Unsupervised domain adaptation (UDA) for cross-modality medical image segmentation has shown great progress by domain-invariant feature learning or image appearance translation. Adapted feature learning usually cannot detect domain shifts at the pixel level and is not able to achieve good results in dense semantic segmentation tasks. Image appearance translation, e.g. CycleGAN, translates images into different styles with good appearance, despite its population, its semantic consistency is hardly to maintain and results in poor cross-modality segmentation. In this paper, we propose intra- and cross-modality semantic consistency (ICMSC) for UDA and our key insight is that the segmentation of synthesised images in different styles should be consistent. Specifically, our model consists of an image translation module and a domain-specific segmentation module. The image translation module is a standard CycleGAN, while the segmentation module contains two domain-specific segmentation networks. The intra-modality semantic consistency (IMSC) forces the reconstructed image after a cycle to be segmented in the same way as the original input image, while the cross-modality semantic consistency (CMSC) encourages the synthesized images after translation to be segmented exactly the same as before translation. Comprehensive experimental results on cross-modality hip joint bone segmentation show the effectiveness of our proposed method, which achieves an average DICE of 81.61% on the acetabulum and 88.16% on the proximal femur, outperforming other state-of-the-art methods. It is worth to note that without UDA, a model trained on CT for hip joint bone segmentation is non-transferable to MRI and has almost zero-DICE segmentation.
We present a method to generate subject-specific cartilage for the hip joint. Given bone geometry, our approach is agnostic to image modality, creates conforming interfaces, and is well suited for finite element analysis. We demonstrate our method on ten hip joints showing anatomical shape consistency and well-behaved stress patterns. Our method is fast and may assist in large-scale biomechanical population studies of the hip joint when manual segmentation or training data is not feasible.
George Mason University（乔治梅森大学）；Beijing Electronic Science and Technology Institute（北京电子科技学院）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；Beijing Jishuitan Hospital（北京积水潭医院）
Purpose: Pelvic bone segmentation in CT has always been an essential step in clinical diagnosis and surgery planning of pelvic bone diseases. Existing methods for pelvic bone segmentation are either hand-crafted or semi-automatic and achieve limited accuracy when dealing with image appearance variations due to the multi-site domain shift, the presence of contrasted vessels, coprolith and chyme, bone fractures, low dose, metal artifacts, etc. Due to the lack of a large-scale pelvic CT dataset with annotations, deep learning methods are not fully explored. Methods: In this paper, we aim to bridg…
PAII Inc.（PAII公司）；University of Maryland（马里兰大学）；Chang Gung Memorial Hospital（长庚纪念医院）；Ping An Technology（平安科技）
Bone mineral density (BMD) is a clinically critical indicator of osteoporosis, usually measured by dual-energy X-ray absorptiometry (DEXA). Due to the limited accessibility of DEXA machines and examinations, osteoporosis is often under-diagnosed and under-treated, leading to increased fragility fracture risks. Thus it is highly desirable to obtain BMDs with alternative cost-effective and more accessible medical imaging examinations such as X-ray plain films. In this work, we formulate the BMD estimation from plain hip X-ray images as a regression problem. Specifically, we propose a new semi-su…
University Hospital of Saint-Etienne（圣艾蒂安大学医院）；Hospices Civils de Lyon（里昂民用医院）；CREATIS（CREATIS研究所）；INSA-Lyon（里昂国立应用科学学院）；University of Strasbourg（斯特拉斯堡大学）
By highlighting the regions of the input image that contribute the most to the decision, saliency maps have become a popular method to make neural networks interpretable. In medical imaging, they are particularly well-suited to explain neural networks in the context of abnormality localization. However, from our experiments, they are less suited to classification problems where the features that allow to distinguish between the different classes are spatially correlated, scattered and definitely non-trivial. In this paper we thus propose a new paradigm for better interpretability. To this end …
A Dataset for Deep Learning-based Bone Structure Analyses in Total Hip Arthroplasty
University of Science and Technology of China（中国科学技术大学）；The First Affiliated Hospital of the University of Science and Technology of China（中国科学技术大学附属第一医院）
The University of Iceland（冰岛大学）；ETH Zurich（苏黎世联邦理工学院）；The University of Calgary（卡尔加里大学）；McCaig Institute for Bone and Joint Health（麦凯格骨关节健康研究所）；The Icelandic Heart Association（冰岛心脏协会）；The Johns Hopkins University（约翰斯·霍普金斯大学）
Osteoporosis is a common bone disease that increases the risk of bone fracture. Hip-fracture risk screening methods based on finite element analysis depend on segmented computed tomography (CT) images; however, current femur segmentation methods require manual delineations of large data sets. Here we propose a deep neural network for fully automated, accurate, and fast segmentation of the proximal femur from CT. Evaluation on a set of 1147 proximal femurs with ground truth segmentations demonstrates that our method is apt for hip-fracture risk screening, bringing us one step closer to a clinic…
Validation of musculoskeletal segmentation model with uncertainty estimation for bone and muscle assessment in hip-to-knee clinical CT images
Nara Institute of Science and Technology（奈良先端科学技术大学院大学）；Osaka University（大阪大学）；Keio University School of Medicine（庆应义塾大学医学部）；Hitachi Ltd.（日立制作所）；Ehime University（爱媛大学）
Unsupervised Machine Learning for Osteoporosis Diagnosis Using Singh Index Clustering on Hip Radiographs
Department of Computing Technologies, School of Computing, SRM Institute of Science and Technology（计算机技术系，计算学院，SRM科学与技术学院）；Department of Communication Technology and Network, Faculty of Computer Science and Information Technology, Universiti Putra Malaysia（通信技术与网络系，计算机科学与信息技术学院，马来西亚国立大学）；Department of Orthopaedics, Sri Ramachandra Medical College（骨科系，Sri Ramachandra医学院）
NeuralBoneReg: An Instance-Specific Label-Free Point Cloud-Based Method for Multi-Modal Bone Surface Registration
Research in Orthopedic Computer Science, Balgrist University Hospital, University of Zurich（骨科计算机科学研究所，巴尔格里斯大学医院，苏黎世大学）；AI Center, ETH Zurich（人工智能中心，苏黎世联邦理工学院）
Concordia University（康考迪亚大学）；University of Southern California（南加州大学）
Accurate segmentation of the pelvic CTs is crucial for the clinical diagnosis of pelvic bone diseases and for planning patient-specific hip surgeries. With the emergence and advancements of deep learning for digital healthcare, several methodologies have been proposed for such segmentation tasks. But in a low data scenario, the lack of abundant data needed to train a Deep Neural Network is a significant bottle-neck. In this work, we propose a methodology based on modulation of image tonal distributions and deep learning to boost the performance of networks trained on limited data. The strategy…
The University of Iceland（冰岛大学）；ETH Zürich（苏黎世联邦理工学院）；The Icelandic Heart Association（冰岛心脏协会）；The Johns Hopkins University（约翰斯·霍普金斯大学）
Osteoporosis is a common bone disease that occurs when the creation of new bone does not keep up with the loss of old bone, resulting in increased fracture risk. Adults over the age of 50 are especially at risk and see their quality of life diminished because of limited mobility, which can lead to isolation and depression. We are developing a robust screening method capable of identifying individuals predisposed to hip fracture to address this clinical challenge. The method uses finite element analysis and relies on segmented computed tomography (CT) images of the hip. Presently, the segmentat…
XAttn-BMD: Multimodal Deep Learning with Cross-Attention for Femoral Neck Bone Mineral Density Estimation
School of Electronics and Computer Science, University of Southampton, UK（电子与计算机科学学院，索姆塞特大学，英国）；MRC Lifecourse Epidemiology Centre, University of Southampton, Southampton General Hospital, UK（生命课程流行病学研究中心，索姆塞特大学，南安普顿总医院，英国）；NIHR Southampton Biomedical Research Centre, University of Southampton（南安普顿生物医学研究中心，索姆塞特大学）；University Hospital NHS Foundation Trust, Southampton, UK（南安普顿国家健康服务基金会信托，英国）
Measurements Automatically Extracted from Zero Echo Time MRI Using Deep Learning Image Segmentation and Geometric Modeling Agree with Expert Manual Readings
Nara Institute of Science and Technology（奈良先端科学技术大学院大学）；Osaka University（大阪大学）
In total hip arthroplasty, analysis of postoperative medical images is important to evaluate surgical outcome. Since Computed Tomography (CT) is most prevalent modality in orthopedic surgery, we aimed at the analysis of CT image. In this work, we focus on the metal artifact in postoperative CT caused by the metallic implant, which reduces the accuracy of segmentation especially in the vicinity of the implant. Our goal was to develop an automated segmentation method of the bones and muscles in the postoperative CT images. We propose a method that combines Normalized Metal Artifact Reduction (NM…
University of Queensland（昆士兰大学）；Australian e-Health Research Centre, CSIRO（澳大利亚联邦科学与工业研究组织澳大利亚电子健康研究中心）
Data scarcity is common in deep learning models for medical image segmentation. Previous works proposed multi-dataset learning, either simultaneously or via transfer learning to expand training sets. However, medical image datasets have diverse-sized images and features, and developing a model simultaneously for multiple datasets is challenging. This work proposes Fabric Image Representation Encoding Network (FIRENet), a universal architecture for simultaneous multi-dataset segmentation and transfer learning involving arbitrary numbers of dataset(s). To handle different-sized image and feature…
Automatic CT segmentation of proximal femur is crucial for the diagnosis and risk stratification of orthopedic diseases; however, current methods for the femur CT segmentation mainly rely on manual interactive segmentation, which is time-consuming and has limitations in both accuracy and reproducibility. In this study, we proposed an approach based on deep learning for the automatic extraction of the periosteal and endosteal contours of proximal femur in order to differentiate cortical and trabecular bone compartments. A three-dimensional (3D) end-to-end fully convolutional neural network, whi…
University of Westminster（威斯敏斯特大学）；Calico Life Sciences LLC（Calico生命科学有限责任公司）
An end-to-end image analysis pipeline is presented for the abdominal MRI protocol used in the UK Biobank on the first 38,971 participants. Emphasis is on the processing steps necessary to ensure a high-level of data quality and consistency is produced in order to prepare the datasets for downstream quantitative analysis, such as segmentation and parameter estimation. Quality control procedures have been incorporated to detect and, where possible, correct issues in the raw data. Detection of fat-water swaps in the Dixon series is performed by a deep learning model and corrected automatically. B…
PF-DAformer: Proximal Femur Segmentation via Domain Adaptive Transformer for Dual-Center QCT
Michigan Technological University（密歇根理工大学）；Kennesaw State University（肯尼索州立大学）；University of California, Irvine（加利福尼亚大学尔湾分校）；Tulane University School of Public Health and Tropical Medicine（杜兰大学公共卫生与热带医学院）
Michigan Technological University（密歇根理工大学）；University of California, Irvine（加州大学欧文分校）；Mayo Clinic（梅奥诊所）；Tulane University（杜兰大学）；University of Southern Mississippi（南密西西比大学）
Purpose: Proximal femur image analyses based on quantitative computed tomography (QCT) provide a method to quantify the bone density and evaluate osteoporosis and risk of fracture. We aim to develop a deep-learning-based method for automatic proximal femur segmentation. Methods and Materials: We developed a 3D image segmentation method based on V-Net, an end-to-end fully convolutional neural network (CNN), to extract the proximal femur QCT images automatically. The proposed V-net methodology adopts a compound loss function, which includes a Dice loss and a L2 regularizer. We performed experime…
