Department of Computer Science, Johns Hopkins University（约翰霍普金斯大学计算机科学系）；Department of Pharmacy and Biotechnology, University of Bologna（博洛尼亚大学药学与生物技术系）；Center for Biomolecular Nanotechnologies, Istituto Italiano di Tecnologia（意大利技术研究院生物分子纳米技术中心）；Faculty of Mathematics and Computer Science, Jagiellonian University（杰齐兰大学数学与计算机科学学院）；Department of Cardiology and Internal Medicine, University of Warmia and Mazury（马尔堡大学心内科与内科系）；Diagnostic and Treatment Center Gammed（Gammed诊断与治疗中心）；Department of Bioengineering, University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校生物工程系）；Department of General Surgery, Peking University Third Hospital（北京大学第三医院普外科）；Department of Radiology & Biomedical Imaging, University of California, San Francisco（加州大学旧金山分校放射科与生物医学成像系）；Department of Bioengineering, University of California, Berkeley（加州大学伯克利分校生物工程系）；Department of Radiation Oncology, Johns Hopkins School of Medicine（约翰霍普金斯医学院放射肿瘤学系）；NVIDIA Code, Models & Data（NVIDIA代码、模型与数据）
Wenxuan Li, Xinze Zhou, Qi Chen, Tianyu Lin, Pedro R. A. S. Bassi, Szymon Plotka, Jaroslaw B. Cwikla, Xiaoxi Chen, Chen Ye, Zheren Zhu, Kai Ding, Heng Li, Kang … 展开作者
Wenxuan Li, Xinze Zhou, Qi Chen, Tianyu Lin, Pedro R. A. S. Bassi, Szymon Plotka, Jaroslaw B. Cwikla, Xiaoxi Chen, Chen Ye, Zheren Zhu, Kai Ding, Heng Li, Kang Wang, Yang Yang, Yucheng Tang, Daguang Xu, Alan L. Yuille, Zongwei Zhou
PanTS is a large-scale, multi-institutional dataset curated to advance research in pancreatic CT analysis. It contains 36,390 CT scans from 145 medical centers, with expert-validated, voxel-wise annotations of over 993,000 anatomical structures, covering pancreatic tumors, pancreas head, body, and tail, and 24 surrounding anatomical structures such as vascular/skeletal structures and abdominal/thoracic organs. Each scan includes metadata such as patient age, sex, diagnosis, contrast phase, in-plane spacing, slice thickness, etc. AI models trained on PanTS achieve significantly better performance in pancreatic tumor detection, localization, and segmentation compared to those trained on existing public datasets. Our analysis indicates that these gains are directly attributable to the 16x larger-scale tumor annotations and indirectly supported by the 24 additional surrounding anatomical structures. As the largest and most comprehensive resource of its kind, PanTS offers a new benchmark for developing and evaluating AI models in pancreatic CT analysis.
An Exceptional Dataset For Rare Pancreatic Tumor Segmentation
School of Computer Science, Fudan University（复旦大学计算机科学技术学院）；Shanghai Medical College, Fudan University Shanghai Cancer Center（复旦大学上海医学院附属肿瘤医院）；School of Computer Science, University of Illinois（伊利诺伊大学计算机科学学院）
Pancreatic duct dilation indicates a high risk of various pancreatic diseases. Segmentation of dilated pancreatic ducts on computed tomography (CT) images shows the potential to assist the early diagnosis, surgical planning and prognosis. Because of the ducts' tiny sizes, slender tubular structures and the surrounding distractions, most current researches on pancreatic duct segmentation achieve low accuracy and always have segmentation errors on the terminal parts of the ducts. To address these problems, we propose a terminal guidance mechanism called cascaded terminal guidance network (CTG-Ne…
The Johns Hopkins University（约翰斯·霍普金斯大学）；The Johns Hopkins University School of Medicine（约翰斯·霍普金斯大学医学院）
This work presents comprehensive results to detect in the early stage the pancreatic neuroendocrine tumors (PNETs), a group of endocrine tumors arising in the pancreas, which are the second common type of pancreatic cancer, by checking the abdominal CT scans. To the best of our knowledge, this task has not been studied before as a computational task. To provide radiologists with tumor locations, we adopt a segmentation framework to classify CT volumes by checking if at least a sufficient number of voxels is segmented as tumors. To quantitatively analyze our method, we collect and voxelwisely l…
Assessing Pancreatic Ductal Adenocarcinoma Vascular Invasion: the PDACVI Benchmark
Sycai Technologies SL, Scientific；Technical Department, Barcelona, Spain；Universit\" a tsklinikum Erlangen, Department of Radiology of the Uniklinikum Erlangen (UKER), Erlangen, Germany；University Hospital Erlangen, Imaging Science Institute, Erlangen, Germany；ICUBE Laboratory, CNRS UMR-7357, University of Strasbourg, Strasbourg, France；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China；University of Chinese Academy of Sciences, Beijing, China；German Cancer Research Center (DKFZ), Medical Image Computing (E230), Heidelberg, Germany；Hospital de Matar\' o , Matar\' o , Spain；Hospital de Sant Pau i la Santa Creu, Diagnostic Imaging Department, Barcelona, Spain；Institut de Recerca Sant Pau - CERCA, Advanced Medical Imaging, Artificial Intelligence；Imaging-Guided Therapy Research Group, Barcelona, Spain；TECNALIA, Basque Research
A Weakly Supervised Segmentation Network Embedding Cross-scale Attention Guidance and Noise-sensitive Constraint for Detecting Tertiary Lymphoid Structures of Pancreatic Tumors
Nanjing University of Science and Technology（南京理工大学）；Nanjing University（南京大学）；Nanjing Drum Tower Hospital（南京鼓楼医院）；Affiliated Hospital of Nanjing University of Chinese Medicine（南京中医药大学附属医院）
PAII Inc.（PAII 公司）；Shengjing Hospital of China Medical University（中国医科大学附属盛京医院）；Ping An Technology（平安科技）
Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers and carries a dismal prognosis. Surgery remains the best chance of a potential cure for patients who are eligible for initial resection of PDAC. However, outcomes vary significantly even among the resected patients of the same stage and received similar treatments. Accurate preoperative prognosis of resectable PDACs for personalized treatment is thus highly desired. Nevertheless, there are no automated methods yet to fully exploit the contrast-enhanced computed tomography (CE-CT) imaging for PDAC. Tumor attenuation chang…
Pancreatic Tumor Segmentation as Anomaly Detection in CT Images Using Denoising Diffusion Models
School of Electrical and Computer Engineering, University of Oklahoma（俄克拉荷马大学电气与计算机工程学院）；University of Oklahoma Health Sciences Center（俄克拉荷马大学健康科学中心）；School of Computer Science, University of Oklahoma（俄克拉荷马大学计算机科学学院）
3DGAUnet: 3D generative adversarial networks with a 3D U-Net based generator to achieve the accurate and effective synthesis of clinical tumor image data for pancreatic cancer
University of Nebraska-Lincoln（内布拉斯加大学林肯分校）；University of Nebraska Medical Center（内布拉斯加大学医学中心）；University of Rochester Medical Center（罗切斯特大学医学中心）
PAII Inc.（PAII公司）；Shengjing Hospital of China Medical University（中国医科大学附属盛京医院）；Changhai Hospital（长海医院）；Ping An Technology Co., Ltd.（平安科技（深圳）有限公司）
Accurate and automated tumor segmentation is highly desired since it has the great potential to increase the efficiency and reproducibility of computing more complete tumor measurements and imaging biomarkers, comparing to (often partial) human measurements. This is probably the only viable means to enable the large-scale clinical oncology patient studies that utilize medical imaging. Deep learning approaches have shown robust segmentation performances for certain types of tumors, e.g., brain tumors in MRI imaging, when a training dataset with plenty of pixel-level fully-annotated tumor images…
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）
Lymph node (LN) metastasis status is one of the most critical prognostic and cancer staging factors for patients with resectable pancreatic ductal adenocarcinoma (PDAC), or in general, for any types of solid malignant tumors. Preoperative prediction of LN metastasis from non-invasive CT imaging is highly desired, as it might be straightforwardly used to guide the following neoadjuvant treatment decision and surgical planning. Most studies only capture the tumor characteristics in CT imaging to implicitly infer LN metastasis and very few work exploit direct LN's CT imaging information. To the b…
MSWAL: 3D Multi-class Segmentation of Whole Abdominal Lesions Dataset
Shanghai AI Lab（上海人工智能实验室）；University of Liverpool（利物浦大学）；Columbia university（哥伦比亚大学）；Monash University（墨尔本大学）；Xi'an Jiaotong-Liverpool University（西安交通大学利物浦大学）；MBZUAI（穆斯林人工智能研究所）；People's Hospital of Gong'an County（公安县人民医院）
本文提出首个 3D 全腹部病变多类分割数据集 MSWAL，覆盖七类常见病变，并提出 Inception nnU-Net 框架，在分割性能上显著优于现有方法。
Improved Prognostic Prediction of Pancreatic Cancer Using Multi-Phase CT by Integrating Neural Distance and Texture-Aware Transformer
DAMO Academy, Alibaba Group（阿里巴巴集团达摩院）；Peking University（北京大学）；Hupan Lab（湖畔实验室）；Sun Yat-sen University Cancer Center（中山大学肿瘤防治中心）；South China Hospital, Shenzhen University（深圳大学华南医院）；Tianjin Medical University Cancer Institute and Hospital（天津医科大学肿瘤医院）；Changsha Institute for Computing and Digital Economy（长沙计算与数字经济研究院）；Guangdong Provincial People’s Hospital（广东省人民医院）；Shengjing Hospital（盛京医院）
Radboud University Medical Center（拉德堡德大学医学中心）；Radboud Institute for Health Sciences（拉德堡德健康科学研究所）
Early detection improves prognosis in pancreatic ductal adenocarcinoma (PDAC) but is challenging as lesions are often small and poorly defined on contrast-enhanced computed tomography scans (CE-CT). Deep learning can facilitate PDAC diagnosis, however current models still fail to identify small (<2cm) lesions. In this study, state-of-the-art deep learning models were used to develop an automatic framework for PDAC detection, focusing on small lesions. Additionally, the impact of integrating surrounding anatomy was investigated. CE-CT scans from a cohort of 119 pathology-proven PDAC patients an…
A Dual-Task Synergy-Driven Generalization Framework for Pancreatic Cancer Segmentation in CT Scans
Southwest Jiaotong University（西南交通大学）；Shanghai Jiao Tong University（上海交通大学）；The Second Affiliated Hospital of Guangzhou Medical University（广州医科大学附属第二医院）
PAII Inc.（PAII公司）；Stony Brook University（石溪大学）；Harvard University（哈佛大学）；Ping An Technology（平安科技）；Changhai Hospital（长海医院）
The pancreatic disease taxonomy includes ten types of masses (tumors or cysts)[20,8]. Previous work focuses on developing segmentation or classification methods only for certain mass types. Differential diagnosis of all mass types is clinically highly desirable [20] but has not been investigated using an automated image understanding approach. We exploit the feasibility to distinguish pancreatic ductal adenocarcinoma (PDAC) from the nine other nonPDAC masses using multi-phase CT imaging. Both image appearance and the 3D organ-mass geometry relationship are critical. We propose a holistic segme…
Expectation-Maximization as the Engine of Scalable Medical Intelligence
Johns Hopkins University（约翰霍普金斯大学）；University of Bologna（博洛尼亚大学）；Italian Institute of Technology（意大利理工学院）；University Hospital Basel（巴塞尔大学医院）；DKFZ（德累斯顿生物医学研究中心）；Jagiellonian University（雅盖隆大学）；University of Illinois Urbana–Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of California, San Francisco（旧金山大学）；NVIDIA（NVIDIA公司）；Johns Hopkins Medicine（约翰霍普金斯医学）
Johns Hopkins University（约翰霍普金斯大学）；University of Bologna（博洛尼亚大学）；Italian Institute of Technology（意大利理工学院）；University of California, San Francisco（加州大学旧金山分校）；Istanbul Medipol University（伊斯坦布尔Medipol大学）；University of Zurich（苏黎世大学）；ETH AI Center（ETH人工智能中心）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；École Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）
Exploiting Longitudinal Context in Clinician-Verified Interactive Lesion Tracking
German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Germany（德国癌症研究中心（DKFZ）海德堡，医学图像计算部，德国）；Faculty of Mathematics and Computer Science, Heidelberg University, Germany（海德堡大学数学与计算机科学学院，德国）；HIDSS4Health -- Helmholtz Information and Data Science School for Health, Karlsruhe/Heidelberg, Germany（HIDSS4Health——海德堡信息与数据科学健康学校，卡尔斯鲁厄/海德堡，德国）；Medical Faculty, Heidelberg University, Germany（海德堡大学医学学院，德国）；University Hospital Brandenburg an der Havel, Brandenburg Medical School Theodor Fontane, Germany（勃兰登堡运河大学医院，布兰登堡泰奥多尔·冯·_fontane医学学校，德国）；Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, Germany（放射肿瘤科模式分析与学习组，海德堡大学医院，德国）
Technical University of Munich（慕尼黑工业大学）；Klinikum Rechts der Isar（伊萨尔河右岸医院）；Imperial College London（帝国理工学院）；German Cancer Consortium DKTK（德国癌症联盟（DKTK））
The prediction of pancreatic ductal adenocarcinoma therapy response is a clinically challenging and important task in this high-mortality tumour entity. The training of neural networks able to tackle this challenge is impeded by a lack of large datasets and the difficult anatomical localisation of the pancreas. Here, we propose a hybrid deep neural network pipeline to predict tumour response to initial chemotherapy which is based on the Response Evaluation Criteria in Solid Tumors (RECIST) score, a standardised method for cancer response evaluation by clinicians as well as tumour markers, and …
Peripancreatic vessel segmentation and anatomical labeling play extremely important roles to assist the early diagnosis, surgery planning and prognosis for patients with pancreatic tumors. However, most current techniques cannot achieve satisfactory segmentation performance for peripancreatic veins and usually make predictions with poor integrity and connectivity. Besides, unsupervised labeling algorithms cannot deal with complex anatomical variation while fully supervised methods require a large number of voxel-wise annotations for training, which is very labor-intensive and time-consuming. T…
The ULS23 Challenge: a Baseline Model and Benchmark Dataset for 3D Universal Lesion Segmentation in Computed Tomography
Radboud University Medical Center（拉德堡德大学医学中心）；Jeroen Bosch Hospital（杰伦博世医院）；University Medical Center Groningen（格罗宁根大学医学中心）；Fraunhofer Institute for Digital Medicine MEVIS（弗劳恩霍夫数字医学梅维斯研究所）
