In this study, we implemented a two-stage deep learning-based approach to segment lesions in PET/CT images for the AutoPET III challenge. The first stage utilized a DynUNet model for coarse segmentation, identifying broad regions of interest. The second stage refined this segmentation using an ensemble of SwinUNETR, SegResNet, and UNet models. Preprocessing involved resampling images to a common resolution and normalization, while data augmentation techniques such as affine transformations and intensity adjustments were applied to enhance model generalization. The dataset was split into 80% training and 20% validation, excluding healthy cases. This method leverages multi-stage segmentation and model ensembling to achieve precise lesion segmentation, aiming to improve robustness and overall performance.
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 …
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…
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。
Institute for Anthropomatics and Robotics (IAR), Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院人机交互与机器人研究所）；German Cancer Consortium (DKTK)（德国癌症联盟）；HIDSS4Health - Helmholtz Information and Data Science School for Health（亥姆霍兹健康信息与数据科学学院（HIDSS4Health））；Institute for AI in Medicine (IKIM), University Hospital Essen（埃森大学医院医学人工智能研究所（IKIM））；University Hospital Essen（埃森大学医院）；West German Cancer Center, Cancer Research Center Cologne Essen (CCCE), University Medicine Essen（埃森大学医学院西德癌症中心、科隆埃森癌症研究中心（CCCE））
Positron Emission Tomography (PET) and Computer Tomography (CT) are routinely used together to detect tumors. PET/CT segmentation models can automate tumor delineation, however, current multimodal models do not fully exploit the complementary information in each modality, as they either concatenate PET and CT data or fuse them at the decision level. To combat this, we propose Mirror U-Net, which replaces traditional fusion methods with multimodal fission by factorizing the multimodal representation into modality-specific branches and an auxiliary multimodal decoder. At these branches, Mirror U…
University Hospital Essen（埃森大学医院）；Cancer Research Center Cologne Essen（科隆埃森癌症研究中心）；University Medicine Essen（埃森大学医学院）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；HIDSS4Health - Helmholtz Information and Data Science School for Health（亥姆霍兹健康信息与数据科学学院）；University of Minho（米尼奥大学）；Computer Algorithms for Medicine Laboratory（医学计算机算法实验室）
Tumor volume and changes in tumor characteristics over time are important biomarkers for cancer therapy. In this context, FDG-PET/CT scans are routinely used for staging and re-staging of cancer, as the radiolabeled fluorodeoxyglucose is taken up in regions of high metabolism. Unfortunately, these regions with high metabolism are not specific to tumors and can also represent physiological uptake by normal functioning organs, inflammation, or infection, making detailed and reliable tumor segmentation in these scans a demanding task. This gap in research is addressed by the AutoPET challenge, wh…
Data Diet: Can Trimming PET/CT Datasets Enhance Lesion Segmentation?
Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；HIDSS4Health - Helmholtz Information and Data Science School for Health（HIDSS4Health——亥姆霍兹健康信息与数据科学学院）；Institute for AI in Medicine（医学人工智能研究所）；University Hospital Essen（埃森大学医院）；Cancer Research Center Cologne Essen (CCCE)（科隆埃森癌症研究中心）；University Medicine Essen（埃森大学医学中心）
INSA Rouen Normandie, Univ Rouen Normandie, Université Le Havre Normandie, Normandie Univ, LITIS UR 4108, F-76000 Rouen, France（法国诺曼底 Rouen 大学）；Nuclear Medicine Department, Henri Becquerel Cancer Center, Rouen, France（亨利·贝克勒尔癌症中心核医学部）；Radiotherapy Department, Henri Becquerel Cancer Center, Rouen, France（亨利·贝克勒尔癌症中心放射治疗部）；Siemens Healthineers（西门子医疗）
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…
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…
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…
Sliding Window FastEdit: A Framework for Lesion Annotation in Whole-body PET Images
HIDSS4Health - Helmholtz Information and Data Science School for Health（亥姆霍兹信息与健康数据科学学院）；Institute for AI in Medicine (IKIM), University Hospital Essen（埃森大学医院医学人工智能研究所）
There has been growing research interest in using deep learning based method to achieve fully automated segmentation of lesion in Positron emission tomography computed tomography(PET CT) scans for the prognosis of various cancers. Recent advances in the medical image segmentation shows the nnUNET is feasible for diverse tasks. However, lesion segmentation in the PET images is not straightforward, because lesion and physiological uptake has similar distribution patterns. The Distinction of them requires extra structural information in the CT images. The present paper introduces a nnUNet based m…
Stress-testing cross-cancer generalizability of 3D nnU-Net for PET-CT tumor segmentation: multi-cohort evaluation with novel oesophageal and lung cancer datasets
QIMR Berghofer Medical Research Institute（昆士兰医学研究 institute）；Australian Institute for Bioengineering and Nanotechnology（澳大利亚生物工程与纳米技术研究所）；The University of Queensland（昆士兰大学）；Frazer Institute（弗拉泽研究所）；Princess Alexandra Hospital（亚历山大公主医院）；Faculty of Health, Medicine and Behavioural Sciences（健康、医学与行为科学学院）；School of Biomedical Sciences（生物医学科学学院）；Uppsala University（乌普萨拉大学）；Rohtak Nuclear Medcare Imaging Therapy and Research Center（罗塔克核医学成像治疗与研究中心）
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…
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…
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）
With the advent of neuromorphic hardware, spiking neural networks can be a good energy-efficient alternative to artificial neural networks. However, the use of spiking neural networks to perform computer vision tasks remains limited, mainly focusing on simple tasks such as digit recognition. It remains hard to deal with more complex tasks (e.g. segmentation, object detection) due to the small number of works on deep spiking neural networks for these tasks. The objective of this paper is to make the first step towards modern computer vision with supervised spiking neural networks. We propose a …
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…
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）
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…
Fudan University Shanghai Cancer Center（复旦大学附属肿瘤医院）；Shanghai Medical College, Fudan University（复旦大学上海医学院）；Fudan University（复旦大学）；Shanghai Engineering Research Center of Molecular Imaging Probes（上海分子影像探针工程技术研究中心）；Key Laboratory of Nuclear Physics and Ion-beam Application (MOE), Fudan University（复旦大学核物理与离子束应用教育部重点实验室）；the University of Sydney（悉尼大学）；Shanghai Proton and Heavy Ion Center（上海市质子重离子医院）
Objective: Deep Learning-based Radiomics (DLR) has achieved great success in medical image analysis and has been considered a replacement for conventional radiomics that relies on handcrafted features. In this study, we aimed to explore the capability of DLR for the prediction of 5-year Progression-Free Survival (PFS) in Nasopharyngeal Carcinoma (NPC) using pretreatment PET/CT. Methods: A total of 257 patients (170/87 in internal/external cohorts) with advanced NPC (TNM stage III or IVa) were enrolled. We developed an end-to-end multi-modality DLR model, in which a 3D convolutional neural netw…
A unified self-supervised and supervised deep learning framework for PET image reconstruction is presented, including deep-learned filtered backprojection (DL-FBP) for sinograms, deep-learned backproject then filter (DL-BPF) for backprojected images, and a more general mapping using a deep network in both the sinogram and image domains (DL-FBP-F). The framework allows varying amounts and types of training data, from the case of having only one single dataset to reconstruct through to the case of having numerous measured datasets, which may or may not be paired with high-quality references. For…
University Medical Center Utrecht（乌得勒支大学医学中心）；DKFZ German Cancer Research Center（德国癌症研究中心）；Heidelberg University（海德堡大学）
Recently, deep learning (DL)-based methods for the generation of synthetic computed tomography (sCT) have received significant research attention as an alternative to classical ones. We present here a systematic review of these methods by grouping them into three categories, according to their clinical applications: I) To replace CT in magnetic resonance (MR)-based treatment planning. II) Facilitate cone-beam computed tomography (CBCT)-based image-guided adaptive radiotherapy. III) Derive attenuation maps for the correction of positron emission tomography (PET). Appropriate database searching …
