Anatomical Consistency Distillation and Inconsistency Synthesis for Brain Tumor Segmentation with Missing Modalities
University of Science and Technology of China（中国科学技术大学）；Tianjin University（天津大学）；Beijing Tiantan Hospital, Capital Medical University（首都医科大学附属北京天坛医院）；Hefei Comprehensive National Science Center（合肥综合性国家科学中心）
Multi-modal Magnetic Resonance Imaging (MRI) is imperative for accurate brain tumor segmentation, offering indispensable complementary information. Nonetheless, the absence of modalities poses significant challenges in achieving precise segmentation. Recognizing the shared anatomical structures between mono-modal and multi-modal representations, it is noteworthy that mono-modal images typically exhibit limited features in specific regions and tissues. In response to this, we present Anatomical Consistency Distillation and Inconsistency Synthesis (ACDIS), a novel framework designed to transfer anatomical structures from multi-modal to mono-modal representations and synthesize modality-specific features. ACDIS consists of two main components: Anatomical Consistency Distillation (ACD) and Modality Feature Synthesis Block (MFSB). ACD incorporates the Anatomical Feature Enhancement Block (AFEB), meticulously mining anatomical information. Simultaneously, Anatomical Consistency ConsTraints (ACCT) are employed to facilitate the consistent knowledge transfer, i.e., the richness of information and the similarity in anatomical structure, ensuring precise alignment of structural features across mono-modality and multi-modality. Complementarily, MFSB produces modality-specific features to rectify anatomical inconsistencies, thereby compensating for missing information in the segmented features. Through validation on the BraTS2018 and BraTS2020 datasets, ACDIS substantiates its efficacy in the segmentation of brain tumors with missing MRI modalities.
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）
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。
A Unified Attention U-Net Framework for Cross-Modality Tumor Segmentation in MRI and CT
Department of Computer Science, New Mexico State University（计算机科学系，新墨西哥州立大学）；Department of Plant and Environmental Sciences, New Mexico State University（植物与环境科学系，新墨西哥州立大学）
Imperial College London（帝国理工学院）；TECNALIA（TECNALIA（西班牙技术中心））；University of the Basque Country (UPV/EHU)（巴斯克大学）；Royal Brompton Hospital（皇家布朗普顿医院）
Despite recent advances in the accuracy of brain tumor segmentation, the results still suffer from low reliability and robustness. Uncertainty estimation is an efficient solution to this problem, as it provides a measure of confidence in the segmentation results. The current uncertainty estimation methods based on quantile regression, Bayesian neural network, ensemble, and Monte Carlo dropout are limited by their high computational cost and inconsistency. In order to overcome these challenges, Evidential Deep Learning (EDL) was developed in recent work but primarily for natural image classific…
BrainNormalizer: Anatomy-Informed Pseudo-Healthy Brain Reconstruction from Tumor MRI via Edge-Guided ControlNet
University of Pittsburgh（匹兹堡大学）；Georgia Institute of Technology（佐治亚理工学院）；University of Texas at Austin（德克萨斯大学奥斯汀分校）；Cedars-Sinai Medical Center（西德萨斯医疗中心）；Mayo Clinic Arizona（梅奥诊所亚利桑那分部）
Effective, robust, and automatic tools for brain tumor segmentation are needed for the extraction of information useful in treatment planning from magnetic resonance (MR) images. Context-aware artificial intelligence is an emerging concept for the development of deep learning applications for computer-aided medical image analysis. In this work, it is investigated whether the addition of contextual information from the brain anatomy in the form of white matter, gray matter, and cerebrospinal fluid masks and probability maps improves U-Net-based brain tumor segmentation. The BraTS2020 dataset wa…
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 and Institute（四川省肿瘤医院研究所）；Sichuan University West China Hospital（四川大学华西医院）；SenseTime Research（商汤科技研究院）
Accurate segmentation of Anatomical brain Barriers to Cancer spread (ABCs) plays an important role for automatic delineation of Clinical Target Volume (CTV) of brain tumors in radiotherapy. Despite that variants of U-Net are state-of-the-art segmentation models, they have limited performance when dealing with ABCs structures with various shapes and sizes, especially thin structures (e.g., the falx cerebri) that span only few slices. To deal with this problem, we propose a High and Multi-Resolution Network (HMRNet) that consists of a multi-scale feature learning branch and a high-resolution bra…
The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Shenzhen Research Institute of Big Data（深圳大数据研究院）；The First Affiliated Hospital of Jinan University（暨南大学附属第一医院）
Automatic segmentation of hepatocellular carcinoma (HCC) in Digital Subtraction Angiography (DSA) videos can assist radiologists in efficient diagnosis of HCC and accurate evaluation of tumors in clinical practice. Few studies have investigated HCC segmentation from DSA videos. It shows great challenging due to motion artifacts in filming, ambiguous boundaries of tumor regions and high similarity in imaging to other anatomical tissues. In this paper, we raise the problem of HCC segmentation in DSA videos, and build our own DSA dataset. We also propose a novel segmentation network called DSA-LT…
Memorial Sloan Kettering Cancer Center（纪念斯隆-凯特琳癌症中心）
Purpose: Radiotherapy presents unique challenges and clinical requirements for longitudinal tumor and organ-at-risk (OAR) prediction during treatment. The challenges include tumor inflammation/edema and radiation-induced changes in organ geometry, whereas the clinical requirements demand flexibility in input/output sequence timepoints to update the predictions on rolling basis and the grounding of all predictions in relationship to the pre-treatment imaging information for response and toxicity assessment in adaptive radiotherapy. Methods: To deal with the aforementioned challenges and to comp…
F2PASeg: Feature Fusion for Pituitary Anatomy Segmentation in Endoscopic Surgery
1Centre for Artificial Intelligence；Robotics, Hong Kong Institute of Science \& Innovation, Chinese Academy of Sciences 2The University of Hong Kong 3Peking Union Medical College Hospital, Chinese Academy of Medical Science
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…
South China University of Technology（华南理工大学）；University of Utah（犹他大学）
In medical applications, the same anatomical structures may be observed in multiple modalities despite the different image characteristics. Currently, most deep models for multimodal segmentation rely on paired registered images. However, multimodal paired registered images are difficult to obtain in many cases. Therefore, developing a model that can segment the target objects from different modalities with unpaired images is significant for many clinical applications. In this work, we propose a novel two-stream translation and segmentation unified attentional generative adversarial network (U…
PAII Inc（PAII公司）；Ping An Technology（平安科技）；Chang Gung Memorial Hospital（长庚纪念医院）
Monitoring treatment response in longitudinal studies plays an important role in clinical practice. Accurately identifying lesions across serial imaging follow-up is the core to the monitoring procedure. Typically this incorporates both image and anatomical considerations. However, matching lesions manually is labor-intensive and time-consuming. In this work, we present deep lesion tracker (DLT), a deep learning approach that uses both appearance- and anatomical-based signals. To incorporate anatomical constraints, we propose an anatomical signal encoder, which prevents lesions being matched w…
A Study of Anatomical Priors for Deep Learning-Based Segmentation of Pheochromocytoma in Abdominal CT
Imaging Biomarkers and Computer-Aided Diagnosis Laboratory（影像生物标记与计算机辅助诊断实验室）；Clinical Center, NIH（国家卫生研究院临床中心）；Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH（欧尼丝·凯蒂·施里弗国家儿童健康与人类发展研究所，国家卫生研究院）；National Cancer Institute, NIH（国家癌症研究所，国家卫生研究院）；National Institute of Diabetes and Digestive and Kidney Diseases, NIH（国家糖尿病、消化系统与肾病研究所，国家卫生研究院）
HANS-Net: Hyperbolic Convolution and Adaptive Temporal Attention for Accurate and Generalizable Liver and Tumor Segmentation in CT Imaging
Department of Computer Science and Engineering, United International University（计算机科学与工程系，国际大学）；Faculty of Science and Technology, Charles Darwin University（科学与技术学院，查尔斯达尔文大学）；Faculty of Science and Information Technology, Charles Darwin University（科学与信息技术学院，查尔斯达尔文大学）
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…
CLIP-Driven Universal Model for Organ Segmentation and Tumor Detection
City University of Hong Kong（香港城市大学）；Johns Hopkins University（约翰斯·霍普金斯大学）；Vanderbilt University（范德堡大学）；Chinese University of Hong Kong（香港中文大学）；CUHK Shenzhen Research Institute（香港中文大学深圳研究院）；NVIDIA（英伟达公司）
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 …
Anatomy-Informed Deep Learning and Radiomics for Automated Neurofibroma Segmentation in Whole-Body MRI
Institute for Applied Medical Informatics（应用医学信息学研究所）；Institute of Computational Neuroscience（计算神经科学研究所）；Center for Biomedical Artificial Intelligence (bAIome)（生物医学人工智能中心）；University Medical Center Hamburg-Eppendorf（汉堡埃彭多夫大学医学中心）；Department of Diagnostic and Interventional Radiology and Nuclear Medicine（诊断与介入放射学及核医学部门）；Department of Neurology（神经内科部门）
Facebook Artificial Intelligence（脸书人工智能）；University of Southern California（南加州大学）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）
Deformable registration of magnetic resonance images between patients with brain tumors and healthy subjects has been an important tool to specify tumor geometry through location alignment and facilitate pathological analysis. Since tumor region does not match with any ordinary brain tissue, it has been difficult to deformably register a patients brain to a normal one. Many patient images are associated with irregularly distributed lesions, resulting in further distortion of normal tissue structures and complicating registration's similarity measure. In this work, we follow a multi-step contex…
Background and Purpose: Radiomics features are used to identify disease types and predict therapy outcomes. However, how the radiomics features are different among different anatomical structures has never been investigated. Hence, we analyzed the radiomics features of 22 anatomical structures in the head and neck area in CT images. Furthermore, we studied whether CT radiomics can classify anatomical structures of the head and neck using unsupervised machine-learning techniques. Materials and methods: We obtained IMRT/VMAT treatment planning data from 36 patients treated for head and neck canc…
Technical University of Kosice（科希策工业大学）；Pavol Jozef Safarik University（帕沃尔·约瑟夫·沙法里克大学）；Louis Pasteur University Hospital（路易斯·巴斯德大学医院）
The kidney cancer is one of the most common cancer types. The treatment frequently include surgical intervention. However, surgery is in this case particularly challenging due to regional anatomical relations. Organ delineation can significantly improve surgical planning and execution. In this contribution, we propose ensemble of two fully convolutional networks for segmentation of kidney, tumor, veins and arteries. While SegResNet architecture achieved better performance on tumor, the nnU-Net provided more precise segmentation for kidneys, arteries and veins. So in our proposed approach we co…
Multimodal positron emission tomography-computed tomography (PET-CT) is used routinely in the assessment of cancer. PET-CT combines the high sensitivity for tumor detection with PET and anatomical information from CT. Tumor segmentation is a critical element of PET-CT but at present, there is not an accurate automated segmentation method. Segmentation tends to be done manually by different imaging experts and it is labor-intensive and prone to errors and inconsistency. Previous automated segmentation methods largely focused on fusing information that is extracted separately from the PET and CT…
Leiden University Medical Center（莱顿大学医学中心）；Sharif University of Technology（谢里夫理工大学）；Netherlands Cancer Institute（荷兰癌症研究所）；Delft University of Technology（代尔夫特理工大学）
Manual or automatic delineation of the esophageal tumor in CT images is known to be very challenging. This is due to the low contrast between the tumor and adjacent tissues, the anatomical variation of the esophagus, as well as the occasional presence of foreign bodies (e.g. feeding tubes). Physicians therefore usually exploit additional knowledge such as endoscopic findings, clinical history, additional imaging modalities like PET scans. Achieving his additional information is time-consuming, while the results are error-prone and might lead to non-deterministic results. In this paper we aim t…
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology（华中科技大学同济医学院同济医院）；Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions（深港脑科学创新研究院——深圳基础研究机构）；United Imaging Research Institute of Innovative Medical Equipment（联影创新医疗装备研究院）；Shenzhen Bay Laboratory（深圳湾实验室）
Achieving accurate and automated tumor segmentation plays an important role in both clinical practice and radiomics research. Segmentation in medicine is now often performed manually by experts, which is a laborious, expensive and error-prone task. Manual annotation relies heavily on the experience and knowledge of these experts. In addition, there is much intra- and interobserver variation. Therefore, it is of great significance to develop a method that can automatically segment tumor target regions. In this paper, we propose a deep learning segmentation method based on multimodal positron em…
University of Sydney（悉尼大学）；Shanghai Jiao Tong University（上海交通大学）
Multi-modality Fluorodeoxyglucose (FDG) positron emission tomography / computed tomography (PET/CT) has been routinely used in the assessment of common cancers, such as lung cancer, lymphoma, and melanoma. This is mainly attributed to the fact that PET/CT combines the high sensitivity for tumor detection of PET and anatomical information from CT. In PET/CT image assessment, automatic tumor segmentation is an important step, and in recent years, deep learning based methods have become the state-of-the-art. Unfortunately, existing methods tend to over-segment the tumor regions and include region…
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…
CancerUniT: Towards a Single Unified Model for Effective Detection, Segmentation, and Diagnosis of Eight Major Cancers Using a Large Collection of CT Scans
DAMO Academy, Alibaba Group（阿里巴巴集团达摩院）；Johns Hopkins University（约翰斯·霍普金斯大学）；Hupan Lab（湖畔实验室）；Sun Yat-sen University Cancer Center（中山大学肿瘤防治中心）；Sichuan Cancer Hospital（四川省肿瘤医院）；The First Affiliated Hospital of Zhejiang University（浙江大学医学院附属第一医院）；Shengjing Hospital of China Medical University（中国医科大学附属盛京医院）；Guangdong Provincial People’s Hospital（广东省人民医院）
HiFi-Syn: Hierarchical Granularity Discrimination for High-Fidelity Synthesis of MR Images with Structure Preservation
Ruijin Hospital, Shanghai Jiao Tong University School of Medicine（上海交通大学医学院附属瑞金医院）；College of Health Science and Technology, Shanghai Jiao Tong University School of Medicine（上海交通大学医学院健康科学技术学院）；Clinical Neuroscience Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine（上海交通大学医学院附属瑞金医院临床神经科学中心）；National Engineering Research Center of Advanced Magnetic Resonance Technologies for Diagnosis and Therapy, Shanghai Jiao Tong University（上海交通大学先进磁共振诊断与治疗技术国家工程研究中心）；Ping An Technology (Shenzhen) Co., Ltd.（平安科技（深圳）有限公司）
AG-CRC: Anatomy-Guided Colorectal Cancer Segmentation in CT with Imperfect Anatomical Knowledge
Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Shanghai Digital Medicine Innovation Center（上海数字医学创新中心）；Ruijin Hospital（瑞金医院）；Shanghai Jiao Tong University School of Medicine（上海交通大学医学院）
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…
Towards Reliable Pediatric Brain Tumor Segmentation: Task-Specific nnU-Net Enhancements
Institute of Natural Sciences, School of Mathematical Sciences, MOE-LSC, Shanghai Jiao Tong University（自然科学院，数学科学学院，MOE-LSC，上海交通大学）；Department of Radiology, Huashan Hospital, Fudan University（放射科，华山医院，复旦大学）；Department of Neurosurgery, Huashan Hospital, Fudan University（神经外科，华山医院，复旦大学）
Size and Smoothness Aware Adaptive Focal Loss for Small Tumor Segmentation
Dept of CSE, Bangladesh University of Business and Technology（信息与通信技术系，孟加拉国商业大学）；Dept of Information and Communication Technology, Islamic University（信息与通信技术系，伊斯兰大学）；IICT, Bangladesh University of Engineering and Technology（工程与技术研究所，孟加拉国工程大学）；Dept of CSE, Green University of Bangladesh（计算机科学与工程系，绿色大学）；School of Electrical Engineering and Robotics（电气工程与机器人学院）
A Scalable AI Driven, IoT Integrated Cognitive Digital Twin for Multi-Modal Neuro-Oncological Prognostics and Tumor Kinetics Prediction using Enhanced Vision Transformer and XAI
University of Calcutta（加尔各答大学）；Institute of Engineering and Management（工程与管理学院）；University of Engineering and Management（工程与管理大学）；San Francisco State University（旧金山州立大学）
The Brain Tumor Sequence Registration (BraTS-Reg) Challenge: Establishing Correspondence Between Pre-Operative and Follow-up MRI Scans of Diffuse Glioma Patients
Division of Computational Pathology, Department of Pathology and Laboratory Medicine, Indiana University School of Medicine（印第安纳大学医学院计算病理学系）；Department of Electrical and Systems Engineering, Washington University in St. Louis（圣路易斯华盛顿大学电气与系统工程系）；GE HealthCare（GE医疗）；Center for Artificial Intelligence and Data Science for Integrated Diagnostics (AI2D) and Center for Biomedical Image Computing and Analytics (CBICA), University of Penn（宾夕法尼亚大学综合诊断人工智能与数据科学中心及生物医学图像计算与分析中心）
ARC Training Centre for Innovative BioEngineering（ARC创新生物工程培训中心）
Distant metastases (DM) refer to the dissemination of tumors, usually, beyond the organ where the tumor originated. They are the leading cause of death in patients with soft-tissue sarcomas (STSs). Positron emission tomography-computed tomography (PET-CT) is regarded as the imaging modality of choice for the management of STSs. It is difficult to determine from imaging studies which STS patients will develop metastases. 'Radiomics' refers to the extraction and analysis of quantitative features from medical images and it has been employed to help identify such tumors. The state-of-the-art in ra…
