Magnetic resonance imaging (MRI) provides varying tissue contrast images of internal organs based on a strong magnetic field. Despite the non-invasive advantage of MRI in frequent imaging, the low contrast MR images in the target area make tissue segmentation a challenging problem. This paper demonstrates the potential benefits of image-to-image translation techniques to generate synthetic high tissue contrast (HTC) images. Notably, we adopt a new cycle generative adversarial network (CycleGAN) with an attention mechanism to increase the contrast within underlying tissues. The attention block, as well as training on HTC images, guides our model to converge on certain tissues. To increase the resolution of HTC images, we employ multi-stage architecture to focus on one particular tissue as a foreground and filter out the irrelevant background in each stage. This multi-stage structure also alleviates the common artifacts of the synthetic images by decreasing the gap between source and target domains. We show the application of our method for synthesizing HTC images on brain MR scans, including glioma tumor. We also employ HTC MR images in both the end-to-end and two-stage segmentation structure to confirm the effectiveness of these images. The experiments over three competitive segmentation baselines on BraTS 2018 dataset indicate that incorporating the synthetic HTC images in the multi-modal segmentation framework improves the average Dice scores 0.8%, 0.6%, and 0.5% on the whole tumor, tumor core, and enhancing tumor, respectively, while eliminating one real MRI sequence from the segmentation procedure.
Rectified flow-based prediction of post-treatment brain MRI from pre-radiotherapy priors for patients with glioma
Amsterdam UMC, Department of Radiation Oncology（阿姆斯特丹大学医学中心放射肿瘤科）；Cancer Center Amsterdam, Imaging and Biomarkers（阿姆斯特丹癌症中心影像与生物标志物）；Department of Radiology and Nuclear Medicine（放射科与核医学科）；Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Brain Imaging（阿姆斯特丹大学医学中心、阿姆斯特丹自由大学、阿姆斯特丹神经科学、脑成像）
Adaptive PromptNet For Auxiliary Glioma Diagnosis without Contrast-Enhanced MRI
Zhengzhou University（郑州大学）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Peng Cheng Laboratory（鹏城实验室）；Guangdong Provincial People’s Hospital（广东省人民医院）；Guangdong Academy of Medical Sciences（广东省医学科学院）
Deep learning-based group-wise registration for longitudinal MRI analysis in glioma
Erasmus MC（伊拉斯姆斯大学医学中心）；Medical Delta（医疗三角洲）；Delft University of Technology（代尔夫特理工大学）；Amsterdam UMC / VU Medical Center（阿姆斯特丹大学医学中心/自由大学医学中心）；Princess Máxima Center for Pediatric Oncology（马克西玛公主儿童肿瘤中心）；The Jackson Laboratory for Genomic Medicine（杰克逊基因组医学实验室）
Treatment-aware Diffusion Probabilistic Model for Longitudinal MRI Generation and Diffuse Glioma Growth Prediction
Oslo University Hospital（奥斯陆大学医院）；Universitat Politècnica de València（瓦伦西亚理工大学）；Instituto Universitario de Tecnologías de la Información y Comunicaciones（信息技术与通信大学学院）
Glioma Multimodal MRI Analysis System for Tumor Layered Diagnosis via Multi-task Semi-supervised Learning
Cancer Research Institute, Central South University（中南大学肿瘤研究所）；School of Physics & Electronic Science, Changsha University of Science & Technology（长沙理工大学物理与电子科学学院）；IFLYTEK Research（iFlytek研究院）；School of Life Sciences, Central South University（中南大学生命科学学院）；Department of Radiology, Xiangya Hospital, Central South University（中南大学湘雅医院放射科）；School of Computer Science and Engineering, Central South University（中南大学计算机科学与工程学院）；Department of Neurosurgery, Xiangya Hospital, Central South University（中南大学湘雅医院神经外科）
Intraoperative Glioma Segmentation with YOLO + SAM for Improved Accuracy in Tumor Resection
Carnegie Vanguard High School（卡内基先锋高中）；Devon Preparatory High School（德文预备高中）；Lake Travis High School（特拉维斯湖高中）；Irvington High School（欧文顿高中）；Algoverse AI Research（Algoverse AI研究机构）
Targeted Neural Architectures in Multi-Objective Frameworks for Complete Glioma Characterization from Multimodal MRI
School of Computer Science and Engineering, Vellore Institute of Technology（韦洛尔理工学院计算机科学与工程学院）；College of Engineering, Guindy, Anna University（安娜大学金迪工程学院）；Institute of Neurosurgery, Madras Medical College（马德拉斯医学院神经外科研究所）
MRI-based classification of IDH mutation and 1p/19q codeletion status of gliomas using a 2.5D hybrid multi-task convolutional neural network
Washington University in St. Louis（圣路易斯华盛顿大学）；Mallinckrodt Institute of Radiology（马林克罗特放射研究所）；Washington University School of Medicine（华盛顿大学医学院）；Institute for Informatics（信息学研究所）
异柠檬酸脱氢酶（IDH）突变和1 p/19 q共缺失状态是胶质瘤的重要预后标志。目前，它们是使用侵入性程序确定的。我们的目标是开发基于人工智能的方法来非侵入性地确定MRI中的这些分子改变。为此，2648例胶质瘤患者的术前MRI扫描（II-IV级）从华盛顿大学医学院收集（WUSM; n = 835）和公开可用的数据集，即脑肿瘤分割（BraTS; n = 378），LGG 1 p/19 q（n = 159），Ivy胶质母细胞瘤Atlas项目（Ivy GAP; n = 41）、癌症基因组图谱（TCGA; n = 461）和伊拉斯谟神经胶质瘤数据库（EGD; n = 774）。提出了一种2.5D混合卷积神经网络，通过利用来自MR扫描的成像特征和来自临床记录和肿瘤位置的先验知识特征，同时定位肿瘤并对其分子状态进行分类。模型在一个内部（TCGA）和两个外部（WUSM和EGD）测试集上进行了测试。对于IDH，表现最好的模型在内部、WUSM和EGD测试集上分别实现了0.925、0.874、0.933的受试者操作特征下的面积（AUROC）和0.899、0.702、0.853的精确-召回曲线下的面积（AUPRC）。对于1 p/19 q，最佳模型在这三个数据分割上分别实现了0.782，0.754，0.842的AUROC和0.588，0.713，0.782的AUPRC。该模型对未知数据的高准确性展示了其泛…
Transferring Knowledge from High-Quality to Low-Quality MRI for Adult Glioma Diagnosis
The Chinese University of Hong Kong (CUHK)（香港中文大学）；CUHK Shenzhen Research Institute（香港中文大学深圳研究院）；China Medical University（中国医科大学）；University College London（伦敦大学学院）；UCL Hawkes Institute（伦敦大学学院霍克斯研究所）
Predicted disease compositions of human gliomas estimated from multiparametric MRI can predict endothelial proliferation, tumor grade, and overall survival
Indiana University School of Medicine（印第安纳大学医学院）；Purdue University（普渡大学）；Dayton Children’s Hospital（代顿儿童医院）
Improving the Segmentation of Pediatric Low-Grade Gliomas through Multitask Learning
The Hospital for Sick Children（病童医院）；University of Toronto（多伦多大学）；Vector Institute（矢量研究所）
脑肿瘤分割是肿瘤体积分析和人工智能算法的关键任务。然而，这是一个耗时的过程，需要神经放射学专业知识。虽然已经有广泛的研究集中在优化成人人群中的脑肿瘤分割，但关于AI引导的儿科肿瘤分割的研究很少。此外，儿童和成人脑肿瘤的MRI信号特征不同，需要开发专门为儿童脑肿瘤设计的分割算法。我们开发了一个分割模型，该模型在来自The Hospital for Sick Children（加拿大安大略省多伦多市）的患有低级胶质瘤（pLGG）的儿科患者的磁共振成像（MRI）上进行训练。该模型利用深度多任务学习（dMTL），将肿瘤的遗传变异分类器作为辅助任务添加到主网络中，最终提高分割结果的准确性。
Diagnostic performance of deep learning for predicting glioma isocitrate dehydrogenase and 1p/19q co-deletion in MRI: a systematic review and meta-analysis
MRI Tumor Segmentation with Densely Connected 3D CNN
Department of Computer Science, University of Rochester（罗切斯特大学计算机科学系）；Department of Electrical Engineering, University of Rochester（罗切斯特大学电气工程系）；Department of Biomedical Engineering, University of Rochester（罗切斯特大学生物医学工程系）；Department of Imaging Sciences, University of Rochester（罗切斯特大学影像科学系）；Faculty of Medicine and Institute of Clinical Radiology, Ludwig Maximilian University（路德维希-马克西米利安大学医学院及临床放射学研究所）
A Benchmark of (MRI-) Foundation Models to Predict IDH Mutational Status in Glioma
Department of Radiation Oncology, Inselspital, Bern University Hospital and University of Bern（伯尔尼大学医院因塞尔斯皮塔尔放射肿瘤科）；University of Bern（伯尔尼大学）；Department of Digital Medicine, University of Bern（伯尔尼大学数字医学系）；School of Life Sciences, FHNW University of Applied Sciences and Arts, Muttenz（FHNW应用科学与艺术大学生命科学学院）；School of Business, FHNW University of Applied Sciences and Arts, Olten（FHNW应用科学与艺术学院商学院）；University Children’s Hospital Basel UKBB（巴塞尔儿童医院UKBB）
Training Beyond Convergence: Grokking nnU-Net for Glioma Segmentation in Sub-Saharan MRI
Multimedia University（多媒体大学）；University of British Columbia（不列颠哥伦比亚大学）；McGill University（麦吉尔大学）；Montreal Neurological Institute（蒙特利尔神经学研究所）；Federal University of Technology Minna（明纳联邦理工大学）；Medical Artificial Intelligence Laboratory（医学人工智能实验室）；Friedrich-Alexander-Universität（弗里德里希-亚历山大大学）
Applications of artificial intelligence in the analysis of histopathology images of gliomas: a review
Fraunhofer Institute for Digital Medicine MEVIS（弗劳恩霍夫数字医学研究所MEVIS）；Hannover Medical School（汉诺威医学院）；Medical Center - University of Freiburg（弗莱堡大学医学中心）；RWTH Aachen University Hospital（亚琛工业大学医院）；Klinikum Bremen-Mitte（不来梅中部医院）；Leibniz Institute for Prevention Research and Epidemiology - BIPS（莱布尼茨预防研究与流行病学研究所）；Bremen Cancer Registry（不来梅癌症登记处）；Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB（弗劳恩霍夫光电、系统技术及图像处理研究所IOSB）；German Heart Center Berlin（柏林德国心脏中心）
Generative Style Transfer for MRI Image Segmentation: A Case of Glioma Segmentation in Sub-Saharan Africa
Institute of Mathematical Science, Strathmore University（斯特拉斯莫尔大学数学科学学院）；Faculty of Science and Engineering, University of Hull（赫尔大学科学与工程学院）；Technical University of Mombasa（蒙巴萨理工大学）；Lawson Health Research Institute（劳森健康研究所）；University of British Columbia（不列颠哥伦比亚大学）；University of Nairobi（内罗毕大学）；University of Cape Town（开普敦大学）；Montreal Neurological Institute, McGill University（麦吉尔大学蒙特利尔神经学研究所）；Brain Mind Institute, The Aga Khan University（阿迦汗大学脑与心智研究所）；Medical Physics Unit, McGill University（麦吉尔大学医学物理部门）
Quantifying U-Net Uncertainty in Multi-Parametric MRI-based Glioma Segmentation by Spherical Image Projection
Duke University（杜克大学）；Duke Kunshan University（昆山杜克大学）；The University of Texas MD Anderson Cancer Center（德克萨斯大学MD安德森癌症中心）；The University of Texas MD Anderson Graduate School of Biomedical Science（德克萨斯大学MD安德森生物医学研究生院）
Improving cross-domain brain tissue segmentation in fetal MRI with synthetic data
Lausanne University Hospital（洛桑大学医院）；University of Lausanne (UNIL)（洛桑大学）；CIBM Center for Biomedical Imaging（CIBM生物医学成像中心）；King’s College London（伦敦国王学院）；FAU Erlangen-Nuremberg（埃尔朗根-纽伦堡大学）
Soft Tissue Sarcoma Co-Segmentation in Combined MRI and PET/CT Data
VRVis Zentrum für Virtual Reality und Visualisierung Forschungs-GmbH（VRVis虚拟现实与可视化研究有限公司）；Center for Medical Physics and Biomedical Engineering, Medical University of Vienna（维也纳医科大学医学物理与生物医学工程中心）；Center for Nuclear Medicine, Clinical Center of Serbia（塞尔维亚临床中心核医学中心）
Unsupervised learning of MRI tissue properties using MRI physics models
Harvard Medical School（哈佛医学院）；Massachusetts General Hospital（麻省总医院）；California Institute of Technology（加州理工学院）；Massachusetts Institute of Technology（麻省理工学院）
Comparative Study of Probabilistic Atlas and Deep Learning Approaches for Automatic Brain Tissue Segmentation from MRI Using N4 Bias Field Correction and Anisotropic Diffusion Pre-processing Techniques
University of Girona（赫罗纳大学）；University of Burgundy（勃艮第大学）；University of Cassino and Southern Lazio（卡西诺和南拉齐奥大学）
