Segmenting vessels in magnetic resonance imaging (MRI) stands as a mainstream approach for evaluating cerebrovascular conditions.Due to the complex semantics and topology of cerebrovascular structures, existing CNN-based segmentation methods often fail to correlate the topological structure and branch vessels, resulting in incomplete segmentation.To address the challenge of global dependencies modelling, transformer architectures have been employed due to their capability of capturing long-range dependencies, and they have shown promise in 3D medical image segmentation.However, the transformer architecture greatly increases the computational burden when processing high-dimensional 3D MRI images.In light of this, a selective state space model (SSM) Mamba has gained recognition for its adeptness in handling long-range dependencies in sequential data, particularly noted for its efficiency and speed in natural language processing applications.Mamba is now widely applied in various computer vision tasks.Based on these findings, in this study, we propose MambaVesselNet, a Hybrid CNN-Mamba network for 3D cerebrovascular segmentation.MambaVesselNet leverages CNNs to capture local features and incorporates the Mamba block at the bottleneck to model long-range dependencies within the whole-volume features.The effectiveness of MambaVesselNet is validated on a public cerebrovascular dataset, and our benchmark demonstrates new state-of-the-art performance.

Three-dimensional cerebrovascular segmentation is crucial for accurate diagnosis and treatment planning of cerebrovascular diseases.However, the lack of high-quality publicly labelled datasets can limit sufficient training, leading to inaccurate results.To address this issue, this study proposes a novel method that combines self-supervised and fully supervised learning, termed the SS-FS Cerebrovascular Segmentation Approach (SS-FS CSA).The method introduces publicly available unlabelled databases into the training process, alleviating the problem of insufficient high-quality labelled medical datasets.The SS-FS CSA method achieves a Dice Similarity Coefficient (DSC) of 82.82%, improving over 2% compared to the SOTA baseline, proving its validity and feasibility in 3D segmentation tasks.

In this paper, we address the issue of scene text detection in the way of direct regression and successfully adapt an effective semantic segmentation model, DeepLab v3+ [1], for this application. In order to handle texts with arbitrary orientations and sizes and improve the recall of small texts, we propose to extract features of multiple scales by inserting multiple Atrous Spatial Pyramid Pooling (ASPP) layers to the DeepLab after the feature maps with different resolutions. Then, we set multiple auxiliary IoU losses at the decoding stage and make auxiliary connections from the intermediate encoding layers to the decoder to assist network training and enhance the discrimination ability of lower encoding layers. Experiments conducted on the benchmark scene text dataset ICDAR2015 demonstrate the superior performance of our proposed network, named as DeepText, over the state-of-the-art approaches.

Decomposing a document written by more than one author into sentences based on authorship is of great significance due to the increasing demand for plagiarism detection, forensic analysis, civil law (i.e., disputed copyright issues), and intelligence issues that involve disputed anonymous documents. Among existing studies for document decomposition, some were limited by specific languages, according to topics or restricted to a document of two authors, and their accuracies have big room for improvement. In this paper, we consider the contextual correlation hidden among sentences and propose an algorithm for Sequential and Unsupervised Decomposition of a Multi‐Author Document (SUDMAD) written in any language, disregarding topics, through the construction of a Hidden Markov Model (HMM) reflecting the authors' writing styles. To build and learn such a model, an unsupervised, statistical approach is first proposed to estimate the initial values of HMM parameters of a preliminary model, which does not require the availability of any information of author's or document's context other than how many authors contributed to writing the document. To further boost the performance of this approach, a boosted HMM learning procedure is proposed next, where the initial classification results are used to create labeled training data to learn a more accurate HMM. Moreover, the contextual relationship among sentences is further utilized to refine the classification results. Our proposed approach is empirically evaluated on three benchmark datasets that are widely used for authorship analysis of documents. Comparisons with recent state‐of‐the‐art approaches are also presented to demonstrate the significance of our new ideas and the superior performance of our approach.