Enhancing Lesion Segmentation in PET/CT Imaging with Deep Learning and Advanced Data Preprocessing Techniques
Shanghai United Imaging Healthcare Advanced Technology Research Institute Co., Ltd.（上海联影医疗科技先进技术研究院有限公司）；Shanghai Jiao Tong University（上海交通大学）；East China Normal University（华东师范大学）
The escalating global cancer burden underscores the critical need for precise diagnostic tools in oncology. This research employs deep learning to enhance lesion segmentation in PET/CT imaging, utilizing a dataset of 900 whole-body FDG-PET/CT and 600 PSMA-PET/CT studies from the AutoPET challenge III. Our methodical approach includes robust preprocessing and data augmentation techniques to ensure model robustness and generalizability. We investigate the influence of non-zero normalization and modifications to the data augmentation pipeline, such as the introduction of RandGaussianSharpen and adjustments to the Gamma transform parameter. This study aims to contribute to the standardization of preprocessing and augmentation strategies in PET/CT imaging, potentially improving the diagnostic accuracy and the personalized management of cancer patients. Our code will be open-sourced and available at https://github.com/jiayiliu-pku/DC2024.
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…
Comprehensive framework for evaluation of deep neural networks in detection and quantification of lymphoma from PET/CT images: clinical insights, pitfalls, and observer agreement analyses
University of British Columbia（英属哥伦比亚大学）；BC Cancer Research Institute（BC癌症研究所）；Microsoft AI for Good Lab（微软AI公益实验室）；Pomeranian Medical University（波美拉尼亚医科大学）；BC Children’s Hospital（BC儿童医院）；St. Mary’s Hospital（圣玛丽医院）；BC Cancer（BC癌症中心）
Tel Aviv University（特拉维夫大学）；Sheba Medical Center（谢巴医疗中心）；University College London Hospital（伦敦大学学院医院）
In this work we present a novel system for generation of virtual PET images using CT scans. We combine a fully convolutional network (FCN) with a conditional generative adversarial network (GAN) to generate simulated PET data from given input CT data. The synthesized PET can be used for false-positive reduction in lesion detection solutions. Clinically, such solutions may enable lesion detection and drug treatment evaluation in a CT-only environment, thus reducing the need for the more expensive and radioactive PET/CT scan. Our dataset includes 60 PET/CT scans from Sheba Medical center. We use…
Automated Lesion Segmentation in Whole-Body PET/CT in a multitracer setting
Shanghai United Imaging Healthcare Advanced Technology Research Institute Co., Ltd.（上海联影医疗科技股份有限公司先进技术研究院）；Shanghai Jiao Tong University（上海交通大学）；East China Normal University（华东师范大学）
In this work, we propose an advanced AI based grading system for OCT images. The proposed system is a very deep fully convolutional attentive classification network trained with end to end advanced transfer learning with online random augmentation. It uses quasi random augmentation that outputs confidence values for diseases prevalence during inference. Its a fully automated retinal OCT analysis AI system capable of pathological lesions understanding without any offline preprocessing/postprocessing step or manual feature extraction. We present a state of the art performance on the publicly ava…
A Unified Vision-Language Model for PSMA PET/CT Report Generation, Visual Question Answering, and Lesion Segmentation
University of Florida（佛罗里达大学）；University of California, San Francisco（加利福尼亚大学旧金山分校）；Stony Brook University（纽约州立大学石溪分校）；The University of Texas MD Anderson Cancer Center（得克萨斯大学MD安德森癌症中心）
RADIANT-PET: Reasoning-Augmented PET/CT Lesion Segmentation with Large Language Models and Reinforcement Learning
Division of Hematology, The Ohio State University Comprehensive Cancer Center（血液科，俄亥俄州立大学综合癌症中心）；Department of Radiology, Mahidol University（医学放射科，玛希多大学）；Department of Radiology, Mettapracharak Hospital（医学放射科，Mettapracharak医院）；Department of Radiation Oncology, The Ohio State University Comprehensive Cancer Center（放射肿瘤科，俄亥俄州立大学综合癌症中心）
This abstract describes the segmentation system used to participate in the challenge ISIC 2017: Skin Lesion Analysis Towards Melanoma Detection. Several preprocessing techniques have been tested for three color representations (RGB, YCbCr and HSV) of 392 images. Results have been used to choose the better preprocessing for each channel. In each case a neural network is trained to predict the Jaccard Index based on object characteristics. The system includes black frames and reference circle detection algorithms but no special treatment is done for hair removal. Segmentation is performed in two…
How to Segment in 3D Using 2D Models: Automated 3D Segmentation of Prostate Cancer Metastatic Lesions on PET Volumes Using Multi-angle Maximum Intensity Projections and Diffusion Models
BC Cancer Research Institute（BC癌症研究所）；University of British Columbia（不列颠哥伦比亚大学）；BC Cancer（BC癌症中心）
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…
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 …
Towards AI Lesion Tracking in PET/CT Imaging: A Siamese-based CNN Pipeline applied on PSMA PET/CT Scans
Technical University of Munich（慕尼黑工业大学）；TUM School of Natural Sciences（慕尼黑工业大学自然科学学院）；Johns Hopkins University School of Medicine（约翰霍普金斯大学医学院）；Munich Institute of Biomedical Engineering（慕尼黑生物医学工程研究所）
PETAR: Localized Findings Generation with Mask-Aware Vision-Language Modeling for PET Automated Reporting
University of Wisconsin–Madison Department of Computer Sciences（威斯康星大学麦迪逊分校计算机科学系）；University of Wisconsin–Madison Department Radiology（威斯康星大学麦迪逊分校放射学系）；Microsoft（微软公司）
AutoPET Challenge III: Testing the Robustness of Generalized Dice Focal Loss trained 3D Residual UNet for FDG and PSMA Lesion Segmentation from Whole-Body PET/CT Images
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）
Many automatic skin lesion diagnosis systems use segmentation as a preprocessing step to diagnose skin conditions because skin lesion shape, border irregularity, and size can influence the likelihood of malignancy. This paper presents, examines and compares two different approaches to skin lesion segmentation. The first approach uses U-Nets and introduces a histogram equalization based preprocessing step. The second approach is a C-Means clustering based approach that is much simpler to implement and faster to execute. The Jaccard Index between the algorithm output and hand segmented images by…
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）
Multimodal Interactive Lung Lesion Segmentation: A Framework for Annotating PET/CT Images based on Physiological and Anatomical Cues
HIDSS4Health - Helmholtz Information and Data Science School for Health（亥姆霍兹信息与健康数据科学学院（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））
VIT University（VIT大学）；SSN College of Engineering（SSN工程学院）
Skin cancer is the most common of all cancers and each year million cases of skin cancer are treated. Treating and curing skin cancer is easy, if it is diagnosed and treated at an early stage. In this work we propose an automatic technique for skin lesion segmentation in dermoscopic images which helps in classifying the skin cancer types. The proposed method comprises of two major phases (1) preprocessing and (2) segmentation using semi-supervised learning algorithm. In the preprocessing phase noise are removed using filtering technique and in the segmentation phase skin lesions are segmented …
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。
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. It is proven that classical data augmentation methods are useful but still limited due to the huge variation in images. Using generative adversarial networks (GAN) is a promising way to address this problem, however, it is challenging to train one model to generate different classes of lesions. In this paper, we propose a deep convolutional conditional generative adversarial network to generate MIP positron emission tomography image (PET) which is a 2D image that represents a 3D volume for fast interpretation, according to diff…
How We Won the ISLES'24 Challenge by Preprocessing
Department of Mechanical Engineering, University of Washington（华盛顿大学机械工程系）；Paul G. Allen School of Computer Science, University of Washington（华盛顿大学保罗·G·阿伦计算机科学学院）；Department of Bioengineering, University of Washington（华盛顿大学生物工程系）
The autoPET3 Challenge: Automated Lesion Segmentation in Whole-Body PET/CT $\unicode{x2013}$ Multitracer Multicenter Generalization
Department of Radiology, LMU University Hospital, LMU Munich（莱比锡大学医院放射科，莱比锡大学）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；University Hospital Tübingen, Department of Radiology（图宾根大学医院放射科）；Department of Radiology, Stanford University（斯坦福大学放射科）；German Cancer Research Center (DKFZ)（德国癌症研究中心（DKFZ））；Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital（海德堡大学医院放射肿瘤学部模式分析与学习组）；Faculty of Mathematics and Computer Science, Heidelberg University（海德堡大学数学与计算机科学学院）；Institute for AI in Medicine (IKIM), University Hospital Essen (AöR)（医学人工智能研究所（IKIM），埃森大学医院（AöR））；Department of Nuclear Medicine, University Hospital Essen (AöR)（核医学部，埃森大学医院（AöR））；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Department of Computer Science and Engineering, The Chinese University of Hong Kong（香港中文大学计算机科学与工程系）；Department of Electronic Engineering, The Chinese University of Hong Kong（香港中文大学电子工程系）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；HIDSS4Health - Helmholtz Information and Data Science School for Health（HIDSS4Health - 海德堡信息与数据科学健康学校）；Department of Nuclear Medicine, LMU University Hospital, LMU Munich（莱比锡大学医院核医学部，莱比锡大学）；Comprehensive Pneumology Center (CPC-M), Member of the German Center for Lung Research (DZL)（综合肺科中心（CPC-M），德国肺癌研究中心（DZL）成员）；relAI – Konrad Zuse School of Excellence in Reliable AI（relAI - 卡诺德·祖斯可靠性人工智能卓越学校）；Cluster of Excellence iFIT (EXC 2180) "Image Guided and Functionally Instructed Tumor Therapies", University of Tübingen（卓越中心iFIT（EXC 2180）"图像引导和功能指导肿瘤治疗"，图宾根大学）
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…
University of Burgundy（勃艮第大学）；University of Cassino（卡西诺大学）；University of Girona（赫罗纳大学）
Skin lesion segmentation is one of the first steps towards automatic Computer-Aided Diagnosis of skin cancer. Vast variety in the appearance of the skin lesion makes this task very challenging. The contribution of this paper is to apply a power foreground extraction technique called GrabCut for automatic skin lesion segmentation with minimal human interaction in HSV color space. Preprocessing was performed for removing the outer black border. Jaccard Index was measured to evaluate the performance of the segmentation method. On average, 0.71 Jaccard Index was achieved on 1000 images from ISIC c…
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…
Stroke Lesion Segmentation in Clinical Workflows: A Modular, Lightweight, and Deployment-Ready Tool
Univ Rennes（里昂大学）；Inria（法国国家信息与自动化技术研究所）；CNRS（法国国家科学研究中心）；Inserm（法国国家健康与医学研究院）；IRISA UMR 6074；Siemens Healthineers（西门子医疗）；CHU Rennes（里昂大学医院）；Centre de Kerpape（凯帕佩中心）
Cross-Source Supervision for Bone Infection Segmentation in Dual-Modality PET-CT
Shanghai Maritime University（上海海洋大学）；University College London（伦敦大学学院）；Shanghai Sixth People’s Hospital（上海第六人民医院）；Shanghai Sixth People’s Hospital Affiliated to SJTU School of Medicine（上海第六人民医院附属复旦大学医学院）；Queen Mary University of London（伦敦女王玛丽大学）
Towards Interactive Lesion Segmentation in Whole-Body PET/CT with Promptable Models
German Cancer Research Center (DKFZ) Heidelberg（德国癌症研究中心（DKFZ）海德堡）；Division of Medical Image Computing（医学影像计算 division）；HIDSS4Health - Helmholtz Information and Data Science School for Health（HIDSS4Health - 欧文信息与数据科学健康学校）；Faculty of Mathematics and Computer Science（数学与计算机科学学院）；Heidelberg University（海德堡大学）；Helmholtz Imaging（海德堡成像）；Pattern Analysis and Learning Group（模式分析与学习组）；Department of Radiation Oncology（放射肿瘤科）
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）
GLOW-FDG: Generalized cancer LesiOn Whole-body segmentation model for $^{18}$F-FDG-PET/CT
Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland（苏黎世大学放射肿瘤科，苏黎世大学，苏黎世，瑞士）；Faculty of Medicine, University of Zurich, Zurich, Switzerland（苏黎世大学医学院，苏黎世，瑞士）；Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany（德国癌症研究中心（DKFZ）医学图像计算部，海德堡，德国）；Department of Radiation Oncology, Institut de Cancérologie de Lorraine, Vandœuvre-lès-Nancy, France（洛林癌症研究所放射肿瘤科，法国 Vandœuvre-lès-Nancy）；Department of Thoracic Surgery, University Hospital Zurich, University of Zurich, Zurich, Switzerland（苏黎世大学胸外科科，苏黎世大学，苏黎世，瑞士）；ETOP IBCSG Partners Foundation, Bern, Switzerland（ETOP IBCSG合作伙伴基金会，瑞士伯恩）；Department of Nuclear Medicine, University Hospital Zurich, University of Zurich, Zurich, Switzerland（苏黎世大学核医学科，苏黎世大学，苏黎世，瑞士）
