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 segmentation-mesh-classification network (SMCN) to provide patient-level diagnosis, by fully utilizing the geometry and location information, which is accomplished by combining the anatomical structure and the semantic detection-by-segmentation network. SMCN learns the pancreas and mass segmentation task and builds an anatomical correspondence-aware organ mesh model by progressively deforming a pancreas prototype on the raw segmentation mask (i.e., mask-to-mesh). A new graph-based residual convolutional network (Graph-ResNet), whose nodes fuse the information of the mesh model and feature vectors extracted from the segmentation network, is developed to produce the patient-level differential classification results. Extensive experiments on 661 patients' CT scans (five phases per patient) show that SMCN can improve the mass segmentation and detection accuracy compared to the strong baseline method nnUNet (e.g., for nonPDAC, Dice: 0.611 vs. 0.478; detection rate: 89% vs. 70%), achieve similar sensitivity and specificity in differentiating PDAC and nonPDAC as expert radiologists (i.e., 94% and 90%), and obtain results comparable to a multimodality test [20] that combines clinical, imaging, and molecular testing for clinical management of patients.
Pancreatic cancer is one of the leading causes of cancer-related death. Accurate detection, segmentation, and differential diagnosis of the full taxonomy of pancreatic lesions, i.e., normal, seven major types of lesions, and other lesions, is critical to aid the clinical decision-making of patient management and treatment. However, existing works focus on segmentation and classification for very specific lesion types (PDAC) or groups. Moreover, none of the previous work considers using lesion prevalence-related non-imaging patient information to assist the differential diagnosis. To this end, …
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…
The Johns Hopkins University（约翰斯·霍普金斯大学）；Huazhong University of Science and Technology（华中科技大学）；The Johns Hopkins University School of Medicine（约翰斯·霍普金斯大学医学院）
Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers with an overall five-year survival rate of 8%. Due to subtle texture changes of PDAC, pancreatic dual-phase imaging is recommended for better diagnosis of pancreatic disease. In this study, we aim at enhancing PDAC automatic segmentation by integrating multi-phase information (i.e., arterial phase and venous phase). To this end, we present Hyper-Pairing Network (HPN), a 3D fully convolution neural network which effectively integrates information from different phases. The proposed approach consists of a dual path network…
Peking Union Medical College Hospital（北京协和医院）；Sun Yat-sen University（中山大学）
The rapid on-site evaluation (ROSE) technique can signifi-cantly accelerate the diagnosis of pancreatic cancer by im-mediately analyzing the fast-stained cytopathological images. Computer-aided diagnosis (CAD) can potentially address the shortage of pathologists in ROSE. However, the cancerous patterns vary significantly between different samples, making the CAD task extremely challenging. Besides, the ROSE images have complicated perturbations regarding color distribution, brightness, and contrast due to different staining qualities and various acquisition device types. To address these chall…
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…
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）
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）
South China University of Technology（华南理工大学）；Guangdong Provincial People’s Hospital（广东省人民医院）；Guangdong Academy of Medical Sciences（广东省医学科学院）
Ultrasonography is an important routine examination for breast cancer diagnosis, due to its non-invasive, radiation-free and low-cost properties. However, the diagnostic accuracy of breast cancer is still limited due to its inherent limitations. It would be a tremendous success if we can precisely diagnose breast cancer by breast ultrasound images (BUS). Many learning-based computer-aided diagnostic methods have been proposed to achieve breast cancer diagnosis/lesion classification. However, most of them require a pre-define ROI and then classify the lesion inside the ROI. Conventional classif…
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）
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）
The rapid on-site evaluation (ROSE) technique can significantly ac-celerate the diagnostic workflow of pancreatic cancer by immediately analyzing the fast-stained cytopathological images with on-site pathologists. Computer-aided diagnosis (CAD) using the deep learning method has the potential to solve the problem of insufficient pathology staffing. However, the cancerous patterns of ROSE images vary greatly between different samples, making the CAD task extremely challenging. Besides, due to different staining qualities and various types of acquisition devices, the ROSE images also have compli…
Lifespan tree of brain anatomy: diagnostic values for motor and cognitive neurodegenerative diseases
CNRS（法国国家科学研究中心）；Univ. Bordeaux（波尔多大学）；Bordeaux INP（波尔多国立综合理工学院）；LABRI（计算机科学、通信与图像处理实验室）；ITACA（ITACA机构）；Universitat Politècnica de València（瓦伦西亚理工大学）；ToNIC, Toulouse NeuroImaging Center（图卢兹神经影像中心）；Université de Toulouse（图卢兹大学）；Inserm（法国国家健康与医学研究院）；UPS（图卢兹第三大学）；CHU Bordeaux（波尔多大学医院）；IMNc（认知神经影像与神经疾病研究所）；University of Otago（奥塔哥大学）；New Zealand Brain Research Institute（新西兰脑研究所）；Neurocentre Magendie（马让迪神经中心）；CHU de Bordeaux（波尔多大学医院）
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）
Memorial Sloan Kettering Cancer Center（纪念斯隆凯特琳癌症中心）
Vision transformers, with their ability to more efficiently model long-range context, have demonstrated impressive accuracy gains in several computer vision and medical image analysis tasks including segmentation. However, such methods need large labeled datasets for training, which is hard to obtain for medical image analysis. Self-supervised learning (SSL) has demonstrated success in medical image segmentation using convolutional networks. In this work, we developed a \underline{s}elf-distillation learning with \underline{m}asked \underline{i}mage modeling method to perform SSL for vision \u…
Pathological area segmentation in cardiac magnetic resonance (MR) images plays a vital role in the clinical diagnosis of cardiovascular diseases. Because of the irregular shape and small area, pathological segmentation has always been a challenging task. We propose an anatomy prior based framework, which combines the U-net segmentation network with the attention technique. Leveraging the fact that the pathology is inclusive, we propose a neighborhood penalty strategy to gauge the inclusion relationship between the myocardium and the myocardial infarction and no-reflow areas. This neighborhood …
University of Central Florida（中佛罗里达大学）；Foundation for Research and Technology Hellas（希腊研究与技术基金会）；Mayo Clinic（梅奥诊所）
Intraductal papillary mucinous neoplasm (IPMN) is a precursor to pancreatic ductal adenocarcinoma. While over half of patients are diagnosed with pancreatic cancer at a distant stage, patients who are diagnosed early enjoy a much higher 5-year survival rate of $34\%$ compared to $3\%$ in the former; hence, early diagnosis is key. Unique challenges in the medical imaging domain such as extremely limited annotated data sets and typically large 3D volumetric data have made it difficult for deep learning to secure a strong foothold. In this work, we construct two novel "inflated" deep network arch…
GAPNet: Granularity Attention Network with Anatomy-Prior-Constraint for Carotid Artery Segmentation
Shandong Artificial Intelligence Institute, Qilu University of Technology (Shandong Academy of Sciences)（齐鲁工业大学（山东省科学院）山东省人工智能研究院）；School of Artificial Intelligence, Hebei University of Technology（河北工业大学人工智能学院）；School of Computer, Electronics and Information, Guangxi University（广西大学计算机与电子信息学院）；School of Intelligence Science and Technology, Nanjing University（南京大学智能科学与技术学院）；University Paris Dauphine, PSL Research University, CNRS, UMR 7534, CEREMADE（巴黎多芬大学（PSL研究大学）、法国国家科学研究中心UMR 7534、CEREMADE）
Southern Medical University（南方医科大学）；ZhuJiang Hospital（珠江医院）；Guangdong Provincial Research Center for Artificial Organ and Tissue Engineering（广东省人工器官与组织工程研究中心）；Institute of Regenerative Medicine（再生医学研究所）；State Key Laboratory of Organ Failure Research（器官衰竭研究国家重点实验室）；School of Biomedical Engineering（生物医学工程学院）；Guangzhou Medical University（广州医科大学）；The 2nd affiliated hospital of Guangzhou Medical University（广州医科大学附属第二医院）
Objectives: Pancreatic cancer is a lethal disease, hard to diagnose and usually results in poor prognosis and high mortality. Developing an artificial intelligence (AI) algorithm to accurately and universally predict the early cancer risk of all kinds of pancreatic cancer is extremely important. We propose an ensemble AI algorithm to predict universally cancer risk of all kinds of pancreatic lesions with noncontrast CT. Methods: Our algorithm combines the radiomics method and a support tensor machine (STM) by the evidence reasoning (ER) technique to construct a binary classifier, called RadSTM…
Anatomy-Guided Multitask Learning for MRI-Based Classification of Placenta Accreta Spectrum and its Subtypes
Sun Yat-sen University（中山大学）；The Third Affiliated Hospital of Guangzhou Medical University（广州医科大学附属第三医院）；Hainan General Hospital, Hainan Medical University（海南医科大学附属海南医院）
Nanjing University of Information Science & Technology（南京信息工程大学）
Cerebrovascular accident, or commonly known as stroke, is an acute disease with extreme impact on patients and healthcare systems and is the second largest cause of death worldwide. Fast and precise stroke lesion detection and location is an extreme important process with regards to stroke diagnosis, treatment, and prognosis. Except from the manual segmentation approach, machine learning based segmentation methods are the most promising ones when considering efficiency and accuracy, and convolutional neural network based models are the first of its kind. However, most of these neural network m…
Magnetic Resonance Imaging (MRI) plays an important role in diagnosing the parotid tumor, where accurate segmentation of tumors is highly desired for determining appropriate treatment plans and avoiding unnecessary surgery. However, the task remains nontrivial and challenging due to ambiguous boundaries and various sizes of the tumor, as well as the presence of a large number of anatomical structures around the parotid gland that are similar to the tumor. To overcome these problems, we propose a novel anatomy-aware framework for automatic segmentation of parotid tumors from multimodal MRI. Fir…
We hypothesize that probabilistic voxel-level classification of anatomy and malignancy in prostate MRI, although typically posed as near-identical segmentation tasks via U-Nets, require different loss functions for optimal performance due to inherent differences in their clinical objectives. We investigate distribution, region and boundary-based loss functions for both tasks across 200 patient exams from the publicly-available ProstateX dataset. For evaluation, we conduct a thorough comparative analysis of model predictions and calibration, measured with respect to multi-class volume segmentat…
Pathology-Guided AI System for Accurate Segmentation and Diagnosis of Cervical Spondylosis
School of Biomedical Engineering, Shanghai Jiao Tong University（上海交通大学生物医学工程学院）；National Engineering Research Center of Advanced Magnetic Resonance Technologies for Diagnosis and Therapy (NERC-AMRT)（先进磁共振诊断与治疗技术国家工程研究中心）；Med-X Research Institute, Shanghai Jiao Tong University（Med-X研究院，上海交通大学）；Department of Spine Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University（上海交通大学医学院附属仁济医院脊柱外科部）；Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine（上海交通大学医学院附属瑞金医院放射科）；Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University（上海交通大学医学院附属仁济医院放射科）
Medical University Vienna（维也纳医科大学）；University of Vienna（维也纳大学）；NVIDIA Corporation（英伟达公司）
Diagnosis and treatment guidance are aided by detecting relevant biomarkers in medical images. Although supervised deep learning can perform accurate segmentation of pathological areas, it is limited by requiring a-priori definitions of these regions, large-scale annotations, and a representative patient cohort in the training set. In contrast, anomaly detection is not limited to specific definitions of pathologies and allows for training on healthy samples without annotation. Anomalous regions can then serve as candidates for biomarker discovery. Knowledge about normal anatomical structure br…
Pancreatic cancer will soon be the second leading cause of cancer-related death in Western society. Imaging techniques such as CT, MRI and ultrasound typically help providing the initial diagnosis, but histopathological assessment is still the gold standard for final confirmation of disease presence and prognosis. In recent years machine learning approaches and pathomics pipelines have shown potential in improving diagnostics and prognostics in other cancerous entities, such as breast and prostate cancer. A crucial first step in these pipelines is typically identification and segmentation of t…
Computer-aided diagnosis (CAD) has long become an integral part of radiological management of breast disease, facilitating a number of important clinical applications, including quantitative assessment of breast density and early detection of malignancies based on X-ray mammography. Common to such applications is the need to automatically discriminate between breast tissue and adjacent anatomy, with the latter being predominantly represented by pectoralis major (or pectoral muscle). Especially in the case of mammograms acquired in the mediolateral oblique (MLO) view, the muscle is easily confu…
Crop and Couple: cardiac image segmentation using interlinked specialist networks
Queen Mary University of London（伦敦玛丽女王大学）；Queen Mary's Digital Environment Research Institute (DERI)（伦敦玛丽女王大学数字环境研究所）；University College London（伦敦大学学院）
A Hybrid Deep Learning Classification of Perimetric Glaucoma Using Peripapillary Nerve Fiber Layer Reflectance and Other OCT Parameters from Three Anatomy Regions
Casey Eye Institute, Oregon Health & Science University（俄勒冈健康与科学大学凯西眼科研究所）；OHSU-PSU School of Public Health（俄勒冈健康与科学大学-波特兰州立大学公共卫生学院）；Bascom Palmer Eye Institute, University of Miami（迈阿密大学巴斯康帕尔默眼科研究所）；Doheny Eye Center and David Geffen School of Medicine at UCLA（加州大学洛杉矶分校多希尼眼科中心与大卫·格芬医学院）；Southern California Eye Institute（南加州眼科研究所）；Wills Eye Hospital（威尔斯眼科医院）
CADS: A Comprehensive Anatomical Dataset and Segmentation for Whole-Body Anatomy in Computed Tomography
Department of Quantitative Biomedicine, University of Zurich（定量生物医学系，苏黎世大学）；ETH AI Center, ETH Zurich（ETH人工智能中心，苏黎世联邦理工学院）；Department of Radiation Oncology, University Hospital and University of Zurich（放射肿瘤科，苏黎世大学医院和苏黎世大学）；Faculty of Medicine, University of Zurich（医学系，苏黎世大学）；Department of Neurology and Clinical Neuroscience Center, University Hospital Zurich and University of Zurich（神经科和临床神经科学中心，苏黎世大学医院和苏黎世大学）；International School of Medicine, Istanbul Medipol University（国际医学院，伊斯坦布尔Medipol大学）；School of Computation, Information and Technology, Technical University of Munich（计算、信息与技术学院，慕尼黑技术大学）；Department of Diagnostic and Interventional Neuroradiology, School of Medicine and Health, Technical University of Munich（诊断与介入神经放射科，医学与健康学院，慕尼黑技术大学）；Department of Radiation Oncology, TUM University Hospital Rechts der Isar, TUM School of Medicine and Health, Technical University of Munich（放射肿瘤科，慕尼黑技术大学医院Rechts der Isar，慕尼黑技术大学医学与健康学院）；Institute of Radiation Medicine (IRM), Helmholtz Zentrum München (HMGU)（放射医学研究所（IRM），亥姆霍兹慕尼黑研究中心（HMGU））；German Consortium for Translational Cancer Research (DKTK), Partner Site Munich, Munich, Germany（德国转化癌症研究联盟（DKTK），慕尼黑分部，德国）；University Hospital Essen, Institute of Interventional and Diagnostic Radiology and Neuroradiology（埃森大学医院，介入与诊断放射学及神经放射学研究所）；University Hospital Essen, Institute for Artificial Intelligence in Medicine（埃森大学医院，医学人工智能研究所）；Institute of Diagnostic and Interventional Radiology, Pediatric Radiology and Neuroradiology, University Medical Center Rostock（诊断与介入放射学、儿童放射学及神经放射学研究所，罗斯托克大学医学中心）；Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna（生物医学成像与图像引导治疗系，维也纳医科大学）；Department of Radiology, University Hospital Basel（放射科，巴塞尔大学医院）；Department of Radiology, Cambridge University Hospitals NHS Foundation Trust（放射科，剑桥大学医院 NHS 基础信托）；Labelata GmbH（Labelata 公司）；Visual and Data-Centric Computing, Zuse Institute Berlin (ZIB)（视觉与数据导向计算，柏林 Zuse 研究所（ZIB））；Brigham and Women’s Hospital（布里格姆和女性医院）
University of Idaho（爱达荷大学）；University of Utah School of Medicine（犹他大学医学院）
Breast tumor segmentation is a critical task in computer-aided diagnosis (CAD) systems for breast cancer detection because accurate tumor size, shape and location are important for further tumor quantification and classification. However, segmenting small tumors in ultrasound images is challenging, due to the speckle noise, varying tumor shapes and sizes among patients, and the existence of tumor-like image regions. Recently, deep learning-based approaches have achieved great success for biomedical image analysis, but current state-of-the-art approaches achieve poor performance for segmenting …
Sano Centre for Computational Medicine（萨诺计算医学中心）；Informatics Institute, University of Amsterdam（阿姆斯特丹大学信息学研究所）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）
Cardiac Magnetic Resonance Imaging is commonly used for the assessment of the cardiac anatomy and function. The delineations of left and right ventricle blood pools and left ventricular myocardium are important for the diagnosis of cardiac diseases. Unfortunately, the movement of a patient during the CMR acquisition procedure may result in motion artifacts appearing in the final image. Such artifacts decrease the diagnostic quality of CMR images and force redoing of the procedure. In this paper, we present a Multi-task Swin UNEt TRansformer network for simultaneous solving of two tasks in the …
