本报告提出了一个用于CT/PET扫描中自动肿瘤分割的归一化模块，为autoPET III Challenge开发。核心创新是引入了SineNormal，它对PET数据应用周期性正弦变换以增强病灶检测。通过突出强度变化并在PET高亮区域产生同心圆环模式，该模型旨在提高分割准确性，特别是对于具有挑战性的多示踪剂PET数据集。该项目的代码可在GitHub上获取。
英文摘要：
This report presents a normalization block for automated tumor segmentation in CT/PET scans, developed for the autoPET III Challenge. The key innovation is the introduction of the SineNormal, which applies periodic sine transformations to PET data to enhance lesion detection. By highlighting intensity variations and producing concentric ring patterns in PET highlighted regions, the model aims to improve segmentation accuracy, particularly for challenging multitracer PET datasets. The code for this project is available on GitHub (https://github.com/BBQtime/Sine-Wave-Normalization-for-Deep-Learning-Based-Tumor-Segmentation-in-CT-PET).
Automatic segmentation of tumor lesions is a critical initial processing step for quantitative PET/CT analysis. However, numerous tumor lesion with different shapes, sizes, and uptake intensity may be distributed in different anatomical contexts throughout the body, and there is also significant uptake in healthy organs. Therefore, building a systemic PET/CT tumor lesion segmentation model is a challenging task. In this paper, we propose a novel training strategy to build deep learning models capable of systemic tumor segmentation. Our method is validated on the training set of the AutoPET 202…
Multi-modal Evidential Fusion Network for Trustworthy PET/CT Tumor Segmentation
Soochow University（苏州大学）；the University of Hong Kong（香港大学）；Southern University of Science and Technology（南方科技大学）；Changshu Institute of Technology（常熟理工学院）；the First Affiliated Hospital of Soochow University（苏州大学附属第一医院）
Southern Medical University（南方医科大学）；LaTIM（拉蒂姆实验室）；INSERM（法国国家健康与医学研究院）；Univ Brest（布雷斯特大学）；Chinese Academy of Medical Sciences and Peking Union Medical College（中国医学科学院北京协和医学院）
Automatic segmentation of head and neck cancer (HNC) tumors and lymph nodes plays a crucial role in the optimization treatment strategy and prognosis analysis. This study aims to employ nnU-Net for automatic segmentation and radiomics for recurrence-free survival (RFS) prediction using pretreatment PET/CT images in multi-center HNC cohort. A multi-center HNC dataset with 883 patients (524 patients for training, 359 for testing) was provided in HECKTOR 2022. A bounding box of the extended oropharyngeal region was retrieved for each patient with fixed size of 224 x 224 x 224 $mm^{3}$. Then 3D nn…
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）
Autopet III challenge: Incorporating anatomical knowledge into nnUNet for lesion segmentation in PET/CT
Institute for AI in Medicine (IKIM)（医学人工智能研究所）；University Hospital Essen (AöR)（埃森大学医院）；Department of Nuclear Medicine（核医学科）；Cancer Research Center Cologne Essen (CCCE)（科隆埃森癌症研究中心）；West German Cancer Center Essen University Hospital Essen (AöR)（埃森大学医院西德癌症中心）；TU Dortmund（多特蒙德工业大学）；German Cancer Consortium (DKTK, Partner site Essen)（德国癌症联盟（埃森合作站点））
本研究针对 PET/CT 病变分割，提出基于示踪剂分类和集成 nnUNet 的方法，融合解剖标签，在 autoPET III 挑战中取得高 Dice 分数。
Memorial Sloan Kettering Cancer Center（纪念斯隆·凯特琳癌症中心）；Huazhong University of Science and Technology（华中科技大学）；Henry Ford Hospital（亨利·福特医院）；University of Maryland School of Medicine（马里兰大学医学院）
Quantification of local metabolic tumor volume (MTV) chan-ges after Chemo-radiotherapy would allow accurate tumor response evaluation. Currently, local MTV changes in esophageal (soft-tissue) cancer are measured by registering follow-up PET to baseline PET using the same transformation obtained by deformable registration of follow-up CT to baseline CT. Such approach is suboptimal because PET and CT capture fundamentally different properties (metabolic vs. anatomy) of a tumor. In this work we combined PET and CT images into a single blended PET-CT image and registered follow-up blended PET-CT i…
Segmentation-Free Outcome Prediction from Head and Neck Cancer PET/CT Images: Deep Learning-Based Feature Extraction from Multi-Angle Maximum Intensity Projections (MA-MIPs)
University of British Columbia（不列颠哥伦比亚大学）；BC Cancer Research Institute（不列颠哥伦比亚癌症研究所）；Geneva University Hospital（日内瓦大学医院）；University Hospital Bern（伯尔尼大学医院）
CT-Guided Spatially-varying Regularization for Voxel-Wise Deformable Whole-Body PET Registration
Institute of Medical Technology, Peking University Health Science Center（北京大学医学部医学技术研究所）；Ren Ji Hospital, Shanghai Jiao Tong University（上海交通大学仁济医院）；University of Oxford（牛津大学）
Washington University in St. Louis（圣路易斯华盛顿大学）；Washington University School of Medicine（华盛顿大学医学院）；Mallinckrodt Institute of Radiology（马林克罗德放射研究所）
Tumor segmentation in oncological PET is challenging, a major reason being the partial-volume effects that arise due to low system resolution and finite voxel size. The latter results in tissue-fraction effects, i.e. voxels contain a mixture of tissue classes. Conventional segmentation methods are typically designed to assign each voxel in the image as belonging to a certain tissue class. Thus, these methods are inherently limited in modeling tissue-fraction effects. To address the challenge of accounting for partial-volume effects, and in particular, tissue-fraction effects, we propose a Baye…
Multimodal positron emission tomography-computed tomography (PET-CT) is used routinely in the assessment of cancer. PET-CT combines the high sensitivity for tumor detection with PET and anatomical information from CT. Tumor segmentation is a critical element of PET-CT but at present, there is not an accurate automated segmentation method. Segmentation tends to be done manually by different imaging experts and it is labor-intensive and prone to errors and inconsistency. Previous automated segmentation methods largely focused on fusing information that is extracted separately from the PET and CT…
Nanjing University of Science and Technology（南京理工大学）；Nanjing University（南京大学）
Automatic segmentation of head and neck tumors plays an important role in radiomics analysis. In this short paper, we propose an automatic segmentation method for head and neck tumors from PET and CT images based on the combination of convolutional neural networks (CNNs) and hybrid active contours. Specifically, we first introduce a multi-channel 3D U-Net to segment the tumor with the concatenated PET and CT images. Then, we estimate the segmentation uncertainty by model ensembles and define a segmentation quality score to select the cases with high uncertainties. Finally, we develop a hybrid …
Multimodal Slice Interaction Network Enhanced by Transfer Learning for Precise Segmentation of Internal Gross Tumor Volume in Lung Cancer PET/CT Imaging
Dept. of Biomedical Engineering Johns Hopkins University（生物医学工程系约翰霍普金斯大学）；Molecular Radiation Sciences Johns Hopkins University（分子放射科学约翰霍普金斯大学）；Dept. of Surgery University of Minnesota（外科系明尼苏达大学）；Dept. of Biomedical Engineering University of Virginia（生物医学工程系弗吉尼亚大学）；Comprehensive Cancer Center Mayo Clinic（综合癌症中心梅奥诊所）
Head and neck tumor segmentation challenge (HECKTOR) 2022 offers a platform for researchers to compare their solutions to segmentation of tumors and lymph nodes from 3D CT and PET images. In this work, we describe our solution to HECKTOR 2022 segmentation task. We re-sample all images to a common resolution, crop around head and neck region, and train SegResNet semantic segmentation network from MONAI. We use 5-fold cross validation to select best model checkpoints. The final submission is an ensemble of 15 models from 3 runs. Our solution (team name NVAUTO) achieves the 1st place on the HECKT…
University of Sydney（悉尼大学）；Shanghai Jiao Tong University（上海交通大学）
Multi-modality Fluorodeoxyglucose (FDG) positron emission tomography / computed tomography (PET/CT) has been routinely used in the assessment of common cancers, such as lung cancer, lymphoma, and melanoma. This is mainly attributed to the fact that PET/CT combines the high sensitivity for tumor detection of PET and anatomical information from CT. In PET/CT image assessment, automatic tumor segmentation is an important step, and in recent years, deep learning based methods have become the state-of-the-art. Unfortunately, existing methods tend to over-segment the tumor regions and include region…
Background: A crucial initial processing step for quantitative PET/CT analysis is the segmentation of tumor lesions enabling accurate feature ex-traction, tumor characterization, oncologic staging, and image-based therapy response assessment. Manual lesion segmentation is however associated with enormous effort and cost and is thus infeasible in clinical routine. Goal: The goal of this study was to report the performance of a deep neural network designed to automatically segment regions suspected of cancer in whole-body 18F-FDG PET/CT images in the context of the AutoPET challenge. Method: A c…
Context-Gated Cross-Modal Perception with Visual Mamba for PET-CT Lung Tumor Segmentation
Unit of Artificial Intelligence and Computer Systems, Università Campus Bio-Medico di Roma, Italy（人工智能与计算机系统单位，罗马大学生物医学校园，意大利）；College of Computer Science and Software Engineering, Shenzhen University, China（计算机科学与软件工程学院，深圳大学，中国）；Dept. of Computer Engineering, Modeling, Electronic and System Engineering, University of Calabria, Italy（计算机工程、建模、电子与系统工程系，卡拉布里亚大学，意大利）；IRCCS San Raffaele Hospital, Italy（圣拉斐拉医院，意大利）；Faculty of Medicine, Vita-Salute San Raffaele University, Italy（医学学院，维塔-桑拉斐拉大学，意大利）；Dept. of Diagnostics and Intervention, Radiation Physics, Biomedical Engineering, Umeå University, Sweden（诊断与干预系，辐射物理，生物医学工程，乌梅大学，瑞典）；Dept. of Naval, Electrical, Electronics and Telecommunications Engineering, University of Genoa, Italy（海军、电子、电子与电信工程系，热那亚大学，意大利）
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology（华中科技大学同济医学院同济医院）；Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions（深港脑科学创新研究院——深圳基础研究机构）；United Imaging Research Institute of Innovative Medical Equipment（联影创新医疗装备研究院）；Shenzhen Bay Laboratory（深圳湾实验室）
Achieving accurate and automated tumor segmentation plays an important role in both clinical practice and radiomics research. Segmentation in medicine is now often performed manually by experts, which is a laborious, expensive and error-prone task. Manual annotation relies heavily on the experience and knowledge of these experts. In addition, there is much intra- and interobserver variation. Therefore, it is of great significance to develop a method that can automatically segment tumor target regions. In this paper, we propose a deep learning segmentation method based on multimodal positron em…
General Electric Healthcare（通用电气医疗集团）；University of Rouen（鲁昂大学）；Henri Becquerel Center（亨利·贝克勒尔中心）
One of the most challenges in medical imaging is the lack of data and annotated data. It is proven that classical segmentation methods such as U-NET are useful but still limited due to the lack of annotated data. Using a weakly supervised learning is a promising way to address this problem, however, it is challenging to train one model to detect and locate efficiently different type of lesions due to the huge variation in images. In this paper, we present a novel approach to locate different type of lesions in positron emission tomography (PET) images using only a class label at the image-leve…
Stermedia Sp. z o. o.（Stermedia有限责任公司）；Lower Silesian Oncology Center（下西里西亚肿瘤中心）
In oncology, Positron Emission Tomography imaging is widely used in diagnostics of cancer metastases, in monitoring of progress in course of the cancer treatment, and in planning radiotherapeutic interventions. Accurate and reproducible delineation of the tumor in the Positron Emission Tomography scans remains a difficult task, despite being crucial for delivering appropriate radiation dose, minimizing adverse side-effects of the therapy, and reliable evaluation of treatment. In this piece of research we attempt to solve the problem of automated delineation of the tumor using 3d implementation…
Erasmus+ Joint Program in Medical Imaging and Applications, University of Burgundy (France), UNICLAM (Italy) and University of Girona (Spain)（伊拉斯谟+医学影像与应用联合项目，勃艮第大学（法国）、UNICLAM（意大利）与赫罗纳大学（西班牙））
Positron Emission Tomography scan images are extensively used in radiotherapy planning, clinical diagnosis, assessment of growth and treatment of a tumor. These all rely on fidelity and speed of detection and delineation algorithm. Despite intensive research, segmentation remained a challenging problem due to the diverse image content, resolution, shape, and noise. This paper presents a fast positron emission tomography tumor segmentation method in which superpixels are extracted first from the input image. Principal component analysis is then applied on the superpixels and also on their avera…
H2ASeg: Hierarchical Adaptive Interaction and Weighting Network for Tumor Segmentation in PET/CT Images
School of Science, Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳）理学院）；School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳）计算机科学与技术学院）
College of Medicine and Biological Information Engineering, Northeastern University（东北大学生物医学与信息工程学院）；Department of Electrical and Computer Engineering, Steven Institute of Technology（史蒂文斯理工学院电气与计算机工程系）；Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education（医学影像智能计算教育部重点实验室）
Whole-body PET/CT scan is an important tool for diagnosing various malignancies (e.g., malignant melanoma, lymphoma, or lung cancer), and accurate segmentation of tumors is a key part for subsequent treatment. In recent years, CNN-based segmentation methods have been extensively investigated. However, these methods often give inaccurate segmentation results, such as over-segmentation and under-segmentation. Therefore, to address such issues, we propose a post-processing method based on a graph convolutional neural network (GCN) to refine inaccurate segmentation parts and improve the overall se…
LaTIM, INSERM, UMR 1101, University Brest（拉蒂姆研究所、法国国家健康与医学研究院、布雷斯特大学）
Development of robust and accurate fully automated methods for medical image segmentation is crucial in clinical practice and radiomics studies. In this work, we contributed an automated approach for Head and Neck (H&N) primary tumor segmentation in combined positron emission tomography / computed tomography (PET/CT) images in the context of the MICCAI 2020 Head and Neck Tumor segmentation challenge (HECKTOR). Our model was designed on the U-Net architecture with residual layers and supplemented with Squeeze-and-Excitation Normalization. The described method achieved competitive results in cro…
Differentiating tumor progression (TP) from treatment-related necrosis (TN) is critical for clinical management decisions in glioblastoma (GBM). Dynamic FDG PET (dPET), an advance from traditional static FDG PET, may prove advantageous in clinical staging. dPET includes novel methods of a model-corrected blood input function that accounts for partial volume averaging to compute parametric maps that reveal kinetic information. In a preliminary study, a convolution neural network (CNN) was trained to predict classification accuracy between TP and TN for $35$ brain tumors from $26$ subjects 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…
Tel Aviv University（特拉维夫大学）；Sheba Medical Center（谢巴医疗中心）
In this work we present a novel system for PET estimation using CT scans. We explore the use of fully convolutional networks (FCN) and conditional generative adversarial networks (GAN) to export PET data from CT data. Our dataset includes 25 pairs of PET and CT scans where 17 were used for training and 8 for testing. The system was tested for detection of malignant tumors in the liver region. Initial results look promising showing high detection performance with a TPR of 92.3% and FPR of 0.25 per case. Future work entails expansion of the current system to the entire body using a much larger d…
Clínica Santa María（圣玛丽亚诊所）；Universidad de los Andes（洛斯安第斯大学）
Lung cancer is the leading cause of cancer death in the world. Accurate determination of the EGFR (epidermal growth factor receptor) mutation status is highly relevant for the proper treatment of this patients. Purpose: The aim of this study was to predict the mutational status of the EGFR in non-small cell lung cancer patients using radiomics features extracted from PET-CT images. Methods: Retrospective study that involve 34 patients with lung cancer confirmed by histology and EGFR status mutation assessment. A total of 2.205 radiomics features were extracted from manual segmentation of the P…
Automatic segmentation of tumor lesions is a critical initial processing step for quantitative PET/CT analysis. However, numerous tumor lesions with different shapes, sizes, and uptake intensity may be distributed in different anatomical contexts throughout the body, and there is also significant uptake in healthy organs. Therefore, building a systemic PET/CT tumor lesion segmentation model is a challenging task. In this paper, we propose a novel step-by-step 3D segmentation method to address this problem. We achieved Dice score of 0.92, false positive volume of 0.89 and false negative volume …
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）
Tumor segmentation in multimodal medical images has seen a growing trend towards deep learning based methods. Typically, studies dealing with this topic fuse multimodal image data to improve the tumor segmentation contour for a single imaging modality. However, they do not take into account that tumor characteristics are emphasized differently by each modality, which affects the tumor delineation. Thus, the tumor segmentation is modality- and task-dependent. This is especially the case for soft tissue sarcomas, where, due to necrotic tumor tissue, the segmentation differs vastly. Closing this …
Numerous oncology indications have extensively quantified metabolically active tumors using positron emission tomography (PET) and computed tomography (CT). F-fluorodeoxyglucose-positron emission tomography (FDG-PET) is frequently utilized in clinical practice and clinical drug research to detect and measure metabolically active malignancies. The assessment of tumor burden using manual or computer-assisted tumor segmentation in FDG-PET images is widespread. Deep learning algorithms have also produced effective solutions in this area. However, there may be a need to improve the performance of a…
INSERM, UMR 1101, LaTIM, University of Brest, IBSAM（法国国家健康与医学研究院，UMR 1101，LaTIM，布雷斯特大学，IBSAM）；Academic department of nuclear Medicine, CHU Milétrie（米莱特里大学医院核医学科）；Memorial Sloan Kettering Cancer Center（纪念斯隆·凯特琳癌症中心）；Mallinckrodt Institute of Radiology and the Siteman Cancer Center, Washington University School of Medicine（华盛顿大学医学院马林克罗特放射学研究所及Siteman癌症中心）
Purpose: The main purpose of this study was to assess the reliability of shape and heterogeneity features in both Positron Emission Tomography (PET) and low-dose Computed Tomography (CT) components of PET/CT. A secondary objective was to investigate the impact of image quantization.Material and methods: A Health Insurance Portability and Accountability Act -compliant secondary analysis of deidentified prospectively acquired PET/CT test-retest datasets of 74 patients from multi-center Merck and ACRIN trials was performed. Metabolically active volumes were automatically delineated on PET with Fu…
Enhanced Lung Cancer Survival Prediction using Semi-Supervised Pseudo-Labeling and Learning from Diverse PET/CT Datasets
BC Cancer Research Institute（不列颠哥伦比亚省癌症研究所）；University of British Columbia（不列颠哥伦比亚大学）；Technological Virtual Collaboration (TECVICO Corp.)（技术虚拟协作（TECVICO公司））；Hamadan University of Medical Sciences（哈马丹医科大学）；Rajaie Cardiovascular Medical and Research Center（拉贾伊心血管医学研究中心）；Iran University of Medical Science（伊朗医科大学）；BC Cancer（不列颠哥伦比亚省癌症中心）
ARC Training Centre for Innovative BioEngineering（ARC创新生物工程培训中心）
Distant metastases (DM) refer to the dissemination of tumors, usually, beyond the organ where the tumor originated. They are the leading cause of death in patients with soft-tissue sarcomas (STSs). Positron emission tomography-computed tomography (PET-CT) is regarded as the imaging modality of choice for the management of STSs. It is difficult to determine from imaging studies which STS patients will develop metastases. 'Radiomics' refers to the extraction and analysis of quantitative features from medical images and it has been employed to help identify such tumors. The state-of-the-art in ra…
University of Sydney（悉尼大学）；Fudan University（复旦大学）；Fudan University Shanghai Cancer Center（复旦大学附属肿瘤医院）
Nasopharyngeal Carcinoma (NPC) is a malignant epithelial cancer arising from the nasopharynx. Survival prediction is a major concern for NPC patients, as it provides early prognostic information to plan treatments. Recently, deep survival models based on deep learning have demonstrated the potential to outperform traditional radiomics-based survival prediction models. Deep survival models usually use image patches covering the whole target regions (e.g., nasopharynx for NPC) or containing only segmented tumor regions as the input. However, the models using the whole target regions will also in…
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…
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）
This paper presents an overview of the second edition of the HEad and neCK TumOR (HECKTOR) challenge, organized as a satellite event of the 24th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2021. The challenge is composed of three tasks related to the automatic analysis of PET/CT images for patients with Head and Neck cancer (H&N), focusing on the oropharynx region. Task 1 is the automatic segmentation of H&N primary Gross Tumor Volume (GTVt) in FDG-PET/CT images. Task 2 is the automatic prediction of Progression Free Survival (PFS) from the s…
Shanghai AI Lab（上海人工智能实验室）；Shanghai Jiao Tong University（上海交通大学）；University of Surrey（萨里大学）；University of Electronic Science and Technology of China（电子科技大学）；Tongji University（同济大学）
Tumor lesion segmentation is one of the most important tasks in medical image analysis. In clinical practice, Fluorodeoxyglucose Positron-Emission Tomography~(FDG-PET) is a widely used technique to identify and quantify metabolically active tumors. However, since FDG-PET scans only provide metabolic information, healthy tissue or benign disease with irregular glucose consumption may be mistaken for cancer. To handle this challenge, PET is commonly combined with Computed Tomography~(CT), with the CT used to obtain the anatomic structure of the patient. The combination of PET-based metabolic and…
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）
The First Affiliated Hospital Zhejiang University（浙江大学附属第一医院）；PAII Inc.（PAII公司）；Chang Gung Memorial Hospital（长庚纪念医院）；Huadong Hospital Affiliated to Fudan University（复旦大学附属华东医院）；Lihuili Hospital, Ningbo Medical Center（宁波市医疗中心李惠利医院）；The First Hospital of Lanzhou University（兰州大学第一医院）；Haining People's Hospital（海宁市人民医院）；Ping An Technology（平安科技）
Background: The current clinical workflow for esophageal gross tumor volume (GTV) contouring relies on manual delineation of high labor-costs and interuser variability. Purpose: To validate the clinical applicability of a deep learning (DL) multi-modality esophageal GTV contouring model, developed at 1 institution whereas tested at multiple ones. Methods and Materials: We collected 606 esophageal cancer patients from four institutions. 252 institution-1 patients had a treatment planning-CT (pCT) and a pair of diagnostic FDG-PETCT; 354 patients from other 3 institutions had only pCT. A two-stre…
University of Texas Southwestern Medical Center（德克萨斯大学西南医学中心）
For Head and Neck Cancers (HNC) patient management, automatic gross tumor volume (GTV) segmentation and accurate pre-treatment cancer recurrence prediction are of great importance to assist physicians in designing personalized management plans, which have the potential to improve the treatment outcome and quality of life for HNC patients. In this paper, we developed an automated primary tumor (GTVp) and lymph nodes (GTVn) segmentation method based on combined pre-treatment positron emission tomography/computed tomography (PET/CT) scans of HNC patients. We extracted radiomics features from the …
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）
University Medical Center Groningen（格罗宁根大学医学中心）；University of Texas MD Anderson Cancer Center（德克萨斯大学MD安德森癌症中心）
Tumor segmentation is a fundamental step for radiotherapy treatment planning. To define an accurate segmentation of the primary tumor (GTVp) of oropharyngeal cancer patients (OPC), simultaneous assessment of different image modalities is needed, and each image volume is explored slice-by-slice from different orientations. Moreover, the manual fixed boundary of segmentation neglects the spatial uncertainty known to occur in tumor delineation. This study proposes a novel automatic deep learning (DL) model to assist radiation oncologists in a slice-by-slice adaptive GTVp segmentation on registere…
BC Cancer Research Institute（不列颠哥伦比亚癌症研究所）；University of British Columbia（不列颠哥伦比亚大学）；Simon Fraser University（西蒙弗雷泽大学）
We utilized a 3D nnU-Net model with residual layers supplemented by squeeze and excitation (SE) normalization for tumor segmentation from PET/CT images provided by the Head and Neck Tumor segmentation chal-lenge (HECKTOR). Our proposed loss function incorporates the Unified Fo-cal and Mumford-Shah losses to take the advantage of distribution, region, and boundary-based loss functions. The results of leave-one-out-center-cross-validation performed on different centers showed a segmentation performance of 0.82 average Dice score (DSC) and 3.16 median Hausdorff Distance (HD), and our results on t…
Generation of Heterogeneous PET Images from Uniform Organ Activity Maps Using a Pretrained Domain-Adapted Diffusion Model
Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, USA（华盛顿大学医学院马林克罗德特放射医学研究所，圣路易斯，美国）；Imaging Science Program, McKelvey School of Engineering, Washington University in St Louis, St. Louis, USA（华盛顿大学圣路易斯分校麦克雷高中工程学院成像科学计划，圣路易斯，美国）；Department of Surgery, Washington University School of Medicine, St. Louis, USA（华盛顿大学医学院外科部，圣路易斯，美国）；Department of Biomedical Engineering, Washington University in St Louis, St. Louis, USA（华盛顿大学圣路易斯分校生物医学工程部，圣路易斯，美国）
AdaMSS: Adaptive Multi-Modality Segmentation-to-Survival Learning for Survival Outcome Prediction from PET/CT Images
The University of Sydney（悉尼大学）；Shanghai Jiao Tong University（上海交通大学）；Fudan University Shanghai Cancer Center（复旦大学附属肿瘤医院）；Fudan University（复旦大学）；Royal Prince Alfred Hospital（皇家阿尔弗雷德亲王医院）
