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Methodology for pediatric head computed tomography image segmentation and volumetric calculation using a tablet computer and stylus pen

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CHILDS NERVOUS SYSTEM 2025年第1期41卷 1-8页

作者： Hashimoto, H. Shimada, M. Takemoto, O. Chiba, Y. Osaka Womens & Childrens Hosp Dept Neurosurg Izumi Osaka 5941101 Japan Osaka Univ Grad Sch Med Dept Neurol Diag & Restorat Suita Osaka 5650871 Japan Massachusetts Gen Hosp Ctr Neurotechnol & Neurorecovery Dept Neurol Boston MA 02114 USA Harvard Med Sch Dept Neurol Boston MA 02115 USA Osaka Womens & Childrens Hosp Dept Radiol Izumi Osaka 5941101 Japan

Purpose: This study presents a MATrix LABoratory (MATLAB)-based methodology for calculating intracranial volumes from head computed tomography (CT) data and compares it with established methods. Methods: Regions of interest (ROI) were manually segmented on CT images using a stylus pen, facilitated by mirroring a computer desktop onto a tablet. The volumetric process involved three main steps: (1) calculating the volume of a single voxel, (2) counting the total number of voxels within the segmented ROI, and (3) multiplying this voxel count by the single-voxel volume. This method was applied to 83 pediatric head CT scans from patients with minor head trauma, and the volumetric results were compared with those obtained from OsiriX. Results: A paired t-test revealed a statistically significant difference (p < 0.001) between volumes obtained with our MATLAB-based method and those from OsiriX, with our method measuring 0.32% higher. However, an unpaired t-test found no statistically significant differences between the volumetric population groups (p = 0.84). Conclusion: The significant difference identified by the paired t-test likely reflects statistical distinctions arising from differences in the calculation methods of the two approaches. Conversely, the unpaired t-test suggests no statistically detectable differences between the volumetric populations. Although this does not imply that the two methods produce identical results, the volumetric populations derived from our method may originate from the same underlying population as those obtained using OsiriX. By taking these points into account, our method has the potential to serve as a valuable tool for volumetric measurements.

关键词： image segmentation Volumetric measurement Tablet computer Stylus pen

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Semi-supervised cardiac magnetic resonance image segmentation based on domain generalization

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High Technology Letters 2025年第1期31卷 41-52页

作者： SHAO Hong HOU Jinyang CUI Wencheng School of Information Science and Engineering Shenyang University of TechnologyShenyang 110870P.R.China

In the realm of medical image segmentation,particularly in cardiac magnetic resonance imaging(MRI),achieving robust performance with limited annotated data is a significant *** often degrades when faced with testing scenarios from unknown *** address this problem,this paper proposes a novel semi-supervised approach for cardiac magnetic resonance image segmentation,aiming to enhance predictive capabilities and domain generalization(DG).This paper establishes an MT-like model utilizing pseudo-labeling and consistency regularization from semi-supervised learning,and integrates uncertainty estimation to improve the accuracy of ***,to tackle the challenge of domain generalization,a data manipulation strategy is introduced,extracting spatial and content-related information from images across different domains,enriching the dataset with a multi-domain *** paper􀆳s method is meticulously evaluated on the publicly available cardiac magnetic resonance imaging dataset M&Ms,validating its *** analyses against various methods highlight the out-standing performance of this paper􀆳s approach,demonstrating its capability to segment cardiac magnetic resonance images in previously unseen domains even with limited annotated data.

关键词： semi-supervised domain generalization(DG) cardiac magnetic resonance image segmentation

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Diabetic Retinopathy image segmentation Method Based on Fusion DenseNet and U-Net Network

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Engineering Letters 2025年第2期33卷 418-428页

作者： Zhang, Yaxuan Xu, Yang Zhai, Jinlong University of Science and Technology LiaoNing Anshan114051 China School of Computer Science and Software Engineering University of Science and Technology LiaoNing Anshan114051 China

Diabetic Retinopathy (DR) is a common and significant complication in patients with diabetes, and severely affecting their quality of life. image segmentation plays a crucial role in the early diagnosis and treatment of DR. However, traditional methods are limited in terms of segmentation accuracy and generalization capability. This paper proposes a novel image segmentation method for diabetic retinopathy based on the integration of DenseNet and U-Net networks. Firstly, DenseNet is utilized to replace the encoder part of U-Net. The dense connection mechanism enhances the efficiency of feature propagation and improves the feature extraction capability of the encoder. Secondly, we introduce Omni-Dimensional Dynamic Convolution (ODConv) to replace traditional convolutions. ODConv is used to handle diverse input features effectively. The model's adaptability and segmentation accuracy for various samples is improved. Finally, we integrate the Convolutional Block Attention Module (CBAM) into the decoder. By leveraging channel and spatial attention, the ability to capture key features is enhanced, and the segmentation accuracy and boundary recognition capabilities are improved. To validate the effectiveness and feasibility of the model. This paper conducts experiments on the DDR dataset and the IDRID dataset. The experimental results show that the proposed method improved the mDice score by 0.53% compared to traditional methods. It also increased the mIoU score by 1.12% over traditional methods. This indicates that the proposed method has better performance in segmenting diabetic retinopathy images. It also demonstrates better generalization ability. © 2025, International Association of Engineers. All rights reserved.

关键词： image segmentation

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BorB: A Novel image segmentation Technique for Improving Plant Disease Classification With Deep Learning Models

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IEEE Access 2025年 13卷 71822-71839页

作者： Ozcan, T. Polat, E. Erciyes University Department of Computer Engineering Melikgazi Kayseri38039 Turkey Niğde Ömer Halisdemir University Department of Computer Engineering Central Campus Niğde51240 Turkey

Disease detection from leaf images has been among the popular studies in recent years. Classifying leaf diseases using computational methods provides great convenience for farming. In the studies carried out in this field, systems that work with high accuracy and are least affected by environmental factors that can be used in agricultural lands come to the fore. This study investigates the application of deep learning architectures for accurate and efficient plant disease detection within the context of the ongoing digital transformation of the agricultural sector. Recognizing the critical role of AI in modernizing agriculture, this research focuses on enhancing the accuracy of the classification of plant diseases. To facilitate this research, a novel dataset, "EruCauliflowerDB", was meticulously curated, comprising high-resolution images of cauliflower plants infected with Alternaria Leaf Spot and Black Rot. The obtained EruCauliflower dataset contains 114 images from the Alternaria Leaf Spot disease class and 99 images from the Black Rot disease class. A novel integrated classification system was developed, encompassing three key stages. First, a novel segmentation method, "BorB," was introduced to effectively isolate diseased leaf regions. This segmentation method enables us to extract features of leaf images in Lab and RGB formats. Combining the features obtained from the two image formats with the OR logical operation separates the leaf region from the background. Second, data augmentation techniques, including geometric transformations, were applied to the segmented images to enhance data diversity and improve model robustness. Finally, four state-of-the-art deep learning models—VGG16, ResNet50, EfficientNetB3, and MobileNetV3 Large—were employed for disease classification. The proposed integrated system demonstrated exceptional performance, achieving 100% classification accuracy on the EruCauliflowerDB dataset across all four models. To assess the system’s ro

关键词： Plant diseases Accuracy Deep learning image segmentation Feature extraction Artificial intelligence Agriculture Training Data augmentation Transfer learning

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Effective Global Context Integration for Lightweight 3D Medical image segmentation

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2025年第5期35卷 4661-4674页

作者： Qiao, Qiang Qu, Meixia Wang, Wenyu Jiang, Bin Guo, Qiang Shandong Univ Sch Mech Elect & Informat Engn Weihai 264209 Peoples R China Shenzhen Res Inst Shandong Univ Shenzhen 518057 Peoples R China Shandong Univ Finance & Econ Sch Comp Sci & Technol Jinan 250014 Peoples R China Shandong Prov Key Lab Digital Media Technol Jinan 250014 Peoples R China

Accurate and fast segmentation of 3D medical images is crucial in clinical analysis. CNNs struggle to capture long-range dependencies because of their inductive biases, whereas the Transformer can capture global features but faces a considerable computational burden. Thus, efficiently integrating global and detailed insights is key for precise segmentation. In this paper, we propose an effective and lightweight architecture named GCI-Net to address this issue. The key characteristic of GCI-Net is the global-guided feature enhancement strategy (GFES), which integrates the global context and facilitates the learning of local information;3D convolutional attention, which captures long-range dependencies;and a progressive downsampling module, which perceives detailed information better. The GFES can capture the local range of information through global-guided feature fusion and global-local contrastive loss. All these designs collectively contribute to lower computational complexity and reliable performance improvements. The proposed model is trained and tested on four public datasets, namely MSD Brain Tumor, ACDC, BraTS2021, and MSD Lung. The experimental results show that, compared with several recent SOTA methods, our GCI-Net achieves superior computational efficiency with comparable or even better segmentation performance. The code is available at https://***/qintianjian-lab/GCI-Net.

关键词： image segmentation Feature extraction Three-dimensional displays Transformers Computational modeling Solid modeling Medical diagnostic imaging Convolution Circuits and systems Decoding Global guidance global-local contrast attention mechanism lightweight model medical image segmentation

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Mixed reality infrastructure based on deep learning medical image segmentation and 3D visualization for bone tumors using DCU-Net

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JOURNAL OF BONE ONCOLOGY 2025年 50卷 100654-100654页

作者： Wang, Kun Han, Yong Ye, Yuguang Chen, Yusi Zhu, Daxin Huang, Yifeng Huang, Ying Chen, Yijie Shi, Jianshe Ding, Bijiao Huang, Jianlong Quanzhou Normal Univ Inst Design Quanzhou 362000 Peoples R China Quanzhou Univ Informat Engn Sch Design Quanzhou 362000 Fujian Peoples R China Quanzhou Normal Univ Sch Math & Comp Sci Quanzhou 362001 Peoples R China Quanzhou Normal Univ Fujian Prov Key Lab Data Intens Comp Quanzhou 362001 Peoples R China Fujian Prov Univ Quanzhou Normal Univ Key Lab Intelligent Comp & Informat Proc Quanzhou 362001 Peoples R China Huaqiao Univ Affliated Strait Hosp Dept Diagnost Radiol Quanzhou 362000 Fujian Peoples R China Huaqiao Univ Affliated Strait Hosp Dept Gen Surg Quanzhou 362000 Fujian Peoples R China

Objective: Segmenting and reconstructing 3D models of bone tumors from 2D image data is of great significance for assisting disease diagnosis and treatment. However, due to the low distinguishability of tumors and surrounding tissues in images, existing methods lack accuracy and stability. This study proposes a U-Net model based on double dimensionality reduction and channel attention gating mechanism, namely the DCU-Net model for oncological image segmentation. After realizing automatic segmentation and 3D reconstruction of osteosarcoma by optimizing feature extraction and improving target space clustering capabilities, we built a mixed reality (MR) infrastructure and explored the application prospects of the infrastructure combining deep learning- based medical image segmentation and mixed reality in the diagnosis and treatment of bone tumors. Methods: We conducted experiments using a hospital dataset for bone tumor segmentation, used the optimized DCU-Net and 3D reconstruction technology to generate bone tumor models, and used set similarity (DSC), recall (R), precision (P), and 3D vertex distance error (VDE) to evaluate segmentation performance and 3D reconstruction effects. Then, two surgeons conducted clinical examination experiments on patients using two different methods, viewing 2D images and virtual reality infrastructure, and used the Likert scale (LS) to compare the effectiveness of surgical plans of the two methods. Results: The DSC, R and P values of the model introduced in this paper all exceed 90%, which has significant advantages compared with methods such as U-Net and Attention-Uet. Furthermore, LS showed that clinicians in the DCU-Net-based MR group had better spatial awareness of tumor preoperative planning. Conclusion: The deep learning DCU-Net algorithm model can improve the performance of tumor CT image segmentation, and the reconstructed fine model can better reflect the actual situation of individual tumors;the MR system constructed based on

关键词： image segmentation DCU-Net model 3D visualization Bone tumor diagnosis Mixed reality

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Small sample learning based on probability-informed neural networks for SAR image segmentation

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Neural Computing and Applications 2025年第14期37卷 8285-8308页

作者： Dostovalova, Anastasia Gorshenin, Andrey Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences Vavilova Str 44/2 Moscow119333 Russia

The paper introduces a probability-informed methodology for the segmentation of synthetic aperture radar (SAR) images in the case of small sample learning. It assumes that the amount of training data is limited to several hundred or thousand elements, which prevents the effective training of state-of-the-art neural network (NN) models. This is a typical problem for real SAR images whose characteristics depend significantly on the sensors used to produce them and cannot always be repeated within open available datasets. To solve this problem, we propose NN models called Probability-Informed Neural Networks (PrINNs). As part of our approach, we introduce the use of probability models as a source of additional features for data. Specifically, the training dataset is enriched by modeling the pixel brightness using a finite normal mixture. We prove that such an extension can reduce errors in the learning process theoretically. The resulting enriched dataset is segmented using attention-based convolutional NNs or visual transformers. Then, post-processing is implemented based on another probability model—quadtree, which is a special case of random Markov fields. As we have theoretically demonstrated, this part of PrINNs is analogous to the graph-convolutional NNs with fixed weights. Using open SAR images obtained by different radars (namely, Sentinel-1, Capella, ESAR and HRSID) with various types of underlying surfaces, the possibility of improving segmentation quality based on PrINNs is demonstrated. We tested various combinations of methods from the PrINNs architecture, and in all cases, the PrINN approach we proposed was superior to any other combination of these methods. From the point of view of the achieved accuracy metrics, the mean F1 score increased up to 19.24%, and the median F1 score was improved up to 9.57%. Some further architectural improvements to PrINNs are also discussed in the paper. © The Author(s), under exclusive licence to Springer-Verlag London Ltd

关键词： image segmentation

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The image Encryption Method Based on ADA-DeepLab image segmentation and GAN Key Generation

Journal of Network Intelligence

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Journal of Network Intelligence 2025年第1期10卷 206-219页

作者： Sun, Xian-He Ma, Hong-Bin Wang, Meng-Sheng Ma, Qi-Tao School of Electronic Engineering Heilongjiang University Harbin150080 China China Mobile Group Shanghai Co. Ltd Shanghai China

Traditional image encryption methods usually focus on the encryption of the entire picture. However, only a small part of the data in the image may carry key information Using the same encryption method may pose a risk of being deciphered. Therefore, we propose an image adaptive encryption method. Firstly, in the process of image segmentation with small targets and multi-target scenarios, issues such as target omission, miscalculation, and prediction gap presence can lead to the loss of a large number of details and a significant reduction in the amount of information. We have designed the ADA-DeepLab image adaptive segmentation network, introduced the variable pyramid module to better localize the regions that need prediction in the adaptive perception domain, and added two attention mechanisms to complement each other, enhancing the recovery of semantic information more efficiently. The research in this paper has been experimentally demonstrated using the PASCAL VOC 2012 dataset. The results show that the proposed ADA-DeepLab model achieves a significant improvement in accuracy and is capable of segmenting the target more accurately. Secondly, we propose a new key generation model, R-GAN, to address issues of network instability and short chaotic cycles in GAN training. We introduce a residual network to generate sequences with larger periods using the GAN model by training on input chaotic sequences. The generator and discriminator are enhanced to stabilize the training process. Finally, the segmented important data rows are encrypted. Experiments have proven that the generated key is more secure and randomized, offering a better hiding effect on both the key regions and the entire image. This method is highly effective in safeguarding important data and holds significant potential for practical applications. © 2025, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： image segmentation

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Learnable Prompting SAM-Induced Knowledge Distillation for Semi-Supervised Medical image segmentation

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IEEE TRANSACTIONS ON MEDICAL IMAGING 2025年第5期44卷 2295-2306页

作者： Huang, Kaiwen Zhou, Tao Fu, Huazhu Zhang, Yizhe Zhou, Yi Gong, Chen Liang, Dong Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Peoples R China Agcy Sci Technol & Res Inst High Performance Comp Singapore 138632 Singapore Southeast Univ Sch Comp Sci & Engn Nanjing 211189 Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Lauterbur Res Ctr Biomed Imaging Shenzhen 518055 Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Res Ctr Med AI Shenzhen 518055 Peoples R China

The limited availability of labeled data has driven advancements in semi-supervised learning for medical image segmentation. Modern large-scale models tailored for general segmentation, such as the Segment Anything Model (SAM), have revealed robust generalization capabilities. However, applying these models directly to medical image segmentation still exposes performance degradation. In this paper, we propose a learnable prompting SAM-induced Knowledge distillation framework (KnowSAM) for semi-supervised medical image segmentation. Firstly, we propose a Multi-view Co-training (MC) strategy that employs two distinct sub-networks to employ a co-teaching paradigm, resulting in more robust outcomes. Secondly, we present a Learnable Prompt Strategy (LPS) to dynamically produce dense prompts and integrate an adapter to fine-tune SAM specifically for medical image segmentation tasks. Moreover, we propose SAM-induced Knowledge Distillation (SKD) to transfer useful knowledge from SAM to two sub-networks, enabling them to learn from SAM's predictions and alleviate the effects of incorrect pseudo-labels during training. Notably, the predictions generated by our subnets are used to produce mask prompts for SAM, facilitating effective inter-module information exchange. Extensive experimental results on various medical segmentation tasks demonstrate that our model outperforms the state-of-the-art semi-supervised segmentation approaches. Crucially, our SAM distillation framework can be seamlessly integrated into other semi-supervised segmentation methods to enhance performance. The code will be released upon acceptance of this manuscript at https://***/taozh2017/KnowSAM.

关键词： image segmentation Biomedical imaging Training Data models Decoding Feature extraction Adaptation models Accuracy Computational modeling Uncertainty Semi-supervised learning medical image segmentation SAM knowledge distillation

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Auto feature weighted c-means type clustering methods for color image segmentation

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2025年 153卷

作者： Zhu, Sijia Liu, Zhe Letchmunan, Sukumar Qiu, Haoye Johns Hopkins Univ Dept Appl Math & Stat Baltimore MD 21218 USA Xinyu Univ Coll Math & Comp Xinyu 338004 Peoples R China Univ Sains Malaysia Sch Comp Sci Minden 11800 Malaysia Hainan Univ Sch Comp Sci & Technol Haikou 570228 Peoples R China

To address the limitations of existing hard c-means (HCM) and fuzzy c-means (FCM) methods, we develop four novel clustering methods: vector-weighted alternative hard c-means (VWAHCM), matrix-weighted alternative hard c-means (MWAHCM), vector-weighted alternative fuzzy c-means (VWAFCM), and matrix-weighted alternative fuzzy c-means (MWAFCM). These methods enhance clustering performance by incorporating nonEuclidean norm metrics and vector-weighted and matrix-weighted schemes without adding extra parameters. Our methods modify the traditional weight constraint from a sum to a product of weights, thereby improving robustness and accuracy. Comprehensive experiments conduct on various real-world datasets and color image segmentation tasks demonstrate the superiority of the proposed methods over traditional HCM and FCM variants. The results show significant improvements in clustering Accuracy (ACC), Normalized mutual information (NMI), Rand index (RI), and Fowlkes-Mallows index (FM). Furthermore, the proposed methods exhibit fast convergence and robust performance, proving their effectiveness in practical applications.

关键词： Hard c-means Fuzzy c-means Non-Euclidean norm Vector-weighted Matrix-weighted image segmentation

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