Magnetic Resonance Imaging (MRI) is a non-invasive diagnostic and radiotherapy (RT) planning tool, offering detailed insights into the anatomy of the human body. The extensive scan time is stressful for patients, who must remain motionless in a prolonged imaging procedure that prioritizes reduction of imaging artifacts. This is challenging for pediatric patients who may require measures for managing voluntary motions such as anesthesia. Several computational approaches reduce scan time (fast MRI), by recording fewer measurements and digitally recovering full information via post-acquisition reconstruction. However, most fast MRI approaches were developed for diagnostic imaging, without addressing reconstruction challenges specific to RT planning. In this work, we developed a deep learning-based method (DeepMRIRec) for MRI reconstruction from undersampled data acquired with RT-specific receiver coil arrangements. We evaluated our method against fully sampled data of T1-weighted MR images acquired from 73 children with brain tumors/surgical beds using loop and posterior coils (12 channels), with and without applying virtual compression of coil elements. DeepMRIRec reduced scanning time by a factor of four producing a structural similarity score surpassing the evaluated state-of-the-art method (0.960 vs 0.896), thereby demonstrating its potential for accelerating MRI scanning for RT planning.
Tumor Location-weighted MRI-Report Contrastive Learning: A Framework for Improving the Explainability of Pediatric Brain Tumor Diagnosis
The Hospital for Sick Children（病童医院）；University of Toronto（多伦多大学）；Vector Institute for Artificial Intelligence（矢量人工智能研究所）；University Hospital Augsburg（奥格斯堡大学医院）
Clinically-Informed Modeling for Pediatric Brain Tumor Classification from Whole-Slide Histopathology Images
Dept. of Computer Science（计算机科学系）；University of Texas at Austin（德克萨斯大学奥斯汀分校）；School of Information（信息学院）；Dell Children's Medical Center（德尔儿童医学中心）；Dept. of OREI（OREI部门）；University of Nevada, Reno（内华达大学里诺分校）
Comparative Analysis of nnUNet and MedNeXt for Head and Neck Tumor Segmentation in MRI-guided Radiotherapy
Sharif University of Technology（谢里夫理工大学）；Institute for AI in Medicine (IKIM), University Medicine Essen（埃森大学医学中心人工智能医学研究所）；Center Algoritmi / LASI, University of Minho（米尼奥大学Algoritmi中心/LASI）；University Hospital RWTH Aachen（亚琛工业大学附属医院）；Cancer Research Center Cologne Essen (CCCE), University Medicine Essen (AöR)（埃森大学医学中心（公法团体）科隆埃森癌症研究中心）；German Cancer Consortium (DKTK), Partner Site Essen（德国癌症联盟埃森合作站点）；TU Dortmund University（多特蒙德工业大学）
A multi-institutional pediatric dataset of clinical radiology MRIs by the Children's Brain Tumor Network
Children’s Hospital of Philadelphia（费城儿童医院）；Perelman School of Medicine, University of Pennsylvania（宾夕法尼亚大学佩雷尔曼医学院）；National Cancer Institute（美国国家癌症研究所）；Children's National Hospital（儿童国家医院）；Sheikh Zayed Institute for Pediatric Surgical Innovation（谢赫扎耶德儿科外科创新研究院）
Magnetic resonance delta radiomics to track radiation response in lung tumors receiving stereotactic MRI-guided radiotherapy
Mass General Brigham（麻省总医院布里格姆医疗系统）；Harvard Medical School（哈佛医学院）；Dana-Farber Cancer Institute（丹娜-法伯癌症研究所）；Harvard T.H. Chan School of Public Health（哈佛大学陈曾熙公共卫生学院）；Maastricht University（马斯特里赫特大学）
Head and Neck Tumor Segmentation of MRI from Pre- and Mid-radiotherapy with Pre-training, Data Augmentation and Dual Flow UNet
University of Electronic Science and Technology of China（电子科技大学）；Shanghai AI Laboratory（上海人工智能实验室）；Sichuan Cancer Hospital & Institute, Sichuan Cancer Center（四川省肿瘤医院·研究所（四川省癌症防治中心））
Pediatric brain tumor classification using digital histopathology and deep learning: evaluation of SOTA methods on a multi-center Swedish cohort
Department of Biomedical Engineering, Linköping University, Sweden（生物医学工程系，利厄普大学）；Center for Medical Image Science and Visualization, Linköping University, Sweden（医学影像科学与可视化中心，利厄普大学）；Crown Princess Victoria Children’s Hospital and Department of Health, Medicine and Caring Sciences, Linköping University, Sweden（维多利亚公主儿童医院和健康、医学与关怀科学系，利厄普大学）；Department of Radiology and Department of Health, Medicine and Caring Sciences, Linköping University, Sweden（放射科和健康、医学与关怀科学系，利厄普大学）；Department of Oncology-Pathology, Karolinska Institutet, Solna, Sweden（肿瘤病理学系，卡罗林斯卡研究所，索纳）；Department of Clinical Pathology and Cancer Diagnostics, Karolinska University Hospital, Sweden（临床病理学和癌症诊断系，卡罗林斯卡大学医院）；Department of Radiation Physics and Department of Medical and Health Sciences, Linköping University, Sweden（放射物理系和医学与健康科学系，利厄普大学）
The Segment Anything foundation model achieves favorable brain tumor autosegmentation accuracy on MRI to support radiotherapy treatment planning
Universitätsklinikum Erlangen（埃尔兰根大学医院）；Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希·亚历山大大学）；Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN)（埃尔兰根-欧洲分子神经科学网络综合癌症中心）
In the past few years, deep learning (DL) models have drawn great attention and shown superior performance on brain tumor and subregion segmentation tasks. However, the success is limited to segmentation of adult gliomas, where sufficient data have been collected, manually labeled, and published for training DL models. It is still challenging to segment pediatric tumors, because the appearances are different from adult gliomas. Hence, directly applying a pretained DL model on pediatric data usually generates unacceptable results. Because pediatric data is very limited, both labeled and unlabel…
TrackRAD2025 challenge dataset: Real-time tumor tracking for MRI-guided radiotherapy
Sichuan Cancer Hospital & Institute（四川省肿瘤医院研究所）；University of Electronic Science and Technology of China（电子科技大学）；LMU University Hospital（LMU大学医院）；LMU Munich（慕尼黑大学）；Catharina Hospital（凯瑟琳医院）；Mater Olbia Hospital（奥尔比亚 Mater 医院）；University Medical Center Utrecht（乌得勒支大学医学中心）；Ludwig-Maximilians-Universität München（慕尼黑大学）；Czech Technical University in Prague（布拉格捷克理工大学）；University of Sydney（悉尼大学）；Fondazione Policlinico Universitario Agostino Gemelli, IRCCS（阿戈斯蒂诺·杰梅利大学综合医院基金会 IRCCS）；Abano Terme Hospital（阿巴诺泰尔梅医院）；GenesisCare；St Vincent’s Hospital（圣文森特医院）；Amsterdam UMC location Vrije Universiteit Amsterdam（阿姆斯特丹自由大学医学中心）；Technical University Eindhoven（埃因霍温理工大学）
SegRap2023: A Benchmark of Organs-at-Risk and Gross Tumor Volume Segmentation for Radiotherapy Planning of Nasopharyngeal Carcinoma
University of Electronic Science and Technology of China（电子科技大学）；Sichuan Cancer Hospital & Institute（四川省肿瘤医院（研究所））；Shanghai AI Lab（上海人工智能实验室）；Canon Medical Systems (China) Co. Ltd.（佳能医疗系统（中国）有限公司）；Stockholm University（斯德哥尔摩大学）；KTH（瑞典皇家理工学院）；Northwestern Polytechnical University（西北工业大学）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
A Multi-Stage Deep Learning Framework with PKCP-MixUp Augmentation for Pediatric Liver Tumor Diagnosis Using Multi-Phase Contrast-Enhanced CT
Wuhan Children's Hospital (Wuhan Maternal and Child Healthcare Hospital), Tongji Medical College, Huazhong University of Science and Technology（华中科技大学同济医学院武汉儿童医院（武汉市妇幼保健院））；Wuhan University（武汉大学）；Wuhan Cloud Computing Technology Co., Ltd.（武汉云计算技术有限公司）
National Tsing Hua University（国立清华大学）；Chang Gung Memorial Hospital（长庚纪念医院）；PAII Inc（PAII公司）
Gross tumor volume (GTV) delineation on tomography medical imaging is crucial for radiotherapy planning and cancer diagnosis. Convolutional neural networks (CNNs) has been predominated on automatic 3D medical segmentation tasks, including contouring the radiotherapy target given 3D CT volume. While CNNs may provide feasible outcome, in clinical scenario, double-check and prediction refinement by experts is still necessary because of CNNs' inconsistent performance on unexpected patient cases. To provide experts an efficient way to modify the CNN predictions without retrain the model, we propose…
The Hospital for Sick Children（病童医院）；University of Toronto（多伦多大学）；Vector Institute（矢量研究所）
Brain tumor segmentation is a critical task for tumor volumetric analyses and AI algorithms. However, it is a time-consuming process and requires neuroradiology expertise. While there has been extensive research focused on optimizing brain tumor segmentation in the adult population, studies on AI guided pediatric tumor segmentation are scarce. Furthermore, MRI signal characteristics of pediatric and adult brain tumors differ, necessitating the development of segmentation algorithms specifically designed for pediatric brain tumors. We developed a segmentation model trained on magnetic resonance…
A No-Reference Medical Image Quality Assessment Method Based on Automated Distortion Recognition Technology: Application to Preprocessing in MRI-guided Radiotherapy
Beijing University of Posts and Telecommunications（北京邮电大学）；National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College（中国医学科学院北京协和医学院国家癌症中心/国家癌症临床医学研究中心/肿瘤医院）
Automated ensemble method for pediatric brain tumor segmentation
Manning College of Information and Computer Sciences, University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校曼宁信息与计算机科学学院）；Institute for Applied Life Sciences, University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校应用生命科学研究所）；UMass Chan Medical School - Baystate Campus（马萨诸塞大学陈医学院贝塞特校区）；Baystate Medical Center（贝塞特医疗中心）
Wuhan Children’s Hospital affiliated with Tongji Medical College（同济医学院附属武汉儿童医院）；Huazhong University of Science and Technology（华中科技大学）
Neuroblastoma is one of the most common cancers in infants, and the initial diagnosis of this disease is difficult. At present, the MYCN gene amplification (MNA) status is detected by invasive pathological examination of tumor samples. This is time-consuming and may have a hidden impact on children. To handle this problem, we adopt multiple machine learning (ML) algorithms to predict the presence or absence of MYCN gene amplification. The dataset is composed of retrospective CT images of 23 neuroblastoma patients. Different from previous work, we develop the algorithm without manually-segmente…
A New Logic For Pediatric Brain Tumor Segmentation
1 Northwestern University, Feinberg School of Medicine, Chicago IL, USA 2 Children’s National Hospital, Sheikh Zayed Institute for Pediatric Surgical Innovation, Washington, DC, USA 3 Ann \& Robert H Lurie Children's Hospital of Chicago, Chicago, IL, USA
University of São Paulo（圣保罗大学）；Instituto da Criança – HC-FM（儿童研究所——医学院附属医院）
The importance of imaging exams, such as Magnetic Resonance Imaging (MRI), for the diagnostic and follow-up of pediatric pathologies and the assessment of anatomical structures' development has been increasingly highlighted in recent times. Manual analysis of MRIs is time-consuming, subjective, and requires significant expertise. To mitigate this, automatic techniques are necessary. Most techniques focus on adult subjects, while pediatric MRI has specific challenges such as the ongoing anatomical and histological changes related to normal development of the organs, reduced signal-to-noise rati…
Towards Reliable Pediatric Brain Tumor Segmentation: Task-Specific nnU-Net Enhancements
Institute of Natural Sciences, School of Mathematical Sciences, MOE-LSC, Shanghai Jiao Tong University（自然科学院，数学科学学院，MOE-LSC，上海交通大学）；Department of Radiology, Huashan Hospital, Fudan University（放射科，华山医院，复旦大学）；Department of Neurosurgery, Huashan Hospital, Fudan University（神经外科，华山医院，复旦大学）
Frequency-Aware Ensemble Learning for BraTS 2025 Pediatric Brain Tumor Segmentation
Institute of Natural Sciences, School of Mathematical Sciences, MOE-LSC, Shanghai Jiao Tong University（自然科学院、数学科学学院、MOE-LSC、上海交通大学）；Department of Radiology, Huashan Hospital, Fudan University（放射科、华山医院、复旦大学）；Department of Neurosurgery, Huashan Hospital, Fudan University（神经外科、华山医院、复旦大学）
The Hong Kong Polytechnic University（香港理工大学）；Research Institute for Smart Ageing, The Hong Kong Polytechnic University（香港理工大学智慧老龄研究学院）；Nantong University（南通大学）
The prediction of adaptive radiation therapy (ART) prior to radiation therapy (RT) for nasopharyngeal carcinoma (NPC) patients is important to reduce toxicity and prolong the survival of patients. Currently, due to the complex tumor micro-environment, a single type of high-resolution image can provide only limited information. Meanwhile, the traditional softmax-based loss is insufficient for quantifying the discriminative power of a model. To overcome these challenges, we propose a supervised multi-view contrastive learning method with an additive margin (MMCon). For each patient, four medical…
Towards Label-Free Brain Tumor Segmentation: Unsupervised Learning with Multimodal MRI
Universitat Politècnica de Catalunya (UPC)（西班牙巴塞罗那理工大学）；University College Dublin (UCD)（都柏林大学）；Dublin City University (DCU)（都柏林城市大学）；Insight Research Ireland Center For Data Analytics（爱尔兰数据分析研究中心）
The Brain Tumor Segmentation in Pediatrics (BraTS-PEDs) Challenge: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs)
Center for Data-Driven Discovery in Biomedicine (D3b)（生物医学数据驱动发现中心）；Children’s Hospital of Philadelphia（费城儿童医院）；Department of Neurosurgery, University of Pennsylvania（宾夕法尼亚大学神经外科）；Center for AI & Data Science for Integrated Diagnostics (AI2D)（综合诊断人工智能与数据科学中心）；Center for Biomedical Image Computing and Analytics (CBICA)（生物医学图像计算与分析中心）；Sheikh Zayed Institute for Pediatric Surgical Innovation, Children’s National Hospital（儿童医院谢赫·扎耶德儿科外科创新研究所）；Sage Bionetworks（赛基生物网络公司）；Medical Artificial Intelligence (MAI) Lab, Crestview Radiology（Crestview放射科医学人工智能实验室）；Montreal Neurological Institute (MNI), McGill University（麦吉尔大学蒙特利尔神经学研究所）；Department of Radiology, Perelman School of Medicine, University of Pennsylvania（宾夕法尼亚大学佩雷尔曼医学院放射科）；Division of Computational Pathology, Department of Pathology and Laboratory Medicine, Indiana University School of Medicine（印第安纳大学医学院病理学与实验医学系计算病理学分部）；Department of Neurosurgery at the University of Southern California（南加州大学神经外科）；Department of Radiology, Duke University Medical Center（杜克大学医学中心放射科）；Mayo Clinic（梅奥诊所）；Center for Global Health, Perelman School of Medicine, University of Pennsylvania（宾夕法尼亚大学佩雷尔曼医学院全球健康中心）；Department of Informatics, Technical University Munich（慕尼黑工业大学信息学系）；TranslaTUM - Central Institute for Translational Cancer Research, Technical University of Munich（慕尼黑工业大学TranslaTUM转化癌症研究中心）
Improving Pediatric Low-Grade Neuroepithelial Tumors Molecular Subtype Identification Using a Novel AUROC Loss Function for Convolutional Neural Networks
The Hospital for Sick Children (SickKids)（病童医院 (SickKids)）；SickKids Research Institute（SickKids研究所）；University of Toronto（多伦多大学）；University Hospital Augsburg（奥格斯堡大学医院）；Vector Institute（Vector研究所）
PAII Inc.（PAII公司）；Johns Hopkins University（约翰斯·霍普金斯大学）；Chang Gung Memorial Hospital（长庚纪念医院）；The First Affiliated Hospital Zhejiang University（浙江大学附属第一医院）；National Tsing Hua University（清华大学（台湾））；Ping An Technology（平安科技）
Finding, identifying and segmenting suspicious cancer metastasized lymph nodes from 3D multi-modality imaging is a clinical task of paramount importance. In radiotherapy, they are referred to as Lymph Node Gross Tumor Volume (GTVLN). Determining and delineating the spread of GTVLN is essential in defining the corresponding resection and irradiating regions for the downstream workflows of surgical resection and radiotherapy of various cancers. In this work, we propose an effective distance-based gating approach to simulate and simplify the high-level reasoning protocols conducted by radiation o…
BraTS-PEDs: Results of the Multi-Consortium International Pediatric Brain Tumor Segmentation Challenge 2023
Center for Data-Driven Discovery in Biomedicine (D3b)（数据驱动生物医学发现中心）；The Children’s Hospital of Philadelphia（费城儿童医院）；Sheikh Zayed Institute for Pediatric Surgical Innovation（谢赫扎伊德儿童外科创新研究所）；Children’s National Hospital（儿童国家医院）；Department of Neurosurgery（神经外科部门）；Dana-Farber Brigham Cancer Center & Boston Children’s Hospital（达纳-法伯布里弗癌症中心及波士顿儿童医院）；Department of Paediatric Radiology（儿科放射科部门）；University of Utah（犹他大学）；Cincinnati Children’s Hospital Medical Center（辛辛那提儿童医学中心）；Department of Radiology（放射科部门）；Division of Neuroradiology, Department of Radiology, Boston Children’s Hospital, Harvard Medical School（神经放射科，放射科，波士顿儿童医院，哈佛医学院）；College of Arts and Sciences, University of Pennsylvania（艺术与科学学院，宾夕法尼亚大学）；Department of Diagnostic and Interventional Radiology, All India Institute of Medical Sciences（诊断与介入放射科，全印度医学科学院）；Department of Diagnostic & Interventional Imaging, Neuroradiology section, The University of Texas Health at Houston, Texas, USA（诊断与介入成像部门，神经放射科，德克萨斯大学健康科学中心，德克萨斯，美国）；Beth Israel Deaconess Medical Center, Harvard Medical School（贝斯以色列医疗中心，哈佛医学院）；UNIDAD DE PATOLOGIA CLINICA（临床病理部门）
The Brain Tumor Segmentation (BraTS) Challenge 2023: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs)
Center for Data-Driven Discovery in Biomedicine (D3b), Children’s Hospital of Philadelphia（费城儿童医院生物医学数据驱动发现中心）；University of Pennsylvania（宾夕法尼亚大学）；Center for AI & Data Science for Integrated Diagnostics (AI2D)（综合诊断人工智能与数据科学中心）；Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania（宾夕法尼亚大学生物医学图像计算与分析中心）；Sheikh Zayed Institute for Pediatric Surgical Innovation, Children’s National Hospital（儿童医院谢赫·扎耶德儿科外科创新研究所）；Thomas Jefferson University Hospital（托马斯杰斐逊大学医院）；Sage Bionetworks；Montreal Neurological Institute (MNI), McGill University（麦吉尔大学蒙特利尔神经学研究所）；Perelman School of Medicine, University of Pennsylvania（宾夕法尼亚大学佩雷尔曼医学院）；University of Southern California（南加州大学）；Duke University Medical Center（杜克大学医学中心）；Mayo Clinic（梅奥诊所）；Center for Global Health, Perelman School of Medicine, University of Pennsylvania（宾夕法尼亚大学佩雷尔曼医学院全球健康中心）；Technical University of Munich（慕尼黑工业大学）；TranslaTUM - Central Institute for Translational Cancer Research, Technical University of Munich（慕尼黑工业大学转化肿瘤学研究中心（TranslaTUM））；National Cancer Institute（美国国家癌症 Institute）
The University of Tokyo（东京大学）；National Institute for Quantum and Radiological Science and Technology（国立量子与放射科学技术研究所）
During the radiotherapy treatment of patients with lung cancer, the radiation delivered to healthy tissue around the tumor needs to be minimized, which is difficult because of respiratory motion and the latency of linear accelerator systems. In the proposed study, we first use the Lucas-Kanade pyramidal optical flow algorithm to perform deformable image registration of chest computed tomography scan images of four patients with lung cancer. We then track three internal points close to the lung tumor based on the previously computed deformation field and predict their position with a recurrent …
PAII Inc.（PAII公司）；Chang Gung Memorial Hospital（长庚纪念医院）；Ping An Technology（平安科技）
Gross tumor volume (GTV) segmentation is a critical step in esophageal cancer radiotherapy treatment planning. Inconsistencies across oncologists and prohibitive labor costs motivate automated approaches for this task. However, leading approaches are only applied to radiotherapy computed tomography (RTCT) images taken prior to treatment. This limits the performance as RTCT suffers from low contrast between the esophagus, tumor, and surrounding tissues. In this paper, we aim to exploit both RTCT and positron emission tomography (PET) imaging modalities to facilitate more accurate GTV segmentati…
University of Southern California（南加州大学）；USC Roski Eye Institute（南加州大学罗斯基眼科研究所）
Papilledema is an ophthalmic neurologic disorder in which increased intracranial pressure leads to swelling of the optic nerves. Undiagnosed papilledema in children may lead to blindness and may be a sign of life-threatening conditions, such as brain tumors. Robust and accurate clinical diagnosis of this syndrome can be facilitated by automated analysis of fundus images using deep learning, especially in the presence of challenges posed by pseudopapilledema that has similar fundus appearance but distinct clinical implications. We present a deep learning-based algorithm for the automatic detect…
Adaptable Segmentation Pipeline for Diverse Brain Tumors with Radiomic-Guided Subtyping and Lesion-Wise Model Ensemble
Sheikh Zayed Institute for Pediatric Surgical Innovation（Sheikh Zayed儿童外科创新研究所）；Children’s National Hospital（儿童医院）；University of Washington（华盛顿大学）；Universidad Politécnica de Madrid（马德里理工大学）；CIBER-BBN；ISCIII；School of Medicine and Health Sciences（医学与健康科学学院）
SegRap2025: A Benchmark of Gross Tumor Volume and Lymph Node Clinical Target Volume Segmentation for Radiotherapy Planning of Nasopharyngeal Carcinoma
School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China（电子科技大学机械与电子工程学院）；Department of Radiation Oncology, Sichuan Cancer Center, Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital and Institute, University of Electronic Science and Technology of China（四川省肿瘤医院放射肿瘤科）；Shanghai AI Lab, Shanghai, China（上海人工智能实验室）；The Affiliated Hospital of Qingdao University, Qingdao, China（青岛大学附属医院）；Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China（香港中文大学计算机科学与工程系）；Faculty of Information Technology, Monash University, Melbourne, Australia（墨尔本大学信息科技学院）；Department of Computing and Department of Brain Sciences, Imperial College London, United Kingdom（伦敦帝国理工学院计算机系和脑科学系）；School of Computer Science and Engineering, Northwestern Polytechnical University, Xi'an, China（西北工业大学计算机科学与工程学院）；UMIT Tirol - Private University for Health Sciences and Health Technology, Austria（蒂罗尔大学（私人健康科学与健康技术大学））；School of Information and Communication Engineering, University of Electronic Science and Technology of China（电子科技大学信息与通信工程学院）；National Heart and Lung Institute, Imperial College London, London, United Kingdom（伦敦帝国理工学院国家心脏和肺研究所）；Hawkes Institute, Department of Computer Science, University College London, London, United Kingdom（伦敦大学学院霍克斯研究所）；Shanghai MediWorks Precision Instruments Co., Ltd., China（上海 MediWorks 精密仪器有限公司）；School of Automation and Intelligent Sensing, Shanghai Jiao Tong University, Shanghai, China（上海交通大学自动化与智能感知学院）；Department of Systems Hub, Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China（香港科技大学（广州）系统中心）
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）
University of Electronic Science and Technology of China（电子科技大学）；Shanghai AI Laboratory（上海人工智能实验室）；Sichuan Cancer Hospital and Institute（四川省肿瘤医院研究所）；Sichuan University West China Hospital（四川大学华西医院）；SenseTime Research（商汤科技研究院）
Accurate segmentation of Anatomical brain Barriers to Cancer spread (ABCs) plays an important role for automatic delineation of Clinical Target Volume (CTV) of brain tumors in radiotherapy. Despite that variants of U-Net are state-of-the-art segmentation models, they have limited performance when dealing with ABCs structures with various shapes and sizes, especially thin structures (e.g., the falx cerebri) that span only few slices. To deal with this problem, we propose a High and Multi-Resolution Network (HMRNet) that consists of a multi-scale feature learning branch and a high-resolution bra…
PAII Inc.（PAII公司）；Chang Gung Memorial Hospital（长庚纪念医院）；Ping An Technology（平安科技）
Clinical target volume (CTV) delineation from radiotherapy computed tomography (RTCT) images is used to define the treatment areas containing the gross tumor volume (GTV) and/or sub-clinical malignant disease for radiotherapy (RT). High intra- and inter-user variability makes this a particularly difficult task for esophageal cancer. This motivates automated solutions, which is the aim of our work. Because CTV delineation is highly context-dependent--it must encompass the GTV and regional lymph nodes (LNs) while also avoiding excessive exposure to the organs at risk (OARs)--we formulate it as a…
University of Texas Southwestern Medical Center（德克萨斯大学西南医学中心）
In post-operative radiotherapy for prostate cancer, the cancerous prostate gland has been surgically removed, so the clinical target volume (CTV) to be irradiated encompasses the microscopic spread of tumor cells, which cannot be visualized in typical clinical images such as computed tomography or magnetic resonance imaging. In current clinical practice, physicians segment CTVs manually based on their relationship with nearby organs and other clinical information, per clinical guidelines. Automating post-operative prostate CTV segmentation with traditional image segmentation methods has been a…
