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 is available. However, more than often, we are facing the challenge that only (very) limited annotations are feasible to acquire, especially for hard tumors. Pancreatic ductal adenocarcinoma (PDAC) segmentation is one of the most challenging tumor segmentation tasks, yet critically important for clinical needs. Previous work on PDAC segmentation is limited to the moderate amounts of annotated patient images (n<300) from venous or venous+arterial phase CT scans. Based on a new self-learning framework, we propose to train the PDAC segmentation model using a much larger quantity of patients (n~=1,000), with a mix of annotated and un-annotated venous or multi-phase CT images. Pseudo annotations are generated by combining two teacher models with different PDAC segmentation specialties on unannotated images, and can be further refined by a teaching assistant model that identifies associated vessels around the pancreas. A student model is trained on both manual and pseudo annotated multi-phase images. Experiment results show that our proposed method provides an absolute improvement of 6.3% Dice score over the strong baseline of nnUNet trained on annotated images, achieving the performance (Dice = 0.71) similar to the inter-observer variability between radiologists.
Brunel University（布鲁内尔大学）；University College London（伦敦大学学院）
Pancreatic cancers have one of the worst prognoses compared to other cancers, as they are diagnosed when cancer has progressed towards its latter stages. The current manual histological grading for diagnosing pancreatic adenocarcinomas is time-consuming and often results in misdiagnosis. In digital pathology, AI-based cancer grading must be extremely accurate in prediction and uncertainty quantification to improve reliability and explainability and are essential for gaining clinicians trust in the technology. We present Bayesian Convolutional Neural Networks for automated pancreatic cancer gra…
University of Toronto（多伦多大学）；Lunenfeld-Tanenbaum Research Institute, Sinai Health System（西奈卫生系统卢嫩菲尔德-塔南鲍姆研究所）；McMaster University（麦克马斯特大学）；Hamilton Health Sciences（汉密尔顿健康科学中心）；Sunnybrook Health Sciences Centre（桑尼布鲁克健康科学中心）；Sunnybrook Research Institute（桑尼布鲁克研究所）
Cox proportional hazard model (CPH) is commonly used in clinical research for survival analysis. In quantitative medical imaging (radiomics) studies, CPH plays an important role in feature reduction and modeling. However, the underlying linear assumption of CPH model limits the prognostic performance. In addition, the multicollinearity of radiomic features and multiple testing problem further impedes the CPH models performance. In this work, using transfer learning, a convolutional neural network (CNN) based survival model was built and tested on preoperative CT images of resectable Pancreatic…
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…
University of Toronto（多伦多大学）；Lunenfeld-Tanenbaum Research Institute（卢嫩菲尔德-塔南鲍姆研究所）；Sinai Health System（西奈健康系统）；McMaster University（麦克马斯特大学）；Hamilton Health Sciences（汉密尔顿健康科学）；Juravinski Hospital and Cancer Centre（朱拉文斯基医院及癌症中心）；Sunnybrook Health Sciences Centre（桑尼布鲁克健康科学中心）；Sunnybrook Research Institute（桑尼布鲁克研究所）
Pancreatic Ductal Adenocarcinoma (PDAC) is one of the most aggressive cancers with an extremely poor prognosis. Radiomics has shown prognostic ability in multiple types of cancer including PDAC. However, the prognostic value of traditional radiomics pipelines, which are based on hand-crafted radiomic features alone is limited. Convolutional neural networks (CNNs) have been shown to outperform these feature-based models in computer vision tasks. However, training a CNN from scratch needs a large sample size which is not feasible in most medical imaging studies. As an alternative solution, CNN-b…
University of Toronto（多伦多大学）；Lunenfeld-Tanenbaum Research Institute, Sinai Health System（西奈卫生系统卢嫩菲尔德-塔南鲍姆研究所）；McMaster University（麦克马斯特大学）；Hamilton Health Sciences（汉密尔顿健康科学中心）；Sunnybrook Health Sciences Centre（桑尼布鲁克健康科学中心）；Sunnybrook Research Institute（桑尼布鲁克研究所）
As an analytic pipeline for quantitative imaging feature extraction and analysis, radiomics has grown rapidly in the past a few years. Recent studies in radiomics aim to investigate the relationship between tumors imaging features and clinical outcomes. Open source radiomics feature banks enable the extraction and analysis of thousands of predefined features. On the other hand, recent advances in deep learning have shown significant potential in the quantitative medical imaging field, raising the research question of whether predefined radiomics features have predictive information in addition…
East China Normal University（华东师范大学）；Johns Hopkins University（约翰斯·霍普金斯大学）；Shanghai Jiao Tong University（上海交通大学）；Johns Hopkins University School of Medicine（约翰斯·霍普金斯大学医学院）
Pancreatic ductal adenocarcinoma (PDAC) is the third most common cause of cancer death in the United States. Predicting tumors like PDACs (including both classification and segmentation) from medical images by deep learning is becoming a growing trend, but usually a large number of annotated data are required for training, which is very labor-intensive and time-consuming. In this paper, we consider a partially supervised setting, where cheap image-level annotations are provided for all the training data, and the costly per-voxel annotations are only available for a subset of them. We propose a…
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…
Johns Hopkins University（约翰斯·霍普金斯大学）；Johns Hopkins Medical Institutions（约翰斯·霍普金斯医疗机构）
Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers among the population. Screening for PDACs in dynamic contrast-enhanced CT is beneficial for early diagnosis. In this paper, we investigate the problem of automated detecting PDACs in multi-phase (arterial and venous) CT scans. Multiple phases provide more information than single phase, but they are unaligned and inhomogeneous in texture, making it difficult to combine cross-phase information seamlessly. We study multiple phase alignment strategies, i.e., early alignment (image registration), late alignment (high-level fe…
Technical University of Munich（慕尼黑工业大学）；Klinikum Rechts der Isar, Technical University of Munich（慕尼黑工业大学 rechts der Isar 医院）；Fudan University（复旦大学）
The lethal nature of pancreatic ductal adenocarcinoma (PDAC) calls for early differential diagnosis of pancreatic cysts, which are identified in up to 16% of normal subjects, and some of which may develop into PDAC. Previous computer-aided developments have achieved certain accuracy for classification on segmented cystic lesions in CT. However, pancreatic cysts have a large variation in size and shape, and the precise segmentation of them remains rather challenging, which restricts the computer-aided interpretation of CT images acquired for differential diagnosis. We propose a computer-aided f…
A Multi-Stage Fine-Tuning and Ensembling Strategy for Pancreatic Tumor Segmentation in Diagnostic and Therapeutic MRI
German Cancer Research Center (DKFZ) Heidelberg（德国癌症研究中心（DKFZ）海德堡）；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, DKFZ（海德堡影像中心，DKFZ）；Pattern Analysis and Learning Group（模式分析与学习小组）；Department of Radiation Oncology（放射肿瘤科）；Heidelberg University Hospital（海德堡大学医院）；International School of Medicine（国际医学院）；Istanbul Medipol University（伊斯坦布尔梅迪波尔大学）
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）
Automatic abnormality detection in abdominal CT scans can help doctors improve the accuracy and efficiency in diagnosis. In this paper we aim at detecting pancreatic ductal adenocarcinoma (PDAC), the most common pancreatic cancer. Taking the fact that the existence of tumor can affect both the shape and the texture of pancreas, we design a system to extract the shape and texture feature at the same time for detecting PDAC. In this paper we propose a two-stage method for this 3D classification task. First, we segment the pancreas into a binary mask. Second, a FusionNet is proposed to take both …
Early and Prediagnostic Detection of Pancreatic Cancer from Computed Tomography
Johns Hopkins University（约翰霍普金斯大学）；University of Bologna（博洛尼亚大学）；Istituto Italiano di Tecnologia（意大利技术研究院）；Shandong Provincial Qianfoshan Hospital（山东省千佛山医院）；Qilu Hospital of Shandong University（山东大学齐鲁医院）；Jagiellonian University（杰齐洛尼亚大学）；University of California, San Francisco（加州大学旧金山分校）；University of California, Berkeley（加州大学伯克利分校）；Johns Hopkins Medicine（约翰霍普金斯医学中心）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of Warmia and Mazury（马尔堡大学）；National Medical Institute of the Ministry of Internal Affairs and Administration（内政部和行政管理局国家医疗研究所）；Henan Provincial People’s Hospital & The People’s Hospital of Zhengzhou University（河南省人民医院及郑州大学人民医院）；City of Hope National Medical Center（希望国家医学中心）；Centre Européen de Calcul Atomique et Moléculaire, École Polytechnique Fédérale de Lausanne（欧洲原子和分子计算中心，洛桑联邦理工学院）；Northeastern University（东北大学）；Rutgers University（罗格斯大学）；The First Affiliated Hospital of Soochow University（苏州大学第一附属医院）；Massachusetts General Hospital（麻省总医院）
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…
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…
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…
National Technical University of Athens（雅典国立技术大学）；National and Kapodistrian University of Athens（雅典国立卡波迪斯特里安大学）；Aretaieion Hospital（阿雷泰翁医院）
Deep Convolutional Neural Networks (CNNs) for image classification successively alternate convolutions and downsampling operations, such as pooling layers or strided convolutions, resulting in lower resolution features the deeper the network gets. These downsampling operations save computational resources and provide some translational invariance as well as a bigger receptive field at the next layers. However, an inherent side-effect of this is that high-level features, produced at the deep end of the network, are always captured in low resolution feature maps. The inverse is also true, as sha…
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…
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）
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）
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 …
