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The precise and automatic segmentation of prostatemagnetic resonance imaging (MRI) images is vital for assisting doctors in diagnosing prostate diseases. In recent years, many advanced methods have been applied to prostate segmentation, but due to the variability caused by prostate diseases, automatic segmentation of the prostate presents significant challenges. In this paper, we propose an attention-guided multi-scale feature fusion network (AGMSFNet) to segment prostateMRI images. We propose an attention mechanism for extracting multi-scale features, and introduce a 3D transformer module to enhance global feature representation by adding it during the transition phase from encoder to decoder. In the decoder stage, a feature fusion module is proposed to obtain global context information. We evaluate our model on MRI images of the prostate acquired from a local hospital. The relative volume difference (RVD) and dice similarity coefficient (DSC) between the results of automatic prostate segmentation and ground truth were 1.21% and 93.68%, respectively. To quantitatively evaluate prostate volume on MRI, which is of significant clinical significance, we propose a unique AGMSF-Net. The essential performance evaluation and validation experiments have demonstrated the effectiveness of our method in automatic prostate segmentation. [ABSTRACT FROM AUTHOR]

The point cloud semantic understanding task has made remarkable progress along with the development of 3D deep learning. However, aggregating spatial information to improve the local feature learning capability of the network remains a major challenge. Many methods have been used for improving local information learning, such as constructing a multi‐area structure for capturing different area information. However, it will lose some local information due to the independent learning point feature. To solve this problem, a new network is proposed that considers the importance of the differences between points in the neighbourhood. Capturing local feature information can be enhanced by highlighting the different feature importance of the point cloud in the neighbourhood. First, T‐Net is constructed to learn the point cloud transformation matrix for point cloud disorder. Second, transformer is used to improve the problem of local information loss due to the independence of each point in the neighbourhood. The experimental results show that 92.2% accuracy overall was achieved on the ModelNet40 dataset and 93.8% accuracy overall was achieved on the ModelNet10 dataset. [ABSTRACT FROM AUTHOR]

Point cloud segmentation is one of the most important tasks in LiDAR remote sensing with widespread scientific, industrial, and commercial applications. The research thereof has resulted in many breakthroughs in 3D object and scene understanding. Existing methods typically utilize hierarchical architectures for feature representation. However, the commonly used sampling and grouping methods in hierarchical networks are not only time-consuming but also limited to point-wise 3D coordinates, ignoring the local semantic homogeneity of point clusters. To address these issues, we propose a novel 3D point cloud representation network, called Dynamic Clustering Transformer Network (DCTNet). It has an encoder–decoder architecture, allowing for both local and global feature learning. Specifically, the encoder consists of a series of dynamic clustering-based Local Feature Aggregating (LFA) blocks and Transformer-based Global Feature Learning (GFL) blocks. In the LFA block, we propose novel semantic feature-based dynamic sampling and clustering methods, which enable the model to be aware of local semantic homogeneity for local feature aggregation. Furthermore, instead of traditional interpolation approaches, we propose a new semantic feature-guided upsampling method in the decoder for dense prediction. To our knowledge, DCTNet is the first work to introduce semantic information-based dynamic clustering into 3D Transformers. Extensive experiments on an object-based dataset (ShapeNet), and an airborne multispectral LiDAR dataset demonstrate the State-of-the-Art (SOTA) segmentation performance of DCTNet in terms of both accuracy and efficiency. Our code will be made publicly available.

• SwinUNETR + uncertainty quantification aids acute ischemic stroke diagnosis via NCCT. • The proposed model predicted lesion volume with a mean Dice score: 46.7 %. • Our method was tested on the public AISD dataset, resulting in a Dice score: 61.9 %. • The study explores feasibility of the model's applications in the clinical scenario. We adopt the existing Deep learning architecture to support diagnosing acute ischemic stroke by automatically detecting lesion location on 3D non-contrast CT brain scans. We also investigate the feasibility of the model's applications in the clinical scenario by data analysis. We retrospectively collected 3D non-contrast CT scans of 317 patients with acute ischemic stroke from the China Medical University Hospital. All patients underwent standard baseline non-contrast CT scanning followed by diffusion-weighted imaging. We utilized these data for training the existing model – SwinUNETR, which includes a self-attention module as an encoder and a convolutional-based decoder. Moreover, the software innovatively incorporates uncertainty quantification to enhance model performance. In the test set, the AI model predicted lesion volume with a mean Dice score of 46.7 % compared to diffusion-weighted imaging verified by experts. The model completed the analysis on a 3D non-contrast CT scan in approximately 30 s. The average difference between the model-segmented acute ischemic stroke lesion volume (67.11 ml) and diffusion-weighted imaging lesion volume (35.2 ml) was 27.09 ml. Pearson correlation of lesion volume between prediction and ground truth is 83.46 %. We also found our model has superior performance in the CT scan with lesion volume > 40 ml and 3 h < onset-to-CT time <24 h. Moreover, our approach was applied to the AISD public dataset, yielding a Dice score of 0.619 upon testing. This model could help facilitate timely and accurate diagnosis of acute ischemic stroke in a clinical emergency setting and low-resourced hospital. [ABSTRACT FROM AUTHOR]

Objective. The objective of this study is to develop an efficient multimodal learning framework for the classification of glaucoma. Glaucoma is a group of eye diseases that can result in vision loss and blindness, often due to delayed detection and treatment. Fundus images and optical coherence tomography (OCT) images have proven valuable for the diagnosis and management of glaucoma. However, current models that combine features from both modalities often lack efficient spatial relationship modeling. Approach. In this study, we propose an innovative approach to address the classification of glaucoma. We focus on leveraging the features of OCT volumes and harness the capabilities of transformer models to capture long-range spatial relationships. To achieve this, we introduce a 3D transformer model to extract features from OCT volumes, enhancing the model's effectiveness. Additionally, we employ downsampling techniques to enhance model efficiency. We then utilize the spatial feature relationships between OCT volumes and fundus images to fuse the features extracted from both sources. Main results. Our proposed framework has yielded remarkable results, particularly in terms of glaucoma grading performance. We conducted our experiments using the GAMMA dataset, and our approach outperformed traditional feature fusion methods. By effectively modeling spatial relationships and combining OCT volume and fundus map features, our framework achieved outstanding classification results. Significance. This research is of significant importance in the field of glaucoma diagnosis and management. Efficient and accurate glaucoma classification is essential for timely intervention and prevention of vision loss. Our proposed approach, which integrates 3D transformer models, offers a novel way to extract and fuse features from OCT volumes and fundus images, ultimately enhancing the effectiveness of glaucoma classification. This work has the potential to contribute to improved patient care, particularly in the early detection and treatment of glaucoma, thereby reducing the risk of vision impairment and blindness., (© 2023 Institute of Physics and Engineering in Medicine.)