Although deep convolutional networks have been widely studied for head and neck (HN) organs at risk (OAR) segmentation, their use for routine clinical treatment planning is limited by a lack of robustness to imaging artifacts, low soft tissue contrast on CT, and the presence of abnormal anatomy. In order to address these challenges, we developed a computationally efficient nested block self-attention (NBSA) method that can be combined with any convolutional network. Our method achieves computational efficiency by performing non-local calculations within memory blocks of fixed spatial extent. Contextual dependencies are captured by passing information in a raster scan order between blocks, as well as through a second attention layer that causes bi-directional attention flow. We implemented our approach on three different networks to demonstrate feasibility. Following training using 200 cases, we performed comprehensive evaluations using conventional and clinical metrics on a separate set of 172 test scans sourced from external and internal institution datasets without any exclusion criteria. NBSA required a similar number of computations (15.7 gflops) as the most efficient criss-cross attention (CCA) method and generated significantly more accurate segmentations for brain stem (Dice of 0.89 vs. 0.86) and parotid glands (0.86 vs. 0.84) than CCA. NBSA's segmentations were less variable than multiple 3D methods, including for small organs with low soft-tissue contrast such as the submandibular glands (surface Dice of 0.90).
Memorial Sloan Kettering Cancer Center（纪念斯隆-凯特琳癌症中心）
Purpose: Radiotherapy presents unique challenges and clinical requirements for longitudinal tumor and organ-at-risk (OAR) prediction during treatment. The challenges include tumor inflammation/edema and radiation-induced changes in organ geometry, whereas the clinical requirements demand flexibility in input/output sequence timepoints to update the predictions on rolling basis and the grounding of all predictions in relationship to the pre-treatment imaging information for response and toxicity assessment in adaptive radiotherapy. Methods: To deal with the aforementioned challenges and to comp…
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）
A Self-supervised Multimodal Deep Learning Approach to Differentiate Post-radiotherapy Progression from Pseudoprogression in Glioblastoma
University Hospital 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-欧洲肿瘤研究所综合癌症中心）；Bavarian Cancer Research Center (BZKF)（巴伐利亚癌症研究中心）；Institute of Neuroradiology, University Hospital Erlangen（埃尔兰根大学医院神经放射学研究所）；Department of Neurosurgery, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希·亚历山大大学埃尔兰根大学医院神经外科）；Universität Würzburg（维尔茨堡大学）
LENAS: Learning-based Neural Architecture Search and Ensemble for 3D Radiotherapy Dose Prediction
The Hong Kong University of Science and Technology（香港科技大学）；Shenzhen United Imaging Research Institute of Innovative Medical Equipment（深圳联合创新医疗装备研究院）；Huazhong University of Science and Technology（华中科技大学）；Tencent YouTu Lab（腾讯优图实验室）
University College London（伦敦大学学院）；King’s College London（伦敦国王学院）；National University of Singapore（新加坡国立大学）
Multi-task neural network architectures provide a mechanism that jointly integrates information from distinct sources. It is ideal in the context of MR-only radiotherapy planning as it can jointly regress a synthetic CT (synCT) scan and segment organs-at-risk (OAR) from MRI. We propose a probabilistic multi-task network that estimates: 1) intrinsic uncertainty through a heteroscedastic noise model for spatially-adaptive task loss weighting and 2) parameter uncertainty through approximate Bayesian inference. This allows sampling of multiple segmentations and synCTs that share their network repr…
Federal University of Sao Paulo（圣保罗联邦大学）；Pontifical Catholic University of Rio Grande do Sul（南里奥格兰德天主教大学）
Contouring of organs at risk is an important but time consuming part of radiotherapy treatment planning. Several authors proposed methods for automatic delineation but the clinical experts eye remains the gold standard method. In this paper, we present a totally visual software for automated delineation of the femural head. The software was successfully characterized in pelvic CT Scan of prostate patients (n=11). The automatic delineation was compared with manual and approved delineation through blind test evaluated by a panel of seniors radiation oncologists (n=9). Clinical experts evaluated …
Mamba-driven MRI-to-CT Synthesis for MRI-only Radiotherapy Planning
School of Electrical；Computer Engineering, National Technical University of Athens Archimedes, Athena Research Center CentraleSup\'elec, University of Paris-Saclay
Amsterdam University Medical Center, University of Amsterdam（阿姆斯特丹大学阿姆斯特丹医学中心）；University Medical Center Utrecht（乌得勒支大学医学中心）；Image Sciences Institute, University Medical Center Utrecht（乌得勒支大学医学中心影像科学研究所）
Convolutional neural networks (CNNs) have been widely and successfully used for medical image segmentation. However, CNNs are typically considered to require large numbers of dedicated expert-segmented training volumes, which may be limiting in practice. This work investigates whether clinically obtained segmentations which are readily available in picture archiving and communication systems (PACS) could provide a possible source of data to train a CNN for segmentation of organs-at-risk (OARs) in radiotherapy treatment planning. In such data, delineations of structures deemed irrelevant to the…
MAR-DTN: Metal Artifact Reduction using Domain Transformation Network for Radiotherapy Planning
FlowReserve Labs S.L.（FlowReserve实验室有限公司）；CITMAga；University of Santiago de Compostela（圣地亚哥德孔波斯特拉大学）；Università degli Studi di Udine（乌迪内大学）；Centro di Riferimento Oncologico di Aviano IRCCS（阿维亚诺肿瘤参考中心IRCCS）
Image-Conditioned Diffusion Models for Quality Assurance of Organ-at-Risk Segmentations in Radiotherapy
UCL Hawkes Institute（UCL霍克斯研究所）；University College London（伦敦大学学院）；National Physical Laboratory（英国国家物理实验室）；University College London Hospital（伦敦大学学院医院）；National Radiotherapy Trials Quality Assurance Group（国家放射治疗试验质量保证组）；Mount Vernon Hospital（芒特弗农医院）；University of Manchester（曼彻斯特大学）
SynthRAD2023 Grand Challenge dataset: generating synthetic CT for radiotherapy
University Medical Center Groningen, University of Groningen（格罗宁根大学医学中心，格罗宁根大学）；Radboud University Medical Center（拉德堡德大学医学中心）；University Medical Center Utrecht（乌得勒支大学医学中心）
INHA University（仁荷大学）；Kyung Hee University（庆熙大学）；Team Elysium Inc.（Team Elysium公司）；Pusan National University Hospital（釜山大学医院）
MR imaging will play a very important role in radiotherapy treatment planning for segmentation of tumor volumes and organs. However, the use of MR-based radiotherapy is limited because of the high cost and the increased use of metal implants such as cardiac pacemakers and artificial joints in aging society. To improve the accuracy of CT-based radiotherapy planning, we propose a synthetic approach that translates a CT image into an MR image using paired and unpaired training data. In contrast to the current synthetic methods for medical images, which depend on sparse pairwise-aligned data or pl…
Deep-learning Segmentation of Small Volumes in CT images for Radiotherapy Treatment Planning
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；OSF HealthCare（OSF医疗保健）；IFC-National Research Council（IFC国家研究委员会）；Terarecon Inc.（Terarecon公司）
Analysis of the 2024 BraTS Meningioma Radiotherapy Planning Automated Segmentation Challenge
Department of Radiation Oncology, Duke University Medical Center（杜克大学医学中心放射肿瘤科）；Computer Vision and Robotics Institute, University of Girona（加泰罗尼亚理工大学计算机视觉与机器人研究所）；Division of Medical Radiation Physics, Department of Physics, Stockholm University（斯德哥尔摩大学医学辐射物理系）；Department of Oncology‑Pathology, Karolinska Institutet（卡罗林斯卡研究所肿瘤病理学系）；Center Algoritmi / LASI, University of Minho（明霍大学中心Algoritmi/LASI）；RWTH Aachen University（亚琛工业大学）；Institute for AI in Medicine, University Hospital Essen（埃森大学医院人工智能医学研究所）；Sheikh Zayed Institute for Pediatric Surgical Innovation, Children’s National Hospital（谢赫扎伊德儿童手术创新研究所，儿童医院）；Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital（阿提尼拉·马丁诺斯生物医学成像中心，麻省总医院）；University of Liverpool（利物浦大学）；Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho（生命与健康科学研究院（ICVS），明霍大学医学院）；ICVS/3B’s - PT Government Associate Laboratory, Braga & Guimarães, Portugal（ICVS/3B’s - 董事会关联实验室，布拉加及瓜拉姆斯，葡萄牙）；Universidad Politécnica de Madrid（马德里理工大学）；CIBER‑BBN, Instituto de Salud Carlos III（CIBER-BBN，卡洛斯三世健康研究所）；Massachusetts General Hospital Cancer Center, Harvard Medical School（哈佛医学院马萨诸塞总医院癌症中心）；Department of Radiation Oncology, SUNY Upstate Medical University（纽约州立大学上州医学院放射肿瘤科）；Center for Intelligent Imaging (ci2), Department of Radiology & Biomedical Imaging, University of California San Francisco（智能成像中心（ci2），大学医学中心放射学与生物医学成像系）
Transformer-based cardiac substructure segmentation from contrast and non-contrast computed tomography for radiotherapy planning
Memorial Sloan Kettering Cancer Center（纪念斯隆·凯特琳癌症中心）；Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg（弗莱堡大学医学院大学医学中心）；German Cancer Consortium (DKTK)（德国癌症联盟（DKTK））
Leiden University Medical Center（莱顿大学医学中心）；University Medical Center Utrecht（乌得勒支大学医学中心）
Joint image registration and segmentation has long been an active area of research in medical imaging. Here, we reformulate this problem in a deep learning setting using adversarial learning. We consider the case in which fixed and moving images as well as their segmentations are available for training, while segmentations are not available during testing; a common scenario in radiotherapy. The proposed framework consists of a 3D end-to-end generator network that estimates the deformation vector field (DVF) between fixed and moving images in an unsupervised fashion and applies this DVF to the …
University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；Normandie Univ（诺曼底大学）；UNIROUEN（鲁昂大学）；UNIHAVRE（勒阿弗尔大学）；INSA Rouen（鲁昂应用科学学院）
Computed tomography (CT) is critical for various clinical applications, e.g., radiotherapy treatment planning and also PET attenuation correction. However, CT exposes radiation during acquisition, which may cause side effects to patients. Compared to CT, magnetic resonance imaging (MRI) is much safer and does not involve any radiations. Therefore, recently, researchers are greatly motivated to estimate CT image from its corresponding MR image of the same subject for the case of radiotherapy planning. In this paper, we propose a data-driven approach to address this challenging problem. Specific…
End-to-End Breast Cancer Radiotherapy Planning via LMMs with Consistency Embedding
Samsung Research（三星研究院）；Center for Advanced Medical Computing and Analysis (CAMCA)（先进医学计算与分析中心）；Massachusetts General Hospital (MGH) and Harvard Medical School（麻省总医院和哈佛医学院）；Yonsei University College of Medicine（延世大学医学院）；Yonsei University（延世大学）；Institute for Innovation in Digital Healthcare（数字医疗创新研究所）；Yongin Severance Hospital（Yongin Severance医院）；Gachon University Gil Hospital（高仁大学Gil医院）；Oncosoft Inc（Oncosoft公司）；Graduate School of AI, Korea Advanced Institute of Science and Technology (KAIST)（人工智能研究生院，韩国科学技术院）
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…
University of Utah（犹他大学）；University of Maryland, School of Medicine（马里兰大学医学院）
Radiation therapy presents a need for dynamic tracking of a target tumor volume. Fiducial markers such as implanted gold seeds have been used to gate radiation delivery but the markers are invasive and gating significantly increases treatment time. Pretreatment acquisition of a respiratory correlated 4DCT allows for determination of accurate motion tracking which is useful in treatment planning. We design a patient-specific motion subspace and a deep convolutional neural network to recover anatomical positions from a single fluoroscopic projection in real-time. We use this deep network to appr…
Université Côte d’Azur（蔚蓝海岸大学）；Inria（法国国家信息与自动化研究所）；Centre Antoine Lacassagne（安托万·拉卡桑中心）
Planning of radiotherapy involves accurate segmentation of a large number of organs at risk, i.e. organs for which irradiation doses should be minimized to avoid important side effects of the therapy. We propose a deep learning method for segmentation of organs at risk inside the brain region, from Magnetic Resonance (MR) images. Our system performs segmentation of eight structures: eye, lens, optic nerve, optic chiasm, pituitary gland, hippocampus, brainstem and brain. We propose an efficient algorithm to train neural networks for an end-to-end segmentation of multiple and non-exclusive class…
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）
Polytechnique Montréal（蒙特利尔理工学院）；Centre Hospitalier de l’Université de Montréal（蒙特利尔大学医疗中心）
Adaptive radiotherapy is a growing field of study in cancer treatment due to it's objective in sparing healthy tissue. The standard of care in several institutions includes longitudinal cone-beam computed tomography (CBCT) acquisitions to monitor changes, but have yet to be used to improve tumor control while managing side-effects. The aim of this study is to demonstrate the clinical value of pre-treatment CBCT acquired daily during radiation therapy treatment for head and neck cancers for the downstream task of predicting severe toxicity occurrence: reactive feeding tube (NG), hospitalization…
Budget-Aware Uncertainty for Radiotherapy Segmentation QA Using nnU-Net
Department of Biomedical Sciences, Humanitas University（人类医学科学系，人类itas大学）；Radiotherapy and Radiosurgery Department, IRCCS Humanitas Research Hospital（放疗与放射外科部门，IRCCS人类itas研究医院）
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））
University of Texas Southwestern Medical Center（德克萨斯大学西南医学中心）
The treatment planning process for patients with head and neck (H&N) cancer is regarded as one of the most complicated due to large target volume, multiple prescription dose levels, and many radiation-sensitive critical structures near the target. Treatment planning for this site requires a high level of human expertise and a tremendous amount of effort to produce personalized high quality plans, taking as long as a week, which deteriorates the chances of tumor control and patient survival. To solve this problem, we propose to investigate a deep learning-based dose prediction model, Hierarchic…
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））
TransAnaNet: Transformer-based Anatomy Change Prediction Network for Head and Neck Cancer Patient Radiotherapy
UT Southwestern Medical Center（德克萨斯大学西南医学中心）；University of Maryland Medical Center（马里兰大学医学中心）；Central Arkansas Radiation Therapy Institute（中央阿肯色放射治疗研究所）
Georgia Institute of Technology（佐治亚理工学院）；Memorial Sloan-Kettering Cancer Center（纪念斯隆-凯特琳癌症中心）；Peking University Cancer Hospital（北京大学肿瘤医院）
In current clinical practice, noisy and artifact-ridden weekly cone-beam computed tomography (CBCT) images are only used for patient setup during radiotherapy. Treatment planning is done once at the beginning of the treatment using high-quality planning CT (pCT) images and manual contours for organs-at-risk (OARs) structures. If the quality of the weekly CBCT images can be improved while simultaneously segmenting OAR structures, this can provide critical information for adapting radiotherapy mid-treatment as well as for deriving biomarkers for treatment response. Using a novel physics-based da…
University of Texas Southwestern Medical Center（德克萨斯大学西南医学中心）
Cone-beam CT (CBCT)-based online adaptive radiotherapy calls for accurate auto-segmentation to reduce the time cost for physicians to edit contours. However, deep learning (DL)-based direct segmentation of CBCT images is a challenging task, mainly due to the poor image quality and lack of well-labelled large training datasets. Deformable image registration (DIR) is often used to propagate the manual contours on the planning CT (pCT) of the same patient to CBCT. In this work, we undertake solving the problems mentioned above with the assistance of DIR. Our method consists of three main componen…
Leiden University Medical Center（莱顿大学医学中心）；Leiden Institute of Advanced Computer Science（莱顿高级计算机科学研究所）
Medical image registration and segmentation are two of the most frequent tasks in medical image analysis. As these tasks are complementary and correlated, it would be beneficial to apply them simultaneously in a joint manner. In this paper, we formulate registration and segmentation as a joint problem via a Multi-Task Learning (MTL) setting, allowing these tasks to leverage their strengths and mitigate their weaknesses through the sharing of beneficial information. We propose to merge these tasks not only on the loss level, but on the architectural level as well. We studied this approach in th…
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 College London Hospitals NHS Foundation Trust（伦敦大学学院医院国民保健服务基金会信托）；Google Health（谷歌健康）；DeepMind（深度思维）；Clatterbridge Cancer Centre NHS Foundation Trust（克拉特布里奇癌症中心国民保健服务基金会信托）；University College London（伦敦大学学院）
Over half a million individuals are diagnosed with head and neck cancer each year worldwide. Radiotherapy is an important curative treatment for this disease, but it requires manual time consuming delineation of radio-sensitive organs at risk (OARs). This planning process can delay treatment, while also introducing inter-operator variability with resulting downstream radiation dose differences. While auto-segmentation algorithms offer a potentially time-saving solution, the challenges in defining, quantifying and achieving expert performance remain. Adopting a deep learning approach, we demons…
