ISA-Net: Improved spatial attention network for PET-CT tumor segmentation
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）
Zhengyong Huang, Sijuan Zou, Guoshuai Wang, Zixiang Chen, Hao Shen, Haiyan Wang, Na Zhang, Lu Zhang, Fan Yang, Haining Wangg, Dong Liang, Tianye Niu, Xiaohua Zhuc, Zhanli Hua
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 emission tomography-computed tomography (PET-CT), which combines the high sensitivity of PET and the precise anatomical information of CT. We design an improved spatial attention network(ISA-Net) to increase the accuracy of PET or CT in detecting tumors, which uses multi-scale convolution operation to extract feature information and can highlight the tumor region location information and suppress the non-tumor region location information. In addition, our network uses dual-channel inputs in the coding stage and fuses them in the decoding stage, which can take advantage of the differences and complementarities between PET and CT. We validated the proposed ISA-Net method on two clinical datasets, a soft tissue sarcoma(STS) and a head and neck tumor(HECKTOR) dataset, and compared with other attention methods for tumor segmentation. The DSC score of 0.8378 on STS dataset and 0.8076 on HECKTOR dataset show that ISA-Net method achieves better segmentation performance and has better generalization. Conclusions: The method proposed in this paper is based on multi-modal medical image tumor segmentation, which can effectively utilize the difference and complementarity of different modes. The method can also be applied to other multi-modal data or single-modal data by proper adjustment.
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…
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…
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…
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 …
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…
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…
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）
University of Sydney（悉尼大学）；Royal Prince Alfred Hospital（阿尔弗雷德王子医院）
The analysis of multi-modality positron emission tomography and computed tomography (PET-CT) images for computer aided diagnosis applications requires combining the sensitivity of PET to detect abnormal regions with anatomical localization from CT. Current methods for PET-CT image analysis either process the modalities separately or fuse information from each modality based on knowledge about the image analysis task. These methods generally do not consider the spatially varying visual characteristics that encode different information across the different modalities, which have different priori…
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 …
A Generalist Foundation Model for Total-body PET/CT Enables Diagnostic Reporting and System-wide Metabolic Profiling
Shandong First Medical University & Shandong Academy of Medical Sciences（山东第一医科大学（山东省医学科学院））；Shandong Provincial Hospital, Shandong First Medical University（山东第一医科大学附属省立医院）；Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology（华中科技大学同济医学院附属湖北肿瘤医院）；The Second Affiliated Hospital of Shandong First Medical University（山东第一医科大学第二附属医院）；The first Affiliated Hospital of Shandong First Medical University（山东第一医科大学第一附属医院）
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…
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…
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…
Despite its tremendous value for the diagnosis, treatment monitoring and surveillance of children with cancer, whole body staging with positron emission tomography (PET) is time consuming and associated with considerable radiation exposure. 100x (1% of the standard clinical dosage) ultra-low-dose/ultra-fast whole-body PET reconstruction has the potential for cancer imaging with unprecedented speed and improved safety, but it cannot be achieved by the naive use of machine learning techniques. In this study, we utilize the global similarity between baseline and follow-up PET and magnetic resonan…
The University of Sydney（悉尼大学）；Shanghai Jiao Tong University（上海交通大学）
Outcome prediction is crucial for head and neck cancer patients as it can provide prognostic information for early treatment planning. Radiomics methods have been widely used for outcome prediction from medical images. However, these methods are limited by their reliance on intractable manual segmentation of tumor regions. Recently, deep learning methods have been proposed to perform end-to-end outcome prediction so as to remove the reliance on manual segmentation. Unfortunately, without segmentation masks, these methods will take the whole image as input, such that makes them difficult to foc…
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）
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…
KTH Royal Institute of Technology（瑞典皇家理工学院）；Karolinska Institutet（卡罗林斯卡学院）
Tumor segmentation in PET-CT images is challenging due to the dual nature of the acquired information: low metabolic information in CT and low spatial resolution in PET. U-Net architecture is the most common and widely recognized approach when developing a fully automatic image segmentation method in the medical field. We proposed a two-step approach, aiming to refine and improve the segmentation performances of tumoral lesions in PET-CT. The first step generates a prior tumor appearance map from the PET-CT volumes, regarded as prior tumor information. The second step, consisting of a standard…
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 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…
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 …
3D Lymphoma Segmentation on PET/CT Images via Multi-Scale Information Fusion with Cross-Attention
Zhengzhou University of Light Industry（郑州轻工业大学）；Peking University Institute of Hematology（北京大学血液病研究所）；Kennesaw State University（肯尼索州立大学）；Michigan Technological University（密歇根理工大学）
The objective of this study was to develop a PET tumor-segmentation framework that addresses the challenges of limited spatial resolution, high image noise, and lack of clinical training data with ground-truth tumor boundaries in PET imaging. We propose a three-module PET-segmentation framework in the context of segmenting primary tumors in 3D FDG-PET images of patients with lung cancer on a per-slice basis. The first module generates PET images containing highly realistic tumors with known ground-truth using a new stochastic and physics-based approach, addressing lack of training data. The se…
Mamba Based Feature Extraction And Adaptive Multilevel Feature Fusion For 3D Tumor Segmentation From Multi-modal Medical Image
School of Computer Science and Engineering, Central South University, Changsha, 410083, China（计算机科学与工程学院，中南大学，长沙，410083，中国）；Hunan Engineering Research Center of Machine Vision and Intelligent Medicine, Central South University, Changsha, 410083, China（机器视觉与智能医学工程研究中心，中南大学，长沙，410083，中国）；Department of Radiation Oncology, Washington University in St. Louis, USA（放射肿瘤科，华盛顿大学圣路易斯分校，美国）；Department of Nuclear Medicine, Henri Becquerel Cancer Center, Rouen, France（核医学科，亨利·贝克勒尔癌症中心，鲁昂，法国）；University of Rouen-Normandy, AMIS - QuantIF UR 4108, F-76000, Rouen, France（鲁昂-诺曼底大学，AMIS - QuantIF UR 4108，法国，F-76000，鲁昂，法国）
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…
Deep evidential fusion with uncertainty quantification and contextual discounting for multimodal medical image segmentation
Université de technologie de Compiègne（贡比涅技术大学）；CNRS（法国国家科学研究中心）；Université de Rouen Normandie（鲁昂诺曼底大学）；Centre Henri Becquerel（亨利·贝克勒尔中心）；Institut universitaire de France（法国大学研究院）
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…
Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) automatic 3-D registration is implemented and validated for small animal image volumes so that the high-resolution anatomical MRI information can be fused with the low spatial resolution of functional PET information for the localization of lesion that is currently in high demand in the study of tumor of cancer (oncology) and its corresponding preparation of pharmaceutical drugs. Though many registration algorithms are developed and applied on human brain volumes, these methods may not be as efficient on small animal datas…
Merging-Diverging Hybrid Transformer Networks for Survival Prediction in Head and Neck Cancer
School of Computer Science, The University of Sydney（悉尼大学计算机科学学院）；Institute of Translational Medicine, Shanghai Jiao Tong University（上海交通大学转化医学研究院）；Royal Prince Alfred Hospital（皇家阿尔弗雷德王子医院）；Med-X Research Institute, Shanghai Jiao Tong University（上海交通大学Med-X研究院）
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…
SGRNet: Spatially Guided Radiology Network for Structured Radiological Reporting of Head and Neck Cancer
University of Strasbourg（斯特拉斯堡大学）；IHU Strasbourg（斯特拉斯堡大学医院研究所）；Indian Institute of Technology (IIT) Madras（印度马德拉斯理工学院）；All India Institute of Medical Sciences（全印度医学科学院）
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…
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）
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…
Objective evaluation of new and improved methods for PET imaging requires access to images with ground truth, as can be obtained through simulation studies. However, for these studies to be clinically relevant, it is important that the simulated images are clinically realistic. In this study, we develop a stochastic and physics-based method to generate realistic oncological two-dimensional (2-D) PET images, where the ground-truth tumor properties are known. The developed method extends upon a previously proposed approach. The approach captures the observed variabilities in tumor properties fro…
Recent progress in automated PET/CT lesion segmentation using deep learning methods has demonstrated the feasibility of this task. However, tumor lesion detection and segmentation in whole-body PET/CT is still a chal-lenging task. To promote research on machine learning-based automated tumor lesion segmentation on whole-body FDG-PET/CT data, Automated Lesion Segmentation in Whole-Body FDG-PET/CT (autoPET) challenge is held, and a large, publicly available training dataset is provided. In this report, we present our solution to the autoPET challenge. We employ multi-modal residual U-Net with de…
Efficient Parameter Adaptation for Multi-Modal Medical Image Segmentation and Prognosis
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）；Michigan State University（密歇根州立大学）；M42 Health（M42健康）
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）
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…
Time-driven Survival Analysis from FDG-PET/CT in Non-Small Cell Lung Cancer
Radiology, Department of Surgical Sciences（外科科学系放射学部）；Antaros Medical（Antaros医疗）；Molecular Imaging and Medical Physics, Department of Surgical Sciences（外科科学系分子成像与医学物理部）
REACT-KD: Region-Aware Cross-modal Topological Knowledge Distillation for Interpretable Medical Image Classification
Department of Health Technology and Informatics, Hong Kong Polytechnic University（健康科技与信息技术系，香港理工大学）；Department of Nuclear Medicine and PET, Hong Kong Sanatorium and Hospital（核医学与PET部门，香港疗养院及医院）
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…
