Effective representation of neuronal morphology is essential for cell typing and understanding brain function. However, the complexity of neuronal morphology manifests not only in inter-class structural differences but also in intra-class variations across developmental stages and environmental conditions. Such diversity poses significant challenges for existing methods in balancing robustness and discriminative power when representing neuronal morphology. To address this, we propose SGTMorph, a hybrid Graph Transformer framework that leverages the local topological modeling capabilities of graph neural networks and the global relational reasoning strengths of Transformers to explicitly encode neuronal structural information. SGTMorph incorporates a random walk-based positional encoding scheme to facilitate effective information propagation across neuronal graphs and introduces a spatially invariant encoding mechanism to improve adaptability with diverse morphology. This integrated approach enables a robust and comprehensive representation of neuronal morphology while maintaining biological fidelity. To enable label-free feature learning, we devise a self-supervised learning strategy grounded in geometric and topological similarity metrics. Extensive experiments on five datasets demonstrate SGTMorph's superior performance in neuron morphology classification and retrieval tasks. Furthermore, Its practical utility in neuronal function research is validated through the accurate predictions of two functional features: the laminar distribution of somas and axonal projection patterns. The code is available at https://github.com/big-rain/SGTMorph.

MOTIVATION: Recent brain mapping efforts are producing large-scale whole-brain images using different imaging modalities. Accurate alignment and delineation of anatomical structures in these images are essential for numerous studies. These requirements are typically modeled as two distinct tasks: registration and segmentation. However, prevailing methods, fail to fully explore and utilize the inherent correlation and complementarity between the two tasks. Furthermore, variations in brain anatomy, brightness, and texture pose another formidable challenge in designing multi-modal similarity metrics. A high-throughput approach capable of overcoming the bottleneck of multi-modal similarity metric design, while effective leveraging the highly correlated and complementary nature of two tasks is highly desirable. RESULTS: We introduce a deep learning framework for joint registration and segmentation of multi-modal brain images. Under this framework, registration and segmentation tasks are deeply coupled and collaborated at two hierarchical layers. In the inner layer, we establish a strong feature-level coupling between the two tasks by learning a unified common latent feature representation. In the outer layer, we introduce a mutually supervised dual-branch network to decouple latent features and facilitate task-level collaboration between registration and segmentation. Since the latent features we designed are also modality-independent, the bottleneck of designing multi-modal similarity metric is essentially addressed. Another merit offered by this framework is the interpretability of latent features, which allows intuitive manipulation of feature learning, thereby further enhancing network training efficiency and the performance of both tasks. Extensive experiments conducted on both multi-modal and mono-modal datasets of mouse and human brains demonstrate the superiority of our method. AVAILABILITY AND IMPLEMENTATION: The code is available at https://github.com/tingtingup/DCRS.

With the development of deep learning, breakthroughs have been made in the field of semantic segmentation. However, it is difficult to generate a fine mask on the same medical images because medical images have low contrast, high resolution, and insufficient semantic information. In most scenarios, existing approaches mostly use a pooling layer to reduce the resolution of feature maps. Therefore, it is difficult for them to consider the whole image features, resulting in information loss and performance degradation. In this paper, a multiscale asymmetric encoder-decoder semantic segmentation network is proposed. The network consists of two parts, which perform feature extraction and image restoration on the input, respectively. The encoder network obtains multiscale feature information by connecting multiple ASPP modules to form a feature pyramid. Meanwhile, the upsampling layer of each decoder can be connected to the feature map generated by the corresponding ASPP module. Finally, the classification information of each pixel is obtained through the sigmoid function. The performance of the proposed method can be verified on publicly available datasets. The experimental evidence shows that the proposed method can take full advantage of multiscale feature information and achieve superior performance with less inference computational cost.