UPMAD-Net: A Brain Tumor Segmentation Network with Uncertainty Guidance and Adaptive Multimodal Feature Fusion
School of Computer Science and Technology, Zhengzhou University of Light Industry（郑州轻工业大学计算机与通信工程学院）；Kennesaw State University（肯尼索州立大学）；Michigan Technological University（密歇根理工大学）
Background: Brain tumor segmentation has a significant impact on the diagnosis and treatment of brain tumors. Accurate brain tumor segmentation remains challenging due to their irregular shapes, vague boundaries, and high variability. Objective: We propose a brain tumor segmentation method that combines deep learning with prior knowledge derived from a region-growing algorithm. Methods: The proposed method utilizes a multi-scale feature fusion (MSFF) module and adaptive attention mechanisms (AAM) to extract multi-scale features and capture global contextual information. To enhance the model's robustness in low-confidence regions, the Monte Carlo Dropout (MC Dropout) strategy is employed for uncertainty estimation. Results: Extensive experiments demonstrate that the proposed method achieves superior performance on Brain Tumor Segmentation (BraTS) datasets, significantly outperforming various state-of-the-art methods. On the BraTS2021 dataset, the test Dice scores are 89.18% for Enhancing Tumor (ET) segmentation, 93.67% for Whole Tumor (WT) segmentation, and 91.23% for Tumor Core (TC) segmentation. On the BraTS2019 validation set, the validation Dice scores are 87.43%, 90.92%, and 90.40% for ET, WT, and TC segmentation, respectively. Ablation studies further confirmed the contribution of each module to segmentation accuracy, indicating that each component played a vital role in overall performance improvement. Conclusion: This study proposed a novel 3D brain tumor segmentation network based on the U-Net architecture. By incorporating the prior knowledge and employing the uncertainty estimation method, the robustness and performance were improved. The code for the proposed method is available at https://github.com/chenzhao2023/UPMAD_Net_BrainSeg.
A Weakly Supervised Segmentation Network Embedding Cross-scale Attention Guidance and Noise-sensitive Constraint for Detecting Tertiary Lymphoid Structures of Pancreatic Tumors
Nanjing University of Science and Technology（南京理工大学）；Nanjing University（南京大学）；Nanjing Drum Tower Hospital（南京鼓楼医院）；Affiliated Hospital of Nanjing University of Chinese Medicine（南京中医药大学附属医院）
Decoupled Pyramid Correlation Network for Liver Tumor Segmentation from CT images
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；University of Southern California（南加利福尼亚大学）；Lenovo Research（联想研究院）；Lenovo Ltd.（联想有限公司）
PGR-Net: Prior-Guided ROI Reasoning Network for Brain Tumor MRI Segmentation
College of Information Engineering, Capital Normal University（首都师范大学信息工程学院）；School of Artificial Intelligence, China University of Mining and Technology-Beijing（中国矿业大学(北京)人工智能学院）
BrainNormalizer: Anatomy-Informed Pseudo-Healthy Brain Reconstruction from Tumor MRI via Edge-Guided ControlNet
University of Pittsburgh（匹兹堡大学）；Georgia Institute of Technology（佐治亚理工学院）；University of Texas at Austin（德克萨斯大学奥斯汀分校）；Cedars-Sinai Medical Center（西德萨斯医疗中心）；Mayo Clinic Arizona（梅奥诊所亚利桑那分部）
Synergistic Modality-and-Slice Memory Framework for Cross-Modal 3D Brain Tumor Segmentation
Graduate School of Information, Production and Systems, Waseda University, Japan（信息、生产与系统研究生院，早稻田大学，日本）；School of Computer Science, University of Lincoln, UK（林肯大学计算机科学学院，英国）；University of Nottingham, UK（诺丁汉大学，英国）；University of Nottingham Ningbo China, China（宁波诺丁汉大学，中国）；College of Electrical Engineering and Information, Northeast Agricultural University, Harbin, China（电气工程与信息学院，东北农业大学，中国）；College of Computer Science, Sichuan University, Chengdu, China（计算机科学学院，四川大学，中国）；Yale University, New Haven, CT 06510, USA（耶鲁大学，美国）；School of Engineering and Physical Science, University of Lincoln, Lincoln LN6 7TS, UK（工程与物理科学学院，林肯大学，英国）
Guiding the Guidance: A Comparative Analysis of User Guidance Signals for Interactive Segmentation of Volumetric Images
Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；HIDSS4Health - Helmholtz Information and Data Science School for Health（HIDSS4Health——亥姆霍兹健康信息与数据科学学院）；Institute for AI in Medicine, University Hospital Essen（埃森大学医院医学人工智能研究所）；Cancer Research Center Cologne Essen (CCCE), University Medicine Essen（埃森大学医学院科隆埃森癌症研究中心（CCCE））
Leveraging Semantic Asymmetry for Precise Gross Tumor Volume Segmentation of Nasopharyngeal Carcinoma in Planning CT
DAMO Academy, Alibaba Group（阿里巴巴集团达摩院）；The University of Hong Kong（香港大学）；The First Affiliated Hospital, Zhejiang University（浙江大学第一附属医院）；Hupan Lab（华盼实验室）
RESECT-SEG: Open access annotations of intra-operative brain tumor ultrasound images
Concordia University（康考迪亚大学）；Université Grenoble Alpes（格勒诺布尔阿尔卑斯大学）；CNRS（法国国家科学研究中心）；Grenoble INP（格勒诺布尔理工学院）；Western University（韦仕敦大学）；St. Olavs Hospital（圣奥拉夫医院）；Trondheim University Hospital（特隆赫姆大学医院）；Norwegian University of Science and Technology (NTNU)（挪威科技大学）；SINTEF Digital（SINTEF数字研究所）
目的：磁共振（MR）和超声（US）图像的配准和分割在脑肿瘤的手术规划和切除中起着至关重要的作用。然而，验证这些技术是具有挑战性的，因为缺乏具有高质量地面真相信息的公开来源。为此，我们提出了一个来自先前发表的RESECT数据集（Xiao et al. 2017）的肿瘤组织和切除腔的独特注释数据集，以鼓励对图像处理技术进行更严格的评估。采集和验证方法：RESECT数据库由23例接受切除手术的患者的MR和术中US（iUS）图像组成。所提出的数据集包含肿瘤组织和iUS图像的切除腔注释。两名经验丰富的神经外科医生通过几项评估标准验证了注释的质量。数据格式和可用性：肿瘤组织和切除腔的注释以3D NIFTI格式提供。这两套注释均可在\url{https://osf.io/6y4db}中在线访问。讨论和潜在应用：拟议的数据库包括来自真实世界临床超声脑图像的肿瘤组织和切除腔注释，以评估分割和配准方法。这些标签还可以用于训练深度学习方法。最终，该数据集将进一步提高神经外科中图像引导的质量。
East China Normal University（华东师范大学）；Johns Hopkins University（约翰斯·霍普金斯大学）；Shanghai Jiao Tong University（上海交通大学）；Johns Hopkins University School of Medicine（约翰斯·霍普金斯大学医学院）
SAM-Swin: SAM-Driven Dual-Swin Transformers with Adaptive Lesion Enhancement for Laryngo-Pharyngeal Tumor Detection
College of Big Data and Internet, Shenzhen Technology University（深圳技术大学大数据与互联网学院）；The First Affiliated Hospital, Sun Yat-Sen University（中山大学附属第一医院）；Aberdeen Institute of Data Science and Artificial Intelligence, South China Normal University（华南师范大学阿伯丁数据科学与人工智能学院）
Pancreatic Tumor Segmentation as Anomaly Detection in CT Images Using Denoising Diffusion Models
School of Electrical and Computer Engineering, University of Oklahoma（俄克拉荷马大学电气与计算机工程学院）；University of Oklahoma Health Sciences Center（俄克拉荷马大学健康科学中心）；School of Computer Science, University of Oklahoma（俄克拉荷马大学计算机科学学院）
Mind the Gap: Promoting Missing Modality Brain Tumor Segmentation with Alignment
School of Robotics Xi’an Jiaotong-Liverpool University Suzhou, China；School of Advanced Technology Xi'an Jiaotong-Liverpool University Suzhou, China；School of Advanced Technology Xi’an Jiaotong-Liverpool University Suzhou, China；Data Science Research Center Duke Kunshan University Suzhou, China
Intra-operative Brain Tumor Detection with Deep Learning-Optimized Hyperspectral Imaging
Carl Zeiss AG（卡尔蔡司股份公司）；University of Padua（帕多瓦大学）；University of Applied Sciences Western Switzerland (HES-SO Valais)（瑞士西部应用科学大学（瓦莱州高等专业学院））；Carl Zeiss Meditec AG（卡尔蔡司医疗科技股份公司）；IMEC（比利时微电子研究中心）；University Hospitals Leuven（鲁汶大学医院）；KU Leuven（鲁汶大学）
CDPDNet: Integrating Text Guidance with Hybrid Vision Encoders for Medical Image Segmentation
J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida（J. Crayton Pruitt家庭生物医学工程系，佛罗里达大学）；Department of Medicine, University of Florida（医学系，佛罗里达大学）
Separated Contrastive Learning for Organ-at-Risk and Gross-Tumor-Volume Segmentation with Limited Annotation
Xiamen University（厦门大学）；The Hong Kong University of Science and Technology（香港科技大学）；Peking University（北京大学）；The Hong Kong Polytechnic University（香港理工大学）；Manteia Technologies Co.,Ltd（Manteia科技有限公司）
Hepatocellular Carcinoma Segmentation from Digital Subtraction Angiography Videos using Learnable Temporal Difference
The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Shenzhen Research Institute of Big Data（深圳大数据研究院）；The First Affiliated Hospital of Jinan University（暨南大学附属第一医院）
Multi-modal Evidential Fusion Network for Trustworthy PET/CT Tumor Segmentation
Soochow University（苏州大学）；the University of Hong Kong（香港大学）；Southern University of Science and Technology（南方科技大学）；Changshu Institute of Technology（常熟理工学院）；the First Affiliated Hospital of Soochow University（苏州大学附属第一医院）
MPFlow: Multi-modal Posterior-Guided Flow Matching for Zero-Shot MRI Reconstruction
1 Hawkes Institute, UCL \, 2 Dept. of Medical Physics；Biomedical Engineering, UCL 3 Dept. of Computer Science, UCL 4 Centre for AI, DS\&AI, AstraZeneca, UK
Towards Trustworthy AI for Glioma Diagnosis: A Task-Aware Evaluation of Uncertainty Quantification
Erasmus MC, University Medical Center Rotterdam（鹿特丹大学医学中心伊拉斯谟MC）；Amsterdam UMC, University of Amsterdam（阿姆斯特丹大学医学中心阿姆斯特丹UMC）；GE HealthCare（GE医疗）；Erasmus MC Cancer Institute（伊拉斯谟MC癌症研究所）；Medical Delta（医疗三角洲）
The Brain Resection Multimodal Image Registration (ReMIND2Reg) 2025 Challenge
Inria Saclay Île-de-France（法国里昂萨克利研究所）；CEA（法国原子能委员会）；Université Paris-Saclay（巴黎-萨克利大学）；Sorbonne Université（索邦大学）；Institut du Cerveau - Paris Brain Institute - ICM（巴黎脑研究所）；CNRS（法国国家科学研究中心）；Inria（法国国家信息与自动化技术研究所）；Inserm（法国国家医学研究院）；AP-HP（法国国家医院集团）；Hôpital de la Pitié Salpêtrière（皮蒂埃-萨尔普里埃尔医院）；Harvard Medical School（哈佛医学院）；Brigham and Women's Hospital（布里根医院）；Department of Radiology and Radiological Science（放射学与放射科学系）；Johns Hopkins Medical School（约翰霍普金斯医学院）；Institute of Medical Informatics（医学信息学研究所）；University of Lübeck（吕贝克大学）；Image Analysis and Communications Laboratory（图像分析与通信实验室）；Department of Electrical and Computer Engineering（电气与计算机工程系）；Johns Hopkins University（约翰霍普金斯大学）；CSAIL（媒体实验室）
REACT-KD: Region-Aware Cross-modal Topological Knowledge Distillation for Interpretable Medical Image Classification
Department of Health Technology and Informatics, Hong Kong Polytechnic University（健康科技与信息技术系，香港理工大学）；Department of Nuclear Medicine and PET, Hong Kong Sanatorium and Hospital（核医学与PET部门，香港疗养院及医院）
