We propose a 2D Encoder-Decoder based deep learning architecture for semantic segmentation, that incorporates anatomical priors by imitating the encoder component of an autoencoder in latent space. The autoencoder is additionally enhanced by means of hierarchical features, extracted by an U-Net module. Our suggested architecture is trained in an end-to-end manner and is evaluated on the example of pelvic bone segmentation in MRI. A comparison to the standard U-Net architecture shows promising improvements.
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…
Object detection methods are widely adopted for computer-aided diagnosis using medical images. Anomalous findings are usually treated as objects that are described by bounding boxes. Yet, many pathological findings, e.g., bone fractures, cannot be clearly defined by bounding boxes, owing to considerable instance, shape and boundary ambiguities. This makes bounding box annotations, and their associated losses, highly ill-suited. In this work, we propose a new bone fracture detection method for X-ray images, based on a labor effective and flexible annotation scheme suitable for abnormal findings…
Test-time Adaptation of Pelvic Bone Segmentation Models via Dynamic Reliability-Guided
College of Automation, Nanjing University of Posts and Telecommunications（南京邮电大学自动化学院）；Department of Electrical and Computer Engineering, National University of Singapore（新加坡国立大学电气与计算机工程系）；the Affiliated Stomatological Hospital of Nanjing Medical University（南京医科大学附属口腔医院）
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…
Johns Hopkins University（约翰斯·霍普金斯大学）；Johns Hopkins Hospital（约翰斯·霍普金斯医院）；Technische Universität München（慕尼黑工业大学）
In unilateral pelvic fracture reductions, surgeons attempt to reconstruct the bone fragments such that bilateral symmetry in the bony anatomy is restored. We propose to exploit this "structurally symmetric" nature of the pelvic bone, and provide intra-operative image augmentation to assist the surgeon in repairing dislocated fragments. The main challenge is to automatically estimate the desired plane of symmetry within the patient's pre-operative CT. We propose to estimate this plane using a non-linear optimization strategy, by minimizing Tukey's biweight robust estimator, relying on the parti…
University of Tehran（德黑兰大学）；Nara Institute of Science and Technology（奈良科学技术研究所）；Osaka University Graduate School of Medicine（大阪大学医学院）
With the increasing usage of radiograph images as a most common medical imaging system for diagnosis, treatment planning, and clinical studies, it is increasingly becoming a vital factor to use machine learning-based systems to provide reliable information for surgical pre-planning. Segmentation of pelvic bone in radiograph images is a critical preprocessing step for some applications such as automatic pose estimation and disease detection. However, the encoder-decoder style network known as U-Net has demonstrated limited results due to the challenging complexity of the pelvic shapes, especial…
Invisible Yet Detected: PelFANet with Attention-Guided Anatomical Fusion for Pelvic Fracture Diagnosis
Center for Computational & Data Sciences, Independent University, Bangladesh（孟加拉国独立大学计算与数据科学中心）；Department of Computer Science and Engineering, Independent University, Bangladesh（孟加拉国独立大学计算机科学与工程系）；Advanced Medical Engineering Research Institute, University of Hyogo, Japan（日本兵库大学先进医学工程研究所）；Graduate School of Engineering, University of Hyogo, Japan（日本兵库大学工学研究科）
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…
Measurements Automatically Extracted from Zero Echo Time MRI Using Deep Learning Image Segmentation and Geometric Modeling Agree with Expert Manual Readings
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）
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…
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）
Fracture interactive geodesic active contours for bone segmentation
State Key Laboratory of Mathematical Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China（数学科学国家重点实验室，数学与系统科学研究院，中国科学院，北京）；University of Chinese Academy of Sciences, Beijing 100190, China（中国科学院大学，北京）；Department of Orthopedics, The Fourth Medical Center of Chinese PLA General Hospital, Beijing 100048, China（中国人民解放军总医院第四医学中心骨科，北京）；National Clinical Research Center for Orthopedics, Sports Medicine and Rehabilitation, Beijing 100048, China（骨科、运动医学与康复国家临床医学研究中心，北京）；Department of Trauma and Orthopedics, People’s Hospital Peking University, Beijing 100044, China（北京大学人民医院创伤与骨科，北京）
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）
