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 involves pre-processing of test data through histogram specification. This simple yet effective approach can be viewed as a style transfer methodology. The segmentation task uses a U-Net configuration with an EfficientNet-B0 backbone, optimized using an augmented BCE-IoU loss function. This configuration is validated on a total of 284 images taken from two publicly available CT datasets, TCIA (a cancer imaging archive) and the Visible Human Project. The average performance measures for the Dice coefficient and Intersection over Union are 95.7% and 91.9%, respectively, give strong evidence for the effectiveness of the approach, which is highly competitive with state-of-the-art methodologies.
THA-Flow Generative Model: Prosthesis Geometry Prediction from Preoperative CT
Changzhou Jinse Medical Information Technology Co., Ltd.（常州金色医疗信息技术有限公司）；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）
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）；Johns Hopkins University（约翰斯·霍普金斯大学）
CT is commonly used in orthopedic procedures. MRI is used along with CT to identify muscle structures and diagnose osteonecrosis due to its superior soft tissue contrast. However, MRI has poor contrast for bone structures. Clearly, it would be helpful if a corresponding CT were available, as bone boundaries are more clearly seen and CT has standardized (i.e., Hounsfield) units. Therefore, we aim at MR-to-CT synthesis. The CycleGAN was successfully applied to unpaired CT and MR images of the head, these images do not have as much variation of intensity pairs as do images in the pelvic region du…
School of Electrical & Computer Engineering, University of Tehran（德黑兰大学电气与计算机工程学院）；Graduate School of Information Science, Nara Institute of Science and Technology（奈良科学技术研究院信息科学研究生院）；Osaka University Graduate School of Medicine（大阪大学医学院研究生院）
Alignment of the bones in standing position provides useful information in surgical planning. In total hip arthroplasty (THA), pelvic sagittal inclination (PSI) angle in the standing position is an important factor in planning of cup alignment and has been estimated mainly from radiographs. Previous methods for PSI estimation used a patient-specific CT to create digitally reconstructed radiographs (DRRs) and compare them with the radiograph to estimate relative position between the pelvis and the x-ray detector. In this study, we developed a method that estimates PSI angle from a single antero…
McMaster University（麦克马斯特大学）；University of Waterloo（滑铁卢大学）；Juravinski Hospital and Cancer Centre（朱拉文斯基医院与癌症中心）
Bone marrow cytology is required to make a hematological diagnosis, influencing critical clinical decision points in hematology. However, bone marrow cytology is tedious, limited to experienced reference centers and associated with high inter-observer variability. This may lead to a delayed or incorrect diagnosis, leaving an unmet need for innovative supporting technologies. We have developed the first ever end-to-end deep learning-based technology for automated bone marrow cytology. Starting with a bone marrow aspirate digital whole slide image, our technology rapidly and automatically detect…
Bonnet: Ultra-fast whole-body bone segmentation from CT scans
ELLIS Institute Finland（埃利斯研究所芬兰分校）；Aalto University（艾尔沃斯大学）；Clemson University（克莱姆森大学）；ETH Zurich（苏黎世联邦理工学院）；Fujian Medical University Union Hospital（福建省医科大学附属医院）；Fujian Medical University（福建省医科大学）
Cervical cancer threatens the health of women seriously. Radiotherapy is one of the main therapy methods but with high risk of acute hematologic toxicity. Delineating the bone marrow (BM) for sparing using computer tomography (CT) images to plan before radiotherapy can effectively avoid this risk. Comparing with magnetic resonance (MR) images, CT lacks the ability to express the activity of BM. Thus, in current clinical practice, medical practitioners manually delineate the BM on CT images by corresponding to MR images. However, the time?consuming delineating BM by hand cannot guarantee the ac…
University of Tabriz（大不里士大学）；University of Tehran（德黑兰大学）；Tehran University of Medical Sciences（德黑兰医科大学）
Background and Objective: The Acetabular version, an essential factor in total hip arthroplasty, is measured by CT scan as the gold standard. The dose of radiation and expensiveness of CT make anterior-posterior pelvic radiograph an appropriate alternative procedure. In this study, we applied a deep learning approach on anteroposterior pelvic X-rays to measure anatomical version, eliminating the necessity of using Computed tomography scan. Methods: The right and left acetabular version angles of the hips of 300 patients are computed using their CT images. The proposed deep learning model, Atte…
Benchmark of Segmentation Techniques for Pelvic Fracture in CT and X-ray: Summary of the PENGWIN 2024 Challenge
Beijing Rossum Robot Technology Co., Ltd.（北京罗素机器人科技有限公司）；Key Laboratory of Biomechanics and Mechanobiology, Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University（生物力学与机械生物学重点实验室，教育部，北京生物医学创新中心，生物科学与医学工程学院，北航）；Department of Computer Science, Johns Hopkins University（计算机科学系，约翰霍普金斯大学）；Division of Medical Image Computing, German Cancer Research Center (DKFZ)（医学影像计算部，德国癌症研究中心（DKFZ））；Helmholtz Imaging, Heidelberg（海德堡大学医院影像中心）；Smart Medical Imaging, Learning and Engineering (SMILE) Lab, Medical UltraSound Image Computing（智能医学影像、学习与工程（SMILE）实验室，医学超声影像计算）
A Modified VGG19-Based Framework for Accurate and Interpretable Real-Time Bone Fracture Detection
East West University（东西大学）；Islamic University of Technology（伊斯兰技术大学）；Ahsanullah University of Science and Technology（阿赫萨努拉科技大学）；Bangladesh Agricultural University（孟加拉国农业大学）；Brac University（布拉克大学）
A major remaining challenge for magnetic resonance-based attenuation correction methods (MRAC) is their susceptibility to sources of MRI artifacts (e.g. implants, motion) and uncertainties due to the limitations of MRI contrast (e.g. accurate bone delineation and density, and separation of air/bone). We propose using a Bayesian deep convolutional neural network that, in addition to generating an initial pseudo-CT from MR data, also produces uncertainty estimates of the pseudo-CT to quantify the limitations of the MR data. These outputs are combined with MLAA reconstruction that uses the PET em…
University of Science and Technology of China（中国科学技术大学）；Guangzhou First People’s Hospital（广州市第一人民医院）；South China University of Technology（华南理工大学）
In the paper, we present an approach for learning a single model that universally segments 33 anatomical structures, including vertebrae, pelvic bones, and abdominal organs. Our model building has to address the following challenges. Firstly, while it is ideal to learn such a model from a large-scale, fully-annotated dataset, it is practically hard to curate such a dataset. Thus, we resort to learn from a union of multiple datasets, with each dataset containing the images that are partially labeled. Secondly, along the line of partial labelling, we contribute an open-source, large-scale verteb…
Partial Decoder Attention Network with Contour-weighted Loss Function for Data-Imbalance Medical Image Segmentation
Institute of Medical Technology, Peking University Health Science Center, Peking University, Beijing, China（北京大学医学部医学技术研究所）；National Institute of Health Data Science, Peking University, Beijing, China（北京大学国家健康数据科学研究院）；Department of Radiology, Peking University Third Hospital, Beijing, China（北京大学第三医院放射科）；RIKEN Center for Computational Science (R-CCS), Kobe, Japan（日本京都大学RIKEN计算科学中心）；National Institute of Health Data Science, Peking University, the Institute of Medical Technology, Peking University Health Science Center, and the Institute for Artificial Intelligence, Peking University, Beijing, China（北京大学国家健康数据科学研究院、北京大学医学部医学技术研究所以及北京大学人工智能研究所）
University of Technology Sydney（悉尼科技大学）；IBM Australia Limited（IBM澳大利亚有限公司）；University of New England（新英格兰大学）；King Abdulaziz University（阿卜杜勒阿齐兹国王大学）；Universiti Kebangsaan Malaysia（马来西亚国立大学）；Charles Sturt University（查尔斯特大学）；Westmead Hospital（韦斯特米德医院）；Singapore University of Social Sciences（新加坡社科大学）；Cogninet Australia（Cogninet澳大利亚）；University of Southern Queensland（南昆士兰大学）；Ngee Ann Polytechnic（义安理工学院）；Asia University（亚洲大学）
Lung cancer is the leading cause of cancer death worldwide and a good prognosis depends on early diagnosis. Unfortunately, screening programs for the early diagnosis of lung cancer are uncommon. This is in-part due to the at-risk groups being located in rural areas far from medical facilities. Reaching these populations would require a scaled approach that combines mobility, low cost, speed, accuracy, and privacy. We can resolve these issues by combining the chest X-ray imaging mode with a federated deep-learning approach, provided that the federated model is trained on homogenous data to ensu…
