 ± 0.88) × 10(-4) mm(2)·s(-1)( F = 0.18, P > 0.05). By using the tracer-based MRI method, the local diffusion parameters of the brain ECS can be quantitatively measured. The different distribution territories and clearance rates of the tracer in four brain areas indicated that the brain ECS is a physiologically partitioned system.