Cross-modality Knowledge Transfer for Prostate Segmentation from CT Scans
Columbia University Irving Medical Center（哥伦比亚大学欧文医学中心）；University of Central Florida（中佛罗里达大学）；Chung Yuan Christian University（中原大学）；New York Presbyterian Hospital（纽约长老会医院）；Weill Cornell Medical College（威尔康奈尔医学院）
Yucheng Liu, Naji Khosravan, Yulin Liu, Joseph Stember, Jonathan Shoag, Christopher E. Barbieri, Ulas Bagci, Sachin Jambawalikar
Creating large scale high-quality annotations is a known challenge in medical imaging. In this work, based on the CycleGAN algorithm, we propose leveraging annotations from one modality to be useful in other modalities. More specifically, the proposed algorithm creates highly realistic synthetic CT images (SynCT) from prostate MR images using unpaired data sets. By using SynCT images (without segmentation labels) and MR images (with segmentation labels available), we have trained a deep segmentation network for precise delineation of prostate from real CT scans. For the generator in our CycleGAN, the cycle consistency term is used to guarantee that SynCT shares the identical manually-drawn, high-quality masks originally delineated on MR images. Further, we introduce a cost function based on structural similarity index (SSIM) to improve the anatomical similarity between real and synthetic images. For segmentation followed by the SynCT generation from CycleGAN, automatic delineation is achieved through a 2.5D Residual U-Net. Quantitative evaluation demonstrates comparable segmentation results between our SynCT and radiologist drawn masks for real CT images, solving an important problem in medical image segmentation field when ground truth annotations are not available for the modality of interest.
AI-based Automatic Segmentation of Prostate on Multi-modality Images: A Review
Soochow University（苏州大学）；Peking University First Hospital（北京大学第一医院）；Duke Kunshan University（昆山杜克大学）；Swinburne University of Technology（斯威本科技大学）；The Hong Kong Polytechnic University（香港理工大学）；UT Southwestern Medical Center（德克萨斯大学西南医学中心）
ProstAttention-Net: A deep attention model for prostate cancer segmentation by aggressiveness in MRI scans
Univ Lyon（里昂大学）；CNRS（法国国家科学研究中心）；Inserm（法国国家健康与医学研究院）；INSA Lyon（里昂国立应用科学学院）；UCBL（里昂第一大学）；CREATIS（里昂影像与信息处理研究中心）；Hospices Civils de Lyon（里昂民用医院集团）；Hôpital Edouard Herriot（爱德华·埃里奥医院）；University of Sherbrooke（舍布鲁克大学）
Towards Multi-modality Fusion and Prototype-based Feature Refinement for Clinically Significant Prostate Cancer Classification in Transrectal Ultrasound
School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University（深圳大学医学部生物医学工程学院）；Sun Yat-sen University Cancer Center（中山大学肿瘤防治中心）
DiagSet: a dataset for prostate cancer histopathological image classification
Diagnostyka Consilio Sp. z o.o.（诊断康西利奥有限公司）；AGH University of Science and Technology（AGH科技大学）；Andrzej Frycz Modrzewski Krakow University（安德烈·弗赖奇·莫德热夫斯基克拉科夫大学）
Deformable MRI Sequence Registration for AI-based Prostate Cancer Diagnosis
Radboudumc（拉德堡德大学医学中心）；Fraunhofer MEVIS（弗劳恩霍夫梅维斯研究所）；Universität zu Lübeck（吕贝克大学）；University Medical Center Groningen（格罗宁根大学医学中心）；Netherlands Cancer Institute（荷兰癌症研究所）；Norwegian University of Science and Technology（挪威科技大学）
Anisotropic 3D Multi-Stream CNN for Accurate Prostate Segmentation from Multi-Planar MRI
University of Magdeburg（马格德堡大学）；Fraunhofer Institute for Digital Medicine MEVIS（弗劳恩霍夫数字医学研究所 MEVIS）；Radboud University Medical Center（拉德堡德大学医学中心）；University Hospital Magdeburg（马格德堡大学医院）；University of Bremen（不来梅大学）
Deep Learning (DL)-based Automatic Segmentation of the Internal Pudendal Artery (IPA) for Reduction of Erectile Dysfunction in Definitive Radiotherapy of Localized Prostate Cancer
University of Texas Southwestern Medical Center（德克萨斯大学西南医学中心）
AutoIQ: An Ensemble Framework for Automatic Assessment of Geometric Distortion in Prostate Diffusion-Weighted Imaging
Biomedical Imaging Research Institute, Cedars-Sinai Medical Center（生物医学成像研究 institute， Cedars-Sinai 医疗中心）；Department of Bioengineering, University of California（生物工程系，加州大学）；Siemens Medical Solutions USA Inc.（西门子医疗解决方案美国公司）；Siemens Healthineers AG（西门子健康影像股份有限公司）；Department of Imaging, Cedars-Sinai Medical Center（成像部，Cedars-Sinai 医疗中心）；Department of Nuclear Medicine, Cedars-Sinai Medical Center（核医学部，Cedars-Sinai 医疗中心）；Department of Urology, Cedars-Sinai Medical Center（泌尿科，Cedars-Sinai 医疗中心）
A Transfer Learning Approach for Automated Segmentation of Prostate Whole Gland and Transition Zone in Diffusion Weighted MRI
University of Toronto（多伦多大学）；Lunenfeld-Tanenbaum Research Institute, Sinai Health System（Sinai Health System 伦内菲尔德-塔嫩鲍姆研究所）；Brigham and Women’s Hospital（布莱根妇女医院）；The Hospital for Sick Children（SickKids 医院）
Cross-Modal Self-Attention Distillation for Prostate Cancer Segmentation
University of Shanghai for Science and Technology（上海理工大学）；Tongji University（同济大学）；National University of Singapore（新加坡国立大学）；Shanghai Fire Corps Hospital（上海消防总队医院）；Shanghai Tenth People’s Hospital（上海市第十人民医院）
Deep learning in magnetic resonance prostate segmentation: A review and a new perspective
Manchester Metropolitan University（曼彻斯特城市大学）；NHS（英国国民保健署）；The Leeds Teaching Hospital（利兹教学医院）；The Clatterbridge Cancer Center（克莱特布里奇癌症中心）；Leicester University（莱斯特大学）
Stanford University School of Medicine（斯坦福大学医学院）；Stanford University（斯坦福大学）；Bonn University Hospital（波恩大学医院）；Institute for Pathology and Cytology（病理与细胞学研究所）；University Hospital Muenster（明斯特大学医院）
Automatic classification of prostate MR series type using image content and metadata
Brigham and Women’s Hospital（布莱根妇女医院）；German Cancer Research Center（德国癌症研究中心）；Isomics（伊索米克斯公司）；PixelMed Publishing（PixelMed出版公司）；Massachusetts General Hospital（麻省总医院）
Enhancing Prostate Cancer Diagnosis with Deep Learning: A Study using mpMRI Segmentation and Classification
KLS Gogte Institute of Technology（KLS戈格特理工学院）；University of Stavanger（斯塔万格大学）；Dr. M.S. Sheshgiri College of Engineering and Technology（M.S.谢什吉里博士工程技术学院）；JN Medical College（JN医学院）
MS-Net: Multi-Site Network for Improving Prostate Segmentation with Heterogeneous MRI Data
The Chinese University of Hong Kong（香港中文大学）；Stanford University（斯坦福大学）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）
Feature Fusion of Raman Chemical Imaging and Digital Histopathology using Machine Learning for Prostate Cancer Detection
Technological University Dublin（都柏林理工大学）；University College Dublin（都柏林大学学院）；St. James Hospital（圣詹姆斯医院）；Trinity College Dublin（都柏林三一学院）；Beaumont Hospital（博蒙特医院）
Salient Region Matching for Fully Automated MR-TRUS Registration
School of Biomedical Engineering, Shenzhen University（深圳大学生物医学工程学院）；Medical UltraSound Image Computing (MUSIC) Lab（医学超声影像计算实验室）；Smart Medical Imaging, Learning and Engineering (SMILE) Lab（智能医学影像、学习与工程实验室）
A Dilated Residual Hierarchically Fashioned Segmentation Framework for Extracting Gleason Tissues and Grading Prostate Cancer from Whole Slide Images
Khalifa University（哈利法大学）；Beihang University（北京航空航天大学）；University of Louisville（路易斯维尔大学）
前列腺癌（PCa）是男性中第二致命的癌症形式，并且可以通过检查Gleason组织的结构表示来进行临床分级。本文提出了\RV{a new method}分割格里森组织\RV{(patch-wise) in order to grade PCa from the whole slide images (WSI).}此外，所提出的方法包括两个主要贡献：1）一个协同的混合膨胀因子和层次分解的潜在空间表示有效格里森组织提取，和2）一个三层的损失函数，可以惩罚不同的语义分割模型，准确地提取高度相关的模式。除此之外，在包含10，516个完整载玻片扫描（约71.7M块）的大规模PCa数据集上对所提出的框架进行了广泛评估，其中它在提取Gleason组织方面优于最先进的方案3.22%（就平均交叉-联合而言），在PCa进展分级方面优于最先进的方案6.91%（就F1评分而言）。
Distortion-Free High-Resolution PROPELLER-DWI: Technical Advances and Initial Demonstration for the Prostate
New York University Grossman School of Medicine（纽约大学格罗斯曼医学院）；Siemens Shenzhen Magnetic Resonance Ltd.（西门子深圳磁共振有限公司）；New York University Tandon School of Engineering（纽约大学坦顿工程学院）；Siemens Medical Solutions（西门子医疗解决方案）；Indiana University School of Medicine（印第安纳大学医学院）
Improve Cross-Modality Segmentation by Treating T1-Weighted MRI Images as Inverted CT Scans
Charité - Universitätsmedizin Berlin（柏林查理医院）；Freie Universität Berlin（柏林自由大学）；Humboldt Universität zu Berlin（柏林洪堡大学）；Radboud University Medical Center（拉德伯德大学医学中心）；Massachusetts General Hospital and Harvard Medical School（麻省总医院和哈佛医学院）；Technical University of Munich（慕尼黑技术大学）
Adipose Tissue Segmentation in Unlabeled Abdomen MRI using Cross Modality Domain Adaptation
University of Central Florida（中佛罗里达大学）；National Cancer Institute, National Institutes of Health（美国国立卫生研究院国家癌症研究所）；Frederick National Laboratory for Cancer Research（弗雷德里克国家癌症研究实验室）
Performance Deterioration of Deep Learning Models after Clinical Deployment: A Case Study with Auto-segmentation for Definitive Prostate Cancer Radiotherapy
University of Texas Southwestern Medical Center（德克萨斯大学西南医学中心）；Southern Methodist University（南卫理公会大学）；University of Texas at Arlington（德克萨斯大学阿灵顿分校）
An Unpaired Cross-modality Segmentation Framework Using Data Augmentation and Hybrid Convolutional Networks for Segmenting Vestibular Schwannoma and Cochlea
Huazhong University of Science and Technology（华中科技大学）；Kennesaw State University（肯尼索州立大学）
M2Diff: Multi-Modality Multi-Task Enhanced Diffusion Model for MRI-Guided Low-Dose PET Enhancement
Monash University（莫纳什大学）；Monash Biomedical Imaging（莫纳什生物医学成像）；School of Public Health and Preventive Medicine, Monash University（莫纳什大学公共卫生与预防医学院）；Alfred Health（阿尔弗雷德健康中心）
Repeatability of Multiparametric Prostate MRI Radiomics Features
Brigham and Women's Hospital（布莱根妇女医院）；Harvard Medical School（哈佛医学院）；Netherlands Cancer Institute（荷兰癌症研究所）；Maastricht University（马斯特里赫特大学）；Massachusetts General Hospital（麻省总医院）；Isomics, Inc.（Isomics公司）；Dana-Farber Cancer Institute（丹娜-法伯癌症研究所）
DIGEST: Deeply supervIsed knowledGE tranSfer neTwork learning for brain tumor segmentation with incomplete multi-modal MRI scans
Shenzhen Institutes 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）；Peng Cheng Laboratory（鹏城实验室）；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）
Influence of High-Performance Image-to-Image Translation Networks on Clinical Visual Assessment and Outcome Prediction: Utilizing Ultrasound to MRI Translation in Prostate Cancer
