A Self-ensembling Framework for Semi-supervised Knee Cartilage Defects Assessment with Dual-Consistency
Institute for Medical Imaging Technology, School of Biomedical Engineering, Shanghai Jiao Tong University（上海交通大学医学院生物医学工程学院医学影像技术研究所）；Shanghai United Imaging Intelligence Co., Ltd.（上海联影智能科技有限公司）；Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine（上海交通大学医学院附属同仁医院影像科）
Jiayu Huo, Liping Si, Xi Ouyang, Kai Xuan, Weiwu Yao, Zhong Xue, Qian Wang, Dinggang Shen, Lichi Zhang
备注：
accepted by International Workshop on PRedictive Intelligence In MEdicine, 2020
Knee osteoarthritis (OA) is one of the most common musculoskeletal disorders and requires early-stage diagnosis. Nowadays, the deep convolutional neural networks have achieved greatly in the computer-aided diagnosis field. However, the construction of the deep learning models usually requires great amounts of annotated data, which is generally high-cost. In this paper, we propose a novel approach for knee cartilage defects assessment, including severity classification and lesion localization. This can be treated as a subtask of knee OA diagnosis. Particularly, we design a self-ensembling framework, which is composed of a student network and a teacher network with the same structure. The student network learns from both labeled data and unlabeled data and the teacher network averages the student model weights through the training course. A novel attention loss function is developed to obtain accurate attention masks. With dual-consistency checking of the attention in the lesion classification and localization, the two networks can gradually optimize the attention distribution and improve the performance of each other, whereas the training relies on partially labeled data only and follows the semi-supervised manner. Experiments show that the proposed method can significantly improve the self-ensembling performance in both knee cartilage defects classification and localization, and also greatly reduce the needs of annotated data.
School of Biomedical Engineering, Shanghai Jiao Tong University（上海交通大学生物医学工程学院）；Shanghai United Imaging Intelligence Co., Ltd.（上海联影智能医疗科技有限公司）；Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine（上海交通大学医学院附属同仁医院影像科）；School of Biomedical Engineering, ShanghaiTech University（上海科技大学生物医学工程学院）
Knee osteoarthritis (OA) is the most common osteoarthritis and a leading cause of disability. Cartilage defects are regarded as major manifestations of knee OA, which are visible by magnetic resonance imaging (MRI). Thus early detection and assessment for knee cartilage defects are important for protecting patients from knee OA. In this way, many attempts have been made on knee cartilage defect assessment by applying convolutional neural networks (CNNs) to knee MRI. However, the physiologic characteristics of the cartilage may hinder such efforts: the cartilage is a thin curved layer, implying…
Harbin Institute of Technology（哈尔滨工业大学）；University of Western Ontario（西安大略大学）
The morphological changes in knee cartilage (especially femoral and tibial cartilages) are closely related to the progression of knee osteoarthritis, which is expressed by magnetic resonance (MR) images and assessed on the cartilage segmentation results. Thus, it is necessary to propose an effective automatic cartilage segmentation model for longitudinal research on osteoarthritis. In this research, to relieve the problem of inaccurate discontinuous segmentation caused by the limited receptive field in convolutional neural networks, we proposed a novel position-prior clustering-based self-atte…
Analyzing knee cartilage thickness and strain under load can help to further the understanding of the effects of diseases like Osteoarthritis. A precise segmentation of the cartilage is a necessary prerequisite for this analysis. This segmentation task has mainly been addressed in Magnetic Resonance Imaging, and was rarely investigated on contrast-enhanced Computed Tomography, where contrast agent visualizes the border between femoral and tibial cartilage. To overcome the main drawback of manual segmentation, namely its high time investment, we propose to use a 3D Convolutional Neural Network …
Rutgers University（罗格斯大学）；SenseTime Research（商汤科技研究院）；New Jersey Medical School（新泽西医学院）
The 3D morphology and quantitative assessment of knee articular cartilages (i.e., femoral, tibial, and patellar cartilage) in magnetic resonance (MR) imaging is of great importance for knee radiographic osteoarthritis (OA) diagnostic decision making. However, effective and efficient delineation of all the knee articular cartilages in large-sized and high-resolution 3D MR knee data is still an open challenge. In this paper, we propose a novel framework to solve the MR knee cartilage segmentation task. The key contribution is the adversarial learning based collaborative multi-agent segmentation …
MonoUNet: A Robust Tiny Neural Network for Automated Knee Cartilage Segmentation on Point-of-Care Ultrasound Devices
School of Biomedical Engineering, The University of British Columbia（生物医学工程学院，不列颠哥伦比亚大学）；Department of Kinesiology, Michigan State University（运动科学系，密歇根州立大学）；Department of Rheumatology, The University of British Columbia（风湿病学系，不列颠哥伦比亚大学）；Department of Radiology, The University of British Columbia（放射学系，不列颠哥伦比亚大学）；Department of Medicine, The University of British Columbia（医学系，不列颠哥伦比亚大学）
New York University School of Medicine（纽约大学医学院）；New York University（纽约大学）
The integrity of articular cartilage is a crucial aspect in the early diagnosis of osteoarthritis (OA). Many novel MRI techniques have the potential to assess compositional changes of the cartilage extracellular matrix. Among these techniques, diffusion tensor imaging (DTI) of cartilage provides a simultaneous assessment of the two principal components of the solid matrix: collagen structure and proteoglycan concentration. DTI, as for any other compositional MRI technique, require a human expert to perform segmentation manually. The manual segmentation is error-prone and time-consuming ($\sim$…
The University of Queensland（昆士兰大学）；Australian e-Health Research Centre（澳大利亚电子健康研究中心）；Commonwealth Scientific and Industrial Research Organisation（联邦科学与工业研究组织）
In medical image analysis, automated segmentation of multi-component anatomical structures, which often have a spectrum of potential anomalies and pathologies, is a challenging task. In this work, we develop a multi-step approach using U-Net-based neural networks to initially detect anomalies (bone marrow lesions, bone cysts) in the distal femur, proximal tibia and patella from 3D magnetic resonance (MR) images of the knee in individuals with varying grades of osteoarthritis. Subsequently, the extracted data are used for downstream tasks involving semantic segmentation of individual bone and c…
Stanford University（斯坦福大学）；University of Central Florida（中佛罗里达大学）；Columbia University（哥伦比亚大学）；University of Pennsylvania（宾夕法尼亚大学）；University of Minnesota（明尼苏达大学）；University of Copenhagen（哥本哈根大学）；Cleveland Clinic（克利夫兰诊所）；New York University（纽约大学）；Langone Health（朗格尼健康中心）
Purpose: To organize a knee MRI segmentation challenge for characterizing the semantic and clinical efficacy of automatic segmentation methods relevant for monitoring osteoarthritis progression. Methods: A dataset partition consisting of 3D knee MRI from 88 subjects at two timepoints with ground-truth articular (femoral, tibial, patellar) cartilage and meniscus segmentations was standardized. Challenge submissions and a majority-vote ensemble were evaluated using Dice score, average symmetric surface distance, volumetric overlap error, and coefficient of variation on a hold-out test set. Simil…
Reliability assessment and multicenter clinical application of magnetic resonance methods for knee cartilage quantification
Academy for Clinical Innovation and Translation of Shanghai Co., Ltd. (ACITS)（上海临床创新与转化研究院有限公司）；Shanghai IxCell Biotechnology Co., LTD（上海爱科百发生物科技有限公司）；Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine（上海交通大学医学院附属第九人民医院）；Nanyang Technological University（南洋理工大学）；Renji Hospital, Shanghai Jiao Tong University School of Medicine（上海交通大学医学院附属仁济医院）；Shanghai Jiading District Central Hospital（上海市嘉定区中心医院）
Time-efficient combined morphologic and quantitative joint MRI based on clinical image contrasts -- An exploratory in-situ study of standardized cartilage defects
Precise segmentation of knee tissues from magnetic resonance imaging (MRI) is critical in quantitative imaging and diagnosis. Convolutional neural networks (CNNs), which are state of the art, have limitations owing to the lack of image-specific adaptation, such as low tissue contrasts and structural inhomogeneities, thereby leading to incomplete segmentation results. This paper presents a deep learning based automatic segmentation framework for knee tissue segmentation. A novel multipath CNN-based method is proposed, which consists of an encoder decoder-based segmentation network in combinatio…
ITMO University（圣彼得堡国立信息技术机械与光学大学）；Federal Almazov North-West Medical Research Center（阿尔马佐夫西北联邦医学研究中心）；Aix-Marseille Universite（艾克斯-马赛大学）；CNRS（法国国家科学研究中心）
Detection of cartilage loss is crucial for the diagnosis of osteo- and rheumatoid arthritis. A large number of automatic segmentation tools have been reported so far for cartilage assessment in magnetic resonance images of large joints. As compared to knee or hip, wrist cartilage has a more complex structure so that automatic tools developed for large joints are not expected to be operational for wrist cartilage segmentation. In that respect, a fully automatic wrist cartilage segmentation method would be of high clinical interest. We assessed the performance of four optimized variants of the U…
Utilizing 3D Fast Spin Echo Anatomical Imaging to Reduce the Number of Contrast Preparations in $T_{1ρ}$ Quantification of Knee Cartilage Using Learning-Based Methods
Schulich School of Engineering, University of Calgary（卡尔加里大学舒立克工程学院）；Department of Radiology & Diagnostic Imaging, University of Alberta（阿尔伯塔大学放射与诊断成像系）；Alberta Machine Intelligence Institute (AMII), University of Alberta（阿尔伯塔大学阿尔伯塔机器智能研究所（AMII））；Mechanical and Manufacturing Engineering, University of Calgary（卡尔加里大学机械与制造工程系）
Objective assessment of Magnetic Resonance Imaging (MRI) scans of osteoarthritis (OA) can address the limitation of the current OA assessment. Segmentation of bone, cartilage, and joint fluid is necessary for the OA objective assessment. Most of the proposed segmentation methods are not performing instance segmentation and suffer from class imbalance problems. This study deployed Mask R-CNN instance segmentation and improved it (improved-Mask R-CNN (iMaskRCNN)) to obtain a more accurate generalized segmentation for OA-associated tissues. Training and validation of the method were performed usi…
Imperial College London（帝国理工学院）；Hyperfine Research Inc.（Hyperfine研究公司）；University of Birmingham（伯明翰大学）；NIH Clinical Center（美国国立卫生研究院临床中心）
Purpose: To systematically investigate the influence of various data consistency layers, (semi-)supervised learning and ensembling strategies, defined in a $Σ$-net, for accelerated parallel MR image reconstruction using deep learning. Theory and Methods: MR image reconstruction is formulated as learned unrolled optimization scheme with a Down-Up network as regularization and varying data consistency layers. The different architectures are split into sensitivity networks, which rely on explicit coil sensitivity maps, and parallel coil networks, which learn the combination of coils implicitly. D…
