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-validation strategy to select the best model parameters. The proposed hybrid model segmented ten organs-at-risk (OARs) altogether for each case. The performance of the model was evaluated by three metrics, i.e., the Dice Similarity Coefficient (DSC), Hausdorff distance 95% (HD95), and mean surface distance (MSD). We also tested the performance of the model on the Head and Neck 2015 challenge dataset and compared it against several state-of-the-art automated segmentation algorithms. The proposed method generated contours that closely resemble the ground truth for ten OARs. Our results of the new Weaving Attention U-net demonstrate superior or similar performance on the segmentation of head and neck CT images.
FocusNetv2: Imbalanced Large and Small Organ Segmentation with Adversarial Shape Constraint for Head and Neck CT Images
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）
FocusNet: Imbalanced Large and Small Organ Segmentation with an End-to-End Deep Neural Network for Head and Neck CT Images
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）
在本文中，我们提出了一种端到端的深度神经网络，用于解决头颈部（HaN）CT图像中大小器官分割不平衡的问题。为了进行鼻咽癌的放射治疗计划，需要提前精确分割10多个危险器官（正常器官）。然而，头部的大小器官之间的大小比例可以达到数百。直接使用这种不平衡的器官注释来训练深度神经网络通常会导致不准确的小器官标记图。我们提出了一种新型的端到端深度神经网络来解决这一具有挑战性的问题，它通过自动定位、ROI池化和使用专门设计的小器官子网络分割小器官，同时保持大器官分割的精度。一个强大的主网络与密集连接的atrous空间金字塔池和挤压和激励模块用于分割大器官，其中大器官的标签地图直接输出。对于小器官，它们的概率位置而不是标签图由主网络估计。每个小器官的高分辨率和多尺度特征体积根据它们的位置被ROI合并，并被馈送到小器官网络中以精确分割小器官。我们提出的网络进行了广泛的测试，收集的真实数据和\emph{MICCAI Head and Neck Auto Segmentation Challenge 2015}数据集，并表现出优越的性能相比，国家的最先进的分割方法。
A Novel Hybrid Convolutional Neural Network for Accurate Organ Segmentation in 3D Head and Neck CT Images
Shanghai AI Lab（上海人工智能实验室）；Shenzhen Yino Intelligence Techonology Co., Ltd.（深圳一诺智能科技有限公司）；Shenying Medical Technology (Shenzhen) Co., Ltd.（深盈医疗科技（深圳）有限公司）；School of Biomedical Engineering, Shanghai Jiao Tong University（上海交通大学生物医学工程学院）；Institute of Medical Robotics, Shanghai Jiao Tong University（上海交通大学医疗机器人研究所）；Shenzhen Key Lab of Computer Vision and Pattern Recognition, SIAT-SenseTime Joint Lab, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院深圳计算机视觉与模式识别重点实验室、SIAT-商汤联合实验室）；Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院保罗·C·劳特伯生物医学成像研究中心）；Peng Cheng Laboratory（鹏城实验室）；Pazhou Lab（琶洲实验室）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；SIAT-SenseTime Joint Lab（SIAT-商汤联合实验室）；Shenzhen Key Lab of Computer Vision and Pattern Recognition（深圳计算机视觉与模式识别重点实验室）
放射疗法（RT）在临床上广泛用于治疗头颈部（HaN）癌症。RT计划的一个重要步骤是在HaN CT图像中准确分割各种危及器官（OAR）。然而，考虑到典型的HaN CT图像包含数十至数百个切片，手动分割OAR是耗时、繁琐且容易出错的。迫切需要自动分割算法。最近，卷积神经网络（CNN）在这项任务上得到了广泛的研究。特别地，3D CNN经常被采用来处理3D HaN CT图像。幼稚的3D CNN有两个问题。首先，3D CT图像的深度分辨率通常比平面内分辨率低几倍。直接使用3D CNN而不区分这种差异可能会导致提取失真的图像特征并影响最终的分割性能。第二，存在严重的类别不平衡问题，大器官可能比小器官大几倍。很难同时实现对所有器官的准确分割。为了解决这些问题，我们提出了一种新的混合CNN，融合2D和3D卷积，以打击不同的空间分辨率，并提取有效的边缘和语义特征，从3D HaN CT图像。为了适应大型和小型器官，我们的最终模型名为OrganNet2.5D，仅由两个而不是经典的四个下采样操作组成，并引入混合扩张卷积来维持各自的场。在MICCAI 2015挑战数据集上的实验表明，与最先进的方法相比，OrganNet2.5D实现了令人满意的性能。
Automatic segmentation of Organs at Risk in Head and Neck cancer patients from CT and MRI scans
Medical Physics Unit, Department of Oncology, McGill University（麦吉尔大学医学物理单位）；Montreal Institute for Learning Algorithms, Mila（蒙特利尔学习算法研究所）；Department of Computer Science, University of Calgary（卡尔加里大学计算机科学系）；University of British Columbia（不列颠哥伦比亚大学）；Division of Radiation Oncology, Department of Oncology, McGill University（麦吉尔大学放射肿瘤学部）；McGill University Health Centre（麦吉尔大学健康中心）；Lady Davis Institute for Medical Research, Jewish General Hospital（Lady Davis医学研究所，犹太通用医院）；Department of Diagnostic Radiology, McGill University（麦吉尔大学诊断放射学部）；Department of Radiology, University of Florida（佛罗里达大学放射学部）
Comprehensive and Clinically Accurate Head and Neck Organs at Risk Delineation via Stratified Deep Learning: A Large-scale Multi-Institutional Study
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）
Overview of the HECKTOR Challenge at MICCAI 2021: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT Images
HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland（瑞士西部应用科学与艺术大学瓦莱州分校）；Centre Hospitalier Universitaire Vaudois (CHUV)（沃州大学中心医院）；Centre Hospitalier Universitaire de Poitiers (CHUP)（普瓦捷大学中心医院）；Univ Brest（布雷斯特大学）；INSERM（法国国家健康与医学研究院）；LaTIM（医学信息处理实验室）；Cleveland Clinic Foundation（克利夫兰医学中心基金会）；University of Sherbrooke（舍布鲁克大学）
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）
AttentionAnatomy: A unified framework for whole-body organs at risk segmentation using multiple partially annotated datasets
DeepVoxel Inc（深体素公司）；Shanghai General Hospital（上海市第一人民医院）；Shanghai Jiao Tong University School of Medicine（上海交通大学医学院）；University of California, Irvine（加州大学欧文分校）
MIMIR: Deep Regression for Automated Analysis of UK Biobank Body MRI
Department of Surgical Sciences, Uppsala University（乌普萨拉大学外科学系）；Department of Information Technology, Uppsala University（乌普萨拉大学信息技术系）；Antaros Medical AB
