Automated Lymph Node (LN) detection is an important clinical diagnostic task but very challenging due to the low contrast of surrounding structures in Computed Tomography (CT) and to their varying sizes, poses, shapes and sparsely distributed locations. State-of-the-art studies show the performance range of 52.9% sensitivity at 3.1 false-positives per volume (FP/vol.), or 60.9% at 6.1 FP/vol. for mediastinal LN, by one-shot boosting on 3D HAAR features. In this paper, we first operate a preliminary candidate generation stage, towards 100% sensitivity at the cost of high FP levels (40 per patient), to harvest volumes of interest (VOI). Our 2.5D approach consequently decomposes any 3D VOI by resampling 2D reformatted orthogonal views N times, via scale, random translations, and rotations with respect to the VOI centroid coordinates. These random views are then used to train a deep Convolutional Neural Network (CNN) classifier. In testing, the CNN is employed to assign LN probabilities for all N random views that can be simply averaged (as a set) to compute the final classification probability per VOI. We validate the approach on two datasets: 90 CT volumes with 388 mediastinal LNs and 86 patients with 595 abdominal LNs. We achieve sensitivities of 70%/83% at 3 FP/vol. and 84%/90% at 6 FP/vol. in mediastinum and abdomen respectively, which drastically improves over the previous state-of-the-art work.
Meply: A Large-scale Dataset and Baseline Evaluations for Metastatic Perirectal Lymph Node Detection and Segmentation
School of Computer Science and Technology, University of Science and Technology of China（中国科学技术大学计算机科学与技术学院）；Institute of Artificial Intelligence, Hefei Comprehensive National Science Center（合肥综合性国家科学中心人工智能研究院）；The First Affiliated Hospital of Anhui Medical University（安徽医科大学第一附属医院）；Anhui Medical University（安徽医科大学）
CT Synthesis with Conditional Diffusion Models for Abdominal Lymph Node Segmentation
Shanghai Jiao Tong University, Shanghai, China（上海交通大学）；Department of Surgical Oncology and General Surgery, Key Laboratory of Precision Diagnosis and Treatment of Gastrointestinal Tumors, Ministry of Education, The First Hospital of China Medical University, Shenyang, China（外科肿瘤科和普通外科，精准诊断与治疗胃肠肿瘤国家重点实验室，教育部，中国医科大学第一附属医院，沈阳，中国）；Department of Radiology, The First Hospital of China Medical University, Shenyang, China（放射科，中国医科大学第一附属医院，沈阳，中国）；Shanghai AI Laboratory, Shanghai, China（上海人工智能实验室，上海，中国）
Segmentation of 3D High-frequency Ultrasound Images of Human Lymph Nodes Using Graph Cut with Energy Functional Adapted to Local Intensity Distribution
NYU Tandon School of Engineering（纽约大学坦登工程学院）；Riverside Research（河畔研究公司）；University of Hawaii（夏威夷大学）；Kuakini Medical Center（库阿基尼医疗中心）
LNQ Challenge 2023: Learning Mediastinal Lymph Node Segmentation with a Probabilistic Lymph Node Atlas
University of Lübeck（吕贝克大学）；Institute of Medical Informatics（医学信息学研究所）；German Research Center for Artificial Intelligence（德国人工智能研究中心）；German Aerospace Center（德国航空航天中心）
Identifying Metastases in Sentinel Lymph Nodes with Deep Convolutional Neural Networks
Proscia Inc.（Proscia公司）
淋巴结转移是乳腺癌最重要的预后指标之一。然而，目前人工检查前哨淋巴结的诊断程序非常耗时且主观。病理学家必须手动扫描整个数字全载玻片图像（WSI），以寻找转移区域，这些区域有时只能在高分辨率下检测到，或者完全隐藏在人类视觉皮层之外。2015年10月至2016年4月，国际生物医学成像研讨会（ISBI）举办了Camelyon Grand Challenge 2016，以众包淋巴结转移自动检测的想法和算法。使用可推广的染色归一化技术和Proscia Pathology云计算平台，我们在数百万个组织和肿瘤图像瓦片上训练了一个深度卷积神经网络，以对我们的全载玻片图像测试集进行基于载玻片的评估，灵敏度为0.96，特异性为0.89，AUC评分为0.90。我们的结果表明，我们的平台可以自动扫描任何WSI的转移区域，而无需对相应的染色谱进行机构校准。
Weakly Supervised Lymph Nodes Segmentation Based on Partial Instance Annotations with Pre-trained Dual-branch Network and Pseudo Label Learning
University of Electronic Science and Technology of China（电子科技大学）；Shanghai AI Laboratory（上海人工智能实验室）；Sichuan Cancer Hospital & Institute, Sichuan Cancer Center（四川省肿瘤医院·研究所（四川省癌症防治中心））
Mask the Unknown: Assessing Different Strategies to Handle Weak Annotations in the MICCAI2023 Mediastinal Lymph Node Quantification Challenge
School of Computation, Information and Technology, Technical University Munich（慕尼黑工业大学计算、信息与技术学院）；Department of RadioOncology, Klinikum rechts der Isar, Technical University Munich（慕尼黑工业大学右岸医院放射肿瘤科）；Institute of Machine Learning in Biomedical Imaging, Helmholtz Munich（亥姆霍兹慕尼黑生物医学影像机器学习研究所）；Munich Center of Machine Learning (MCML)（慕尼黑机器学习中心）；School of Biomedical Engineering and Imaging Sciences, King’s College London（伦敦国王学院生物医学工程与影像科学学院）
LN-Gen: Rectal Lymph Nodes Generation via Anatomical Features
University of Science and Technology of China（中国科学技术大学）；Hefei Comprehensive National Science Center（合肥综合性国家科学中心）；The First Affiliated Hospital of Anhui Medical University（安徽医科大学第一附属医院）；Anhui Medical University（安徽医科大学）
Semi-Supervised Learning for Cancer Detection of Lymph Node Metastases
University of Bedfordshire（贝德福特大学）；Perm State University（彼尔姆国立大学）；Schaeffler Group（舍弗勒集团）；Universiti Brunei Darussalam（文莱达鲁萨兰大学）；Abramov Software GmbH & Co. KG（阿布拉莫夫软件两合有限公司）
The Application of Deep Learning for Lymph Node Segmentation: A Systematic Review
Department of Health Technology and Informatics, The Hong Kong Polytechnic University（健康科技与信息学系，香港理工大学）；Centre for Smart Health and School of Nursing, The Hong Kong Polytechnic University（智能健康中心及护理学院，香港理工大学）；Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong（影像与介入放射科系，中国香港大学）
Primary Tumor and Inter-Organ Augmentations for Supervised Lymph Node Colon Adenocarcinoma Metastasis Detection
Department of Science and Technology, Linkoping University（林雪平大学科技学院）；Sectra AB（Sectra公司）；Center for Medical Image Science and Visualization, Linkoping University（林雪平大学医学影像科学与可视化中心）
An integrated geometric quantification and shape analysis framework for axillary lymph node metastasis in breast cancer patients
Central South University（中南大学）；The Second Xiangya Hospital, Central South University（中南大学湘雅二医院）；Clinical Research Center for Breast Disease in Hunan Province（湖南省乳腺疾病临床研究中心）；The Chinese University of Hong Kong（香港中文大学）
Universal Lymph Node Detection in Multiparametric MRI with Selective Augmentation
National Institutes of Health（美国国立卫生研究院）；National Library of Medicine（美国国家医学图书馆）；National Center for Biotechnology Information（国家生物技术信息中心）；Clinical Center（临床中心）；Radiology and Imaging Sciences（放射学与影像科学科）
DECT-based Space-Squeeze Method for Multi-Class Classification of Metastatic Lymph Nodes in Breast Cancer
School of Computer Science and Engineering, Sun Yat-sen University（中山大学计算机科学与工程学院）；Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University（中山大学附属肿瘤医院放射科）；Medical Imaging Center, Shenzhen Hospital of Southern Medical University（南方医科大学深圳医院医学影像中心）；Hithink RoyalFlush Information Network Co., Ltd.（慧思科技有限公司）；Department of Radiology, The third Affilliated Hospital of Guangzhou Medical University（广州医学院第三附属医院放射科）
CAFusion: Controllable Anatomical Synthesis of Perirectal Lymph Nodes via SDF-guided Diffusion
School of Computer Science and Technology, University of Science and Technology of China（中国科学技术大学计算机科学与技术学院）；Institute of Artificial Intelligence, Hefei Comprehensive National Science Center（合肥综合性国家科学中心人工智能研究院）；The First Affiliated Hospital of Anhui Medical University（安徽医科大学第一附属医院）；Anhui Medical University（安徽医科大学）
Pathology-Informed Latent Diffusion Model for Anomaly Detection in Lymph Node Metastasis
School of Electrical Engineering, Korea University, Seoul 02841, Korea（韩国大学电子工程学院）；Department of Pathology, Korea University Anam Hospital and Department of Biomedical Informatics, Korea University College of Medicine, Seoul 02841, Korea（韩国大学医学院病理学系）；Department of Hospital Pathology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea（韩国天主大学医学院圣玛丽医院医院病理学系）
MMFusion: Multi-modality Diffusion Model for Lymph Node Metastasis Diagnosis in Esophageal Cancer
Shandong University（山东大学）；Hangzhou Dianzi University（杭州电子科技大学）；Communication University of Zhejiang（浙江传媒学院）；University of Leicester（莱斯特大学）；Sichuan Cancer Hospital and Institution（四川省肿瘤医院）；Sichuan Cancer Center（四川省癌症中心）；University of Electronic Science and Technology of China（电子科技大学）；Suzhou Research Institute of Shandong University（山东大学苏州研究院）
LNQ 2023 challenge: Benchmark of weakly-supervised techniques for mediastinal lymph node quantification
Brigham and Women’s Hospital（布莱根妇女医院）；Harvard Medical School（哈佛医学院）；Massachusetts General Hospital（麻省总医院）；Yunu, Inc.（尤努公司）；Isomics Inc（伊索米克斯公司）；Dana-Farber Cancer Institute（丹娜法伯癌症研究院）；Technical University Munich（慕尼黑工业大学）；Helmholtz Munich（慕尼黑亥姆霍兹中心）；Munich Center of Machine Learning (MCML)（慕尼黑机器学习中心）；King’s College London（伦敦国王学院）；University of Lübeck（吕贝克大学）；German Research Center for Artificial Intelligence（德国人工智能研究中心）；Niigata University of Health and Welfare（新潟医疗福祉大学）；Muroran Institute of Technology（室兰工业大学）；Shanghai Jiao Tong University（上海交通大学）；University of Electronic Science and Technology of China（电子科技大学）；German Cancer Research Center (DKFZ)（德国癌症研究中心）；University of Heidelberg（海德堡大学）；Shanghai AI laboratory（上海人工智能实验室）
DeepStationing: Thoracic Lymph Node Station Parsing in CT Scans using Anatomical Context Encoding and Key Organ Auto-Search
PAII Inc.（PAII公司）；The First Affiliated Hospital Zhejiang University（浙江大学附属第一医院）；Johns Hopkins University（约翰斯·霍普金斯大学）；Ping An Insurance Company of China（中国平安保险（集团）股份有限公司）；Zhejiang Provincial People’s Hospital（浙江省人民医院）
Dynamic Gradient Sparsification Training for Few-Shot Fine-tuning of CT Lymph Node Segmentation Foundation Model
Sichuan Cancer Hospital and Institute, University of Electronic Science and Technology of China（电子科技大学附属四川省肿瘤医院）；School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China（电子科技大学机械与电气工程学院）；The Chinese University of Hong Kong（香港中文大学）；Shanghai AI Laboratory（上海人工智能实验室）；Stanford University School of Medicine（斯坦福大学医学院）
Can Physician Judgment Enhance Model Trustworthiness? A Case Study on Predicting Pathological Lymph Nodes in Rectal Cancer
National Cancer Center Research Institute（国立癌症中心研究所）；RIKEN Center for Advanced Intelligence Project（理化学研究所先进智能项目中心）；National Cancer Center Hospital（国立癌症中心医院）；The University of Tokyo（东京大学）
A deep local attention network for pre-operative lymph node metastasis prediction in pancreatic cancer via multiphase CT imaging
Ping An Technology（平安科技）；Changhai Hospital（长海医院）；PAII Inc.（PAII公司）；Shengjing Hospital of China Medical University（中国医科大学附属盛京医院）；Tianjin Medical University Cancer Institute and Hospital（天津医科大学肿瘤医院）
ConvPath: A Software Tool for Lung Adenocarcinoma Digital Pathological Image Analysis Aided by Convolutional Neural Network
University of Texas Southwestern Medical Center（德克萨斯大学西南医学中心）；National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences（中国医学科学院国家癌症中心/肿瘤医院）；University of Texas MD Anderson Cancer Center（德克萨斯大学MD安德森癌症中心）
