Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy
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）
Stanislav Nikolov, Sam Blackwell, Alexei Zverovitch, Ruheena Mendes, Michelle Livne, Jeffrey De Fauw, Yojan Patel, Clemens Meyer, Harry Askham, Bernardino Romer… 展开作者
Stanislav Nikolov, Sam Blackwell, Alexei Zverovitch, Ruheena Mendes, Michelle Livne, Jeffrey De Fauw, Yojan Patel, Clemens Meyer, Harry Askham, Bernardino Romera-Paredes, Christopher Kelly, Alan Karthikesalingam, Carlton Chu, Dawn Carnell, Cheng Boon, Derek D'Souza, Syed Ali Moinuddin, Bethany Garie, Yasmin McQuinlan, Sarah Ireland, Kiarna Hampton, Krystle Fuller, Hugh Montgomery, Geraint Rees, Mustafa Suleyman, Trevor Back, Cían Hughes, Joseph R. Ledsam, Olaf Ronneberger
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 demonstrate a 3D U-Net architecture that achieves expert-level performance in delineating 21 distinct head and neck OARs commonly segmented in clinical practice. The model was trained on a dataset of 663 deidentified computed tomography (CT) scans acquired in routine clinical practice and with both segmentations taken from clinical practice and segmentations created by experienced radiographers as part of this research, all in accordance with consensus OAR definitions. We demonstrate the model's clinical applicability by assessing its performance on a test set of 21 CT scans from clinical practice, each with the 21 OARs segmented by two independent experts. We also introduce surface Dice similarity coefficient (surface DSC), a new metric for the comparison of organ delineation, to quantify deviation between OAR surface contours rather than volumes, better reflecting the clinical task of correcting errors in the automated organ segmentations. The model's generalisability is then demonstrated on two distinct open source datasets, reflecting different centres and countries to model training. With appropriate validation studies and regulatory approvals, this system could improve the efficiency, consistency, and safety of radiotherapy pathways.
University of California, Irvine（加州大学欧文分校）；Lenovo Research（联想研究院）；Deepvoxel Inc（深度体素公司）；Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine（上海交通大学医学院附属上海第一人民医院）；Tencent Medical AI Lab（腾讯医疗人工智能实验室）
Methods: Our deep learning model, called AnatomyNet, segments OARs from head and neck CT images in an end-to-end fashion, receiving whole-volume HaN CT images as input and generating masks of all OARs of interest in one shot. AnatomyNet is built upon the popular 3D U-net architecture, but extends it in three important ways: 1) a new encoding scheme to allow auto-segmentation on whole-volume CT images instead of local patches or subsets of slices, 2) incorporating 3D squeeze-and-excitation residual blocks in encoding layers for better feature representation, and 3) a new loss function combining…
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…
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））
Leiden University Medical Center（莱顿大学医学中心）；TU Delft（代尔夫特理工大学）
Deep learning models for organ contouring in radiotherapy are poised for clinical usage, but currently, there exist few tools for automated quality assessment (QA) of the predicted contours. Using Bayesian models and their associated uncertainty, one can potentially automate the process of detecting inaccurate predictions. We investigate two Bayesian models for auto-contouring, DropOut and FlipOut, using a quantitative measure - expected calibration error (ECE) and a qualitative measure - region-based accuracy-vs-uncertainty (R-AvU) graphs. It is well understood that a model should have low EC…
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）
Anatomy-preserving unpaired cone-beam CT refinement for image-guided radiotherapy using pseudo-label guided diffusion
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Southern University of Science and Technology（南方科技大学）；Huashan Hospital, Fudan University（复旦大学附属华山医院）
Improving Deep Learning-Based Target Volume Auto-Delineation for Adaptive MR-Guided Radiotherapy in Head and Neck Cancer: Impact of a Volume-Aware Dice Loss
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）
Brno University of Technology（布尔诺理工大学）；TESCAN 3DIM（泰思肯三维成像公司）
This paper deals with segmentation of organs at risk (OAR) in head and neck area in CT images which is a crucial step for reliable intensity modulated radiotherapy treatment. We introduce a convolution neural network with encoder-decoder architecture and a new loss function, the batch soft Dice loss function, used to train the network. The resulting model produces segmentations of every OAR in the public MICCAI 2015 Head And Neck Auto-Segmentation Challenge dataset. Despite the heavy class imbalance in the data, we improve accuracy of current state-of-the-art methods by 0.33 mm in terms of ave…
A 3D deep learning model (OARnet) is developed and used to delineate 28 H&N OARs on CT images. OARnet utilizes a densely connected network to detect the OAR bounding-box, then delineates the OAR within the box. It reuses information from any layer to subsequent layers and uses skip connections to combine information from different dense block levels to progressively improve delineation accuracy. Training uses up to 28 expert manual delineated (MD) OARs from 165 CTs. Dice similarity coefficient (DSC) and the 95th percentile Hausdorff distance (HD95) with respect to MD is assessed for 70 other C…
Cancer Hospital of University of the Chinese Academy of Sciences (Zhejiang Cancer Hospital)（中国科学院大学附属肿瘤医院（浙江省肿瘤医院））；Rutgers University（罗格斯大学）；The Chinese University of Hong Kong（香港中文大学）；SenseTime Research（商汤科技研究院）
Radiotherapy is a treatment where radiation is used to eliminate cancer cells. The delineation of organs-at-risk (OARs) is a vital step in radiotherapy treatment planning to avoid damage to healthy organs. For nasopharyngeal cancer, more than 20 OARs are needed to be precisely segmented in advance. The challenge of this task lies in complex anatomical structure, low-contrast organ contours, and the extremely imbalanced size between large and small organs. Common segmentation methods that treat them equally would generally lead to inaccurate small-organ labeling. We propose a novel two-stage de…
In medical imaging, radiological scans of different modalities serve to enhance different sets of features for clinical diagnosis and treatment planning. This variety enriches the source information that could be used for outcome prediction. Deep learning methods are particularly well-suited for feature extraction from high-dimensional inputs such as images. In this work, we apply a CNN classification network augmented with a FCN preprocessor sub-network to a public TCIA head and neck cancer dataset. The training goal is survival prediction of radiotherapy cases based on pre-treatment FDG PET-…
Convolutional neural networks (CNNs) are increasingly being used to automate segmentation of organs-at-risk in radiotherapy. Since large sets of highly curated data are scarce, we investigated how much data is required to train accurate and robust head and neck auto-segmentation models. For this, an established 3D CNN was trained from scratch with different sized datasets (25-1000 scans) to segment the brainstem, parotid glands and spinal cord in CTs. Additionally, we evaluated multiple ensemble techniques to improve the performance of these models. The segmentations improved with training set…
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）
SwinCross: Cross-modal Swin Transformer for Head-and-Neck Tumor Segmentation in PET/CT Images
Philips Research North America（飞利浦北美研究院）；Massachusetts General Hospital/Harvard Medical School（麻省总医院/哈佛医学院）；The Center for Advanced Medical Computing and Analysis, Massachusetts General Hospital/Harvard Medical School（麻省总医院/哈佛医学院高级医学计算与分析中心）
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）
In radiotherapy planning, manual contouring is labor-intensive and time-consuming. Accurate and robust automated segmentation models improve the efficiency and treatment outcome. We aim to develop a novel hybrid deep learning approach, combining convolutional neural networks (CNNs) and the self-attention mechanism, for rapid and accurate multi-organ segmentation on head and neck computed tomography (CT) images. Head and neck CT images with manual contours of 115 patients were retrospectively collected and used. We set the training/validation/testing ratio to 81/9/25 and used the 10-fold cross-…
PAII Inc.（PAII公司）；The First Affiliated Hospital Zhejiang University（浙江大学附属第一医院）；Ping An Technology（平安科技）；Chang Gung Memorial Hospital（长庚纪念医院）；Johns Hopkins University（约翰斯·霍普金斯大学）；Huadong Hospital Affiliated to Fudan University（复旦大学附属华东医院）；First Affiliated Hospital of Xi'an Jiaotong University（西安交通大学第一附属医院）；People's Hospital of Shanxi Province（山西省人民医院）；Nanfang Hospital, Southern Medical University（南方医科大学南方医院）；The First Hospital of Lanzhou University（兰州大学第一医院）；Chang Gung University（长庚大学）
Accurate organ at risk (OAR) segmentation is critical to reduce the radiotherapy post-treatment complications. Consensus guidelines recommend a set of more than 40 OARs in the head and neck (H&N) region, however, due to the predictable prohibitive labor-cost of this task, most institutions choose a substantially simplified protocol by delineating a smaller subset of OARs and neglecting the dose distributions associated with other OARs. In this work we propose a novel, automated and highly effective stratified OAR segmentation (SOARS) system using deep learning to precisely delineate a comprehe…
GAPNet: Granularity Attention Network with Anatomy-Prior-Constraint for Carotid Artery Segmentation
Shandong Artificial Intelligence Institute, Qilu University of Technology (Shandong Academy of Sciences)（齐鲁工业大学（山东省科学院）山东省人工智能研究院）；School of Artificial Intelligence, Hebei University of Technology（河北工业大学人工智能学院）；School of Computer, Electronics and Information, Guangxi University（广西大学计算机与电子信息学院）；School of Intelligence Science and Technology, Nanjing University（南京大学智能科学与技术学院）；University Paris Dauphine, PSL Research University, CNRS, UMR 7534, CEREMADE（巴黎多芬大学（PSL研究大学）、法国国家科学研究中心UMR 7534、CEREMADE）
University of Texas Southwestern Medical Center（德克萨斯大学西南医学中心）；Sun Yat-sen University（中山大学）
The automatic standardization of nomenclature for anatomical structures in radiotherapy (RT) clinical data is a critical prerequisite for data curation and data-driven research in the era of big data and artificial intelligence, but it is currently an unmet need. Existing methods either cannot handle cross-institutional datasets or suffer from heavy imbalance and poor-quality delineation in clinical RT datasets. To solve these problems, we propose an automated structure nomenclature standardization framework, 3D Non-local Network with Voting (3DNNV). This framework consists of an improved data…
University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；NeuralX, Inc.（NeuralX公司）
The transfer of facial expressions from people to 3D face models is a classic computer graphics problem. In this paper, we present a novel, learning-based approach to transferring facial expressions and head movements from images and videos to a biomechanical model of the face-head-neck complex. Leveraging the Facial Action Coding System (FACS) as an intermediate representation of the expression space, we train a deep neural network to take in FACS Action Units (AUs) and output suitable facial muscle and jaw activation signals for the musculoskeletal model. Through biomechanical simulation, th…
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…
Memorial Sloan-Kettering Cancer Center（纪念斯隆-凯特琳癌症中心）
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. C…
SenseTime Research（商汤科技研究院）；The Chinese University of Hong Kong（香港中文大学）；Shenzhen Yino Intelligence Techonology（深圳依诺智能科技）；Inst. of Cancer Research & Basic Medical Sciences of Chinese Academy of Sciences（中国科学院肿瘤与基础医学研究所）；Cancer Hospital of University of Chinese Academy of Sciences（中国科学院大学附属肿瘤医院）；Zhejiang Cancer Hospital（浙江省肿瘤医院）
In this paper, we propose an end-to-end deep neural network for solving the problem of imbalanced large and small organ segmentation in head and neck (HaN) CT images. To conduct radiotherapy planning for nasopharyngeal cancer, more than 10 organs-at-risk (normal organs) need to be precisely segmented in advance. However, the size ratio between large and small organs in the head could reach hundreds. Directly using such imbalanced organ annotations to train deep neural networks generally leads to inaccurate small-organ label maps. We propose a novel end-to-end deep neural network to solve this …
PAII Inc.（PAII公司）；Chang Gung Memorial Hospital（长庚纪念医院）；The Johns Hopkins University（约翰斯·霍普金斯大学）；National Tsing Hua University（国立清华大学）；Ping An Technology（平安科技）
OAR segmentation is a critical step in radiotherapy of head and neck (H&N) cancer, where inconsistencies across radiation oncologists and prohibitive labor costs motivate automated approaches. However, leading methods using standard fully convolutional network workflows that are challenged when the number of OARs becomes large, e.g. > 40. For such scenarios, insights can be gained from the stratification approaches seen in manual clinical OAR delineation. This is the goal of our work, where we introduce stratified organ at risk segmentation (SOARS), an approach that stratifies OARs into anchor…
Prediction of Distant Metastasis in Head and Neck Cancer Patients Using Tumor and Peritumoral Multi-Modal Deep Learning
Xidian University（西安电子科技大学）；Xijing Hospital, Air Force Medical University of PLA（中国人民解放军空军军医大学西京医院）；The General Hospital of Western Theater Command（西部战区总医院）
A total of twenty paired CT and MR images were used in this study to investigate two conditional generative adversarial networks, Pix2Pix, and Cycle GAN, for generating synthetic CT images for Headand Neck cancer cases. Ten of the patient cases were used for training and included such common artifacts as dental implants; the remaining ten testing cases were used for testing and included a larger range of image features commonly found in clinical head and neck cases. These features included strong metal artifacts from dental implants, one case with a metal implant, and one case with abnormal an…
Background: Deep learning (DL)-based head and neck lymph node level (HN_LNL) autodelineation is of high relevance to radiotherapy research and clinical treatment planning but still underinvestigated in academic literature. Methods: An expert-delineated cohort of 35 planning CTs was used for training of an nnU-net 3D-fullres/2D-ensemble model for autosegmentation of 20 different HN_LNL. A second cohort acquired at the same institution later in time served as the test set (n=20). In a completely blinded evaluation, 3 clinical experts rated the quality of DL autosegmentations in a head-to-head co…
Memorial Sloan Kettering Cancer Center（纪念斯隆凯特琳癌症中心）
Vision transformers, with their ability to more efficiently model long-range context, have demonstrated impressive accuracy gains in several computer vision and medical image analysis tasks including segmentation. However, such methods need large labeled datasets for training, which is hard to obtain for medical image analysis. Self-supervised learning (SSL) has demonstrated success in medical image segmentation using convolutional networks. In this work, we developed a \underline{s}elf-distillation learning with \underline{m}asked \underline{i}mage modeling method to perform SSL for vision \u…
A Multimodal and Multi-centric Head and Neck Cancer Dataset for Segmentation, Diagnosis and Outcome Prediction
Department of Computer Vision, Mohamed bin Zayed University of Artificial Intelligence（计算机视觉系，Mohamed bin Zayed人工智能大学）；Department of Machine Learning, Mohamed bin Zayed University of Artificial Intelligence（机器学习系，Mohamed bin Zayed人工智能大学）；Institute of Informatics, HES-SO Valais-Wallis University of Applied Sciences and Arts（信息学院，HES-SO瓦莱-杜萨大学应用科学与艺术学院）；Department of Nuclear Medicine and Molecular Imaging, Lausanne University Hospital (CHUV)（核医学与分子成像系，洛桑大学医院（CHUV））；Nantes Université, CHU Nantes, Nuclear Medicine Department（南特大学，南特大学医院，核医学部门）
Longitudinal NSCLC Treatment Progression via Multimodal Generative Models
Unit of Artificial Intelligence and Computer Systems, Department of Engineering, Università Campus Bio-Medico di Roma, Italy（人工智能与计算机系统单位，工程系，罗马大学生物医学学院）；Multi-Specialist Clinical Institute for Orthopaedic Trauma Care (COT), Messina, Italy（骨科创伤护理多学科临床研究所（COT），意大利Messina）；Department of Diagnostics and Intervention, Radiation Physics, Biomedical Engineering, Umeå University, Sweden（诊断与介入系，放射物理，生物医学工程，乌梅大学，瑞典）；Operative Research Unit of Radiation Oncology, Fondazione Policlinico Universitario Campus Bio-Medico, Rome, Italy（放射肿瘤手术研究单位，大学生物医学学院基金会，罗马，意大利）；Research Unit of Radiation Oncology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Italy（放射肿瘤研究单位，医学与外科系，罗马大学生物医学学院，意大利）；Department of Biomedical Engineering, University of Basel, Allschwil, Switzerland（生物医学工程系，巴塞尔大学，瑞士Allschwil）；Department of Naval, Electrical, Electronics and Telecommunications Engineering, University of Genoa, Italy（海军、电气、电子与电信工程系，热那亚大学，意大利）
SegReg: Segmenting OARs by Registering MR Images and CT Annotations
Australian Institute for Machine Learning, The University of Adelaide（阿德莱德大学澳大利亚机器学习研究所）；The Australian National University（澳大利亚国立大学）；School of Computer and Mathematical Sciences, The University of Adelaide（阿德莱德大学计算机与数学科学学院）；Australian Bragg Centre for Proton Therapy and Research, South Australian Health and Medical Research Institute（南澳大利亚健康与医学研究所澳大利亚布拉格质子治疗与研究中心）；JBI, The University of Adelaide（阿德莱德大学JBI（乔安娜·布里格斯研究所））；Modbury Hospital（莫德伯里医院）；Flinders Health and Medical Research Institute, Flinders University（弗林德斯大学弗林德斯健康与医学研究所）
HEad and neCK TumOR (HECKTOR) 2025: Benchmark of Segmentation, Diagnosis, and Prognosis in Multimodal PET/CT
Mohamed bin Zayed University of Artificial Intelligence (MBZUAI)（穆罕默德·本·扎耶德人工智能大学）；Amsterdam UMC（阿姆斯特丹大学医学中心）；The Netherlands Cancer Institute（荷兰癌症研究所）；Radboud University Medical Centre（拉德堡德大学医学中心）；University College London（伦敦大学学院）；Imperial College London（帝国理工学院）；Shenzhen Technology University（深圳技术大学）；Shenzhen University（深圳大学）；Newland Digital Technology（新大陆数字技术）；The University of Sydney（悉尼大学）；Shanghai Jiao Tong University（上海交通大学）；University Hospital, Nantes（南特大学医院）；Nantes Université, Centrale Nantes, CNRS, LS2N（南特大学、南特中央理工学院、法国国家科学研究中心、LS2N实验室）；Hangzhou Dianzi University（杭州电子科技大学）；Tsinghua University（清华大学）；Central South University（中南大学）；University of Glasgow（格拉斯哥大学）；China Mobile System Integration Co., Ltd.（中移系统集成有限公司）；Subtle Medical Inc.（Subtle Medical公司）；University Hospital, LMU Munich（慕尼黑大学医院）；Munich Center for Machine Learning（慕尼黑机器学习中心）；BC Cancer Research Institute（不列颠哥伦比亚癌症研究所）；HES-SO Valais-Wallis University of Applied Sciences and Arts（HES-SO瓦莱州应用科学与艺术大学）；Lausanne University Hospital (CHUV)（洛桑大学医院）；LaTIM, INSERM, UMR 1101, Univ Brest（LaTIM实验室、法国国家健康与医学研究院、UMR 1101、布雷斯特大学）
Structured Visual Evidence Decomposition for Evidence-Grounded Multimodal Screening of Obstructive Sleep Apnea-Hypopnea Syndrome
School of Electronic and Electrical Engineering, Shanghai University of Engineering Science（上海工程技术大学电子与电气工程学院）；Tencent Youtu Lab（腾讯云视频实验室）；ENT Institute and Department of Otorhinolaryngology, Eye & ENT Hospital of Fudan University（复旦大学耳鼻喉科医院耳鼻喉科研究所）；National University of Singapore（新加坡国立大学）
Deep Learning-Based Cross-Anatomy CT Synthesis Using Adapted nnResU-Net with Anatomical Feature Prioritized Loss
Univ. Bordeaux, CNRS, LaBRI, UMR 5800, F-33400 Talence, France（波尔多大学，法国国家科学研究中心，LaBRI研究院，UMR 5800）；Universidad Autónoma de Madrid, Madrid, 28049, Spain（马德里自治大学，西班牙）；Pazmany Peter Catholic University, Budapest, 1083, Hungary（佩斯大学，匈牙利）；RIKEN Center for Integrative Medical Sciences, Medical Data Deep Learning Team, Tokyo, Japan（日本再生医学科学中心，医学数据深度学习团队）
