Deep Learning to Segment Pelvic Bones: Large-scale CT Datasets and Baseline Models
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）
Pengbo Liu, Hu Han, Yuanqi Du, Heqin Zhu, Yinhao Li, Feng Gu, Honghu Xiao, Jun Li, Chunpeng Zhao, Li Xiao, Xinbao Wu, S. Kevin Zhou
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 bridge the data gap by curating a large pelvic CT dataset pooled from multiple sources and different manufacturers, including 1, 184 CT volumes and over 320, 000 slices with different resolutions and a variety of the above-mentioned appearance variations. Then we propose for the first time, to the best of our knowledge, to learn a deep multi-class network for segmenting lumbar spine, sacrum, left hip, and right hip, from multiple-domain images simultaneously to obtain more effective and robust feature representations. Finally, we introduce a post-processing tool based on the signed distance function (SDF) to eliminate false predictions while retaining correctly predicted bone fragments. Results: Extensive experiments on our dataset demonstrate the effectiveness of our automatic method, achieving an average Dice of 0.987 for a metal-free volume. SDF post-processor yields a decrease of 10.5% in hausdorff distance by maintaining important bone fragments in post-processing phase. Conclusion: We believe this large-scale dataset will promote the development of the whole community and plan to open source the images, annotations, codes, and trained baseline models at https://github.com/ICT-MIRACLE-lab/CTPelvic1K.
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.
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）
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.
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…
UniFLM: United Segmentation and Measurement on Fetal Limb Ultrasonic Image
Academy of Advanced Interdisciplinary Studies, Wuhan University（武汉大学高等研究院）；School of Computer Science, Wuhan University（武汉大学计算机学院）；Guangzhou Women and Children’s Medical Center（广州市妇女儿童医疗中心）；Institute of Artificial Intelligence, School of Computer Science, Wuhan University（武汉大学计算机学院人工智能研究所）
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）
Purpose: The localisation and segmentation of individual bones is an important preprocessing step in many planning and navigation applications. It is, however, a time-consuming and repetitive task if done manually. This is true not only for clinical practice but also for the acquisition of training data. We therefore not only present an end-to-end learnt algorithm that is capable of segmenting 125 distinct bones in an upper-body CT, but also provide an ensemble-based uncertainty measure that helps to single out scans to enlarge the training dataset with. Methods We create fully automated end-t…
University of Bern（伯尔尼大学）；Inselspital（Inselspital医院（伯尔尼大学医院））；University Hospital of Fribourg（弗里堡大学医院）
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 …
Multi-needle Localization for Pelvic Seed Implant Brachytherapy based on Tip-handle Detection and Matching
Image Processing Center, Beihang University（北航图像处理中心）；Department of Radiation Oncology, Peking University Third Hospital（北京大学第三医院放疗科）；Department of Radiation Oncology, Duke University Medical Center（达勒姆大学医学中心放疗科）
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…
Peking University（北京大学）；Indiana University Bloomington（印第安纳大学伯明顿分校）；Peking Union Medical College Hospital（北京协和医院）
One of the time-consuming routine work for a radiologist is to discern anatomical structures from tomographic images. For assisting radiologists, this paper develops an automatic segmentation method for pelvic magnetic resonance (MR) images. The task has three major challenges 1) A pelvic organ can have various sizes and shapes depending on the axial image, which requires local contexts to segment correctly. 2) Different organs often have quite similar appearance in MR images, which requires global context to segment. 3) The number of available annotated images are very small to use the latest…
Cornell University（康奈尔大学）；CuraCloud Corporation（库拉克劳德公司）；UNC Charlotte（北卡罗来纳大学夏洛特分校）；University at Albany, State University at New York（纽约州立大学奥尔巴尼分校）
Computerized automatic methods have been employed to boost the productivity as well as objectiveness of hand bone age assessment. These approaches make predictions according to the whole X-ray images, which include other objects that may introduce distractions. Instead, our framework is inspired by the clinical workflow (Tanner-Whitehouse) of hand bone age assessment, which focuses on the key components of the hand. The proposed framework is composed of two components: a Mask R-CNN subnet of pixelwise hand segmentation and a residual attention network for hand bone age assessment. The Mask R-C…
Rapid Bone Scintigraphy Enhancement via Semantic Prior Distillation from Segment Anything Model
Department of Nuclear Medicine, Shanghai Children’s Hospital, School of Medicine, Shanghai Jiao Tong University（核医学科，上海儿童医学中心，上海交通大学医学院）；School of Microelectronics, Shanghai University（微电子学院，上海大学）；Department of Ophthalmology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine（眼科，瑞金医院，上海交通大学医学院）；Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology（电子与计算机工程系，香港科学与技术大学）；School of Software Engineering, Sun Yat-sen University（软件工程学院，中山大学）；College of Computer Science and Electronic Engineering, Hunan University（计算机科学与电子工程学院，湖南大学）；School of Computer Science and Technology, Henan Polytechnic University（计算机科学与技术学院，河南理工大学）
本文首次将 SAM 语义先验引入医学图像恢复，通过级联网络和三个语义模块增强儿科快速骨闪烁显像，并构建首个专用数据集 RBS，在多项指标上优于现有方法。
Semi-Supervised Bone Marrow Lesion Detection from Knee MRI Segmentation Using Mask Inpainting Models
Gordon Center for Medical Imaging, Harvard Medical School and Massachusetts General Hospital（哈佛医学院与麻省总医院戈登医学成像中心）；Boston University（波士顿大学）；Seidenberg School of CSIS, Pace University（佩斯大学塞登伯格计算机科学与信息系统学院）；Ewha Womans University（梨花女子大学）
GUMP-Net: An interpretable model-data-driven intelligent algorithm for multi-class pelvic segmentation
State Key Laboratory of Mathematical Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences（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）；University of Chinese Academy of Sciences（中国科学院大学）；Department of Orthopedics, The Fourth Medical Center of Chinese PLA General Hospital（中国人民解放军第四医学中心骨科部）；National Clinical Research Center for Orthopedics, Sports Medicine and Rehabilitation（骨科、运动医学与康复临床研究中心）；Department of Trauma and Orthopedics, People’s Hospital Peking University（北京大学人民医院创伤与骨科部）；Department of Orthopedics and Traumatology, Beijing Jishuitan Hospital, Capital Medical University（首都医科大学北京积水潭医院骨科与创伤科）
Nara Institute of Science and Technology（奈良科学技术研究所）；Osaka University Graduate School of Medicine（大阪大学医学部研究生院）
Purpose: To apply a convolutional neural network (CNN) to develop a system that segments intensity calibration phantom regions in computed tomography (CT) images, and to test the system in a large cohort to evaluate its robustness. Methods: A total of 1040 cases (520 cases each from two institutions), in which an intensity calibration phantom (B-MAS200, Kyoto Kagaku, Kyoto, Japan) was used, were included herein. A training dataset was created by manually segmenting the regions of the phantom for 40 cases (20 cases each). Segmentation accuracy of the CNN model was assessed with the Dice coeffic…
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…
University College London（伦敦大学学院）；University Hospitals Leuven（鲁汶大学医院）；KU Leuven（鲁汶大学）；Sydney Medical School Nepean（悉尼大学尼平医学院）；Nepean Hospital（尼平医院）
Segmentation of the levator hiatus in ultrasound allows to extract biometrics which are of importance for pelvic floor disorder assessment. In this work, we present a fully automatic method using a convolutional neural network (CNN) to outline the levator hiatus in a 2D image extracted from a 3D ultrasound volume. In particular, our method uses a recently developed scaled exponential linear unit (SELU) as a nonlinear self-normalising activation function, which for the first time has been applied in medical imaging with CNN. SELU has important advantages such as being parameter-free and mini-ba…
Pelvic floor MRI segmentation based on semi-supervised deep learning
Institute of Medical Technology, Peking University Health Science Center, Peking University（北京大学医学部医学技术研究院，北京大学）；Biomedical Engineering Department, Peking University（北京大学生物医学工程系）；University of Michigan-Shanghai Jiao Tong University Joint Institute, Shanghai Jiao Tong University（上海交通大学密西根学院）；School of Electrical and Electronic Engineering, Nanyang Technological University（南洋理工大学电子与电气工程学院）；Department of Mechanical Engineering, University of Michigan（密歇根大学机械工程系）；Department of Obstetrics and Gynecology, University of Michigan（密歇根大学妇产科）
Visualization of Organ Movements Using Automatic Region Segmentation of Swallowing CT
Toho University（东邦大学）；Michiwaki Kenkyusho, Co., Ltd.（道脇研究所有限公司）；Meiji Co., Ltd.（明治公司）；Shibaura Institute of Technology（芝浦工业大学）；Fujita Health University（藤田保健卫生大学）；Ohara General Hospital（大原综合医院）；Tsurumi University School of Dental Medicine（鹤见大学牙学院）；Nara Institute of Science and Technology（奈良科学技术研究所）
Yonsei University（延世大学）；National Institute for Mathematical Sciences（国家数学科学研究所）
Low-dose dental cone beam computed tomography (CBCT) has been increasingly used for maxillofacial modeling. However, the presence of metallic inserts, such as implants, crowns, and dental filling, causes severe streaking and shading artifacts in a CBCT image and loss of the morphological structures of the teeth, which consequently prevents accurate segmentation of bones. A two-stage metal artifact reduction method is proposed for accurate 3D low-dose maxillofacial CBCT modeling, where a key idea is to utilize explicit tooth shape prior information from intra-oral scan data whose acquisition do…
Accurate segmentation of prostate and surrounding organs at risk is important for prostate cancer radiotherapy treatment planning. We present a fully automated workflow for male pelvic CT image segmentation using deep learning. The architecture consists of a 2D localization network followed by a 3D segmentation network for volumetric segmentation of prostate, bladder, rectum, and femoral heads. We used a multi-channel 2D U-Net followed by a 3D U-Net with encoding arm modified with aggregated residual networks, known as ResNeXt. The models were trained and tested on a pelvic CT image dataset co…
PDC-Net: Pattern Divide-and-Conquer Network for Pelvic Radiation Injury Segmentation
School of Computer Science and Engineering, Sun Yat-sen University（中山大学计算机科学与工程学院）；Department of Radiology, The Sixth Affiliated Hospital, Sun Yat-sen University（中山大学第六附属医院放射科）；School of Computer and Information Engineering, Jiangxi Normal University（江西师范大学计算机与信息工程学院）；Department of General Surgery, The Sixth Affiliated Hospital, Sun Yat-sen University（中山大学第六附属医院普外科）；Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University（广东省直肠及盆底疾病重点实验室，生物医学创新中心，中山大学第六附属医院）
University of Twente（特文特大学）；Jönköping University（延雪平大学）
The prevalance of pelvic floor problems is high within the female population. Transperineal ultrasound (TPUS) is the main imaging modality used to investigate these problems. Automating the analysis of TPUS data will help in growing our understanding of pelvic floor related problems. In this study we present a U-net like neural network with some convolutional long short term memory (CLSTM) layers to automate the 3D segmentation of the levator ani muscle (LAM) in TPUS volumes. The CLSTM layers are added to preserve the inter-slice 3D information. We reach human level performance on this segment…
Automated segmentation can assist radiotherapy treatment planning by saving manual contouring efforts and reducing intra-observer and inter-observer variations. The recent development of deep learning approaches has revoluted medical data processing, including semantic segmentation, by dramatically improving performance. However, training effective deep learning models usually require a large amount of high-quality labeled data, which are often costly to collect. We developed a novel semi-supervised adversarial deep learning approach for 3D pelvic CT image semantic segmentation. Unlike supervi…
Various imaging artifacts, low signal-to-noise ratio, and bone surfaces appearing several millimeters in thickness have hindered the success of ultrasound (US) guided computer assisted orthopedic surgery procedures. In this work, a multi-feature guided convolutional neural network (CNN) architecture is proposed for simultaneous enhancement, segmentation, and classification of bone surfaces from US data. The proposed CNN consists of two main parts: a pre-enhancing net, that takes the concatenation of B-mode US scan and three filtered image features for the enhancement of bone surfaces, and a mo…
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…
Deutsches Krebsforschungszentrum (DKFZ)（德国癌症研究中心）；University of Heidelberg（海德堡大学）
Bone segmentation from CT images is a task that has been worked on for decades. It is an important ingredient to several diagnostics or treatment planning approaches and relevant to various diseases. As high-quality manual and semi-automatic bone segmentation is very time-consuming, a reliable and fully automatic approach would be of great interest in many scenarios. In this publication, we propose a UNet inspired architecture to address the task using Deep Learning. We evaluated the approach on whole-body CT scans of patients suffering from multiple myeloma. As the disease decomposes the bone…
Washington University（华盛顿大学）；Washington University School of Medicine（华盛顿大学医学院）
Purpose: The research is to develop a novel CNN-based adversarial deep learning method to improve and expedite the multi-organ semantic segmentation of CT images, and to generate accurate contours on pelvic CT images. Methods: Planning CT and structure datasets for 120 patients with intact prostate cancer were retrospectively selected and divided for 10-fold cross-validation. The proposed adversarial multi-residual multi-scale pooling Markov Random Field (MRF) enhanced network (ARPM-net) implements an adversarial training scheme. A segmentation network and a discriminator network were trained …
The Johns Hopkins University（约翰斯·霍普金斯大学）；University of Maryland（马里兰大学）；R. Adams Cowley Shock Trauma Center（R·亚当斯·考利休克创伤中心）
Trauma is the worldwide leading cause of death and disability in those younger than 45 years, and pelvic fractures are a major source of morbidity and mortality. Automated segmentation of multiple foci of arterial bleeding from abdominopelvic trauma CT could provide rapid objective measurements of the total extent of active bleeding, potentially augmenting outcome prediction at the point of care, while improving patient triage, allocation of appropriate resources, and time to definitive intervention. In spite of the importance of active bleeding in the quick tempo of trauma care, the task is s…
Statistical methods such as sequential Monte Carlo Methods were proposed for detection, segmentation and tracking of objects in digital images. A similar approach, called Shape Particle Filters was introduced for the segmentation of vertebra, lungs and hearts [1]. In this contribution, a global shape and a local appearance model are derived from specific object annotated X-ray images of the metacarpal bones. In the test data a unique labeling of the bone boundary and the background points and a manual annotation is given. Using a set of local features (Haar-like) in the neighborhood of each pi…
National University of Singapore（新加坡国立大学）；Institute for Infocomm Research, A*STAR（新加坡科技研究局资讯通信研究院）；Huazhong University of Science and Technology（华中科技大学）；YITU Tech（依图科技）
Segmentation stands at the forefront of many high-level vision tasks. In this study, we focus on segmenting finger bones within a newly introduced semi-supervised self-taught deep learning framework which consists of a student network and a stand-alone teacher module. The whole system is boosted in a life-long learning manner wherein each step the teacher module provides a refinement for the student network to learn with newly unlabeled data. Experimental results demonstrate the superiority of the proposed method over conventional supervised deep learning methods.
Spinal Pain Research Institute of Tongji University（同济大学脊柱疼痛研究所）；Brigham and Women's Hospital, Harvard Medical School（哈佛医学院布莱根妇女医院）；School of Data and Computer Science, Sun Yat-sen University（中山大学数据科学与计算机学院）；Shanghai Jiao Tong University School of Medicine（上海交通大学医学院）；Graduate School of Frontier Sciences, The University of Tokyo（东京大学前沿科学研究生院）；Burke Neurological Institute（伯克神经学研究所）
Background and objective: Combined evaluation of lumbosacral structures (e.g. nerves, bone) on multimodal radiographic images is routinely conducted prior to spinal surgery and interventional procedures. Generally, magnetic resonance imaging is conducted to differentiate nerves, while computed tomography (CT) is used to observe bony structures. The aim of this study is to investigate the feasibility of automatically segmenting lumbosacral structures (e.g. nerves & bone) on non-contrast CT with deep learning. Methods: a total of 50 cases with spinal CT were manually labeled for lumbosacral nerv…
University of Oxford（牛津大学）；University College London（伦敦大学学院）；InstaDeep（InstaDeep公司）；City University of Hong Kong（香港城市大学）；Radboud University Nijmegen Medical Centre（拉德堡德大学奈梅亨医学中心）
The ability to adapt medical image segmentation networks for a novel class such as an unseen anatomical or pathological structure, when only a few labelled examples of this class are available from local healthcare providers, is sought-after. This potentially addresses two widely recognised limitations in deploying modern deep learning models to clinical practice, expertise-and-labour-intensive labelling and cross-institution generalisation. This work presents the first 3D few-shot interclass segmentation network for medical images, using a labelled multi-institution dataset from prostate canc…
In this paper we present a new 3D segmentation approach for the vertebrae of the lower thoracic and the lumbar spine in spiral computed tomography datasets. We implemented a multi-step procedure. Its main components are deformable models, volume growing, and morphological operations. The performance analysis that included an evaluation of accuracy using the European Spine Phantom, and of intra-operator precision using clinical CT datasets from 10 patients highlight the potential for clinical use. The intra-operator precision of the segmentation procedure was better than 1% for Bone Mineral Den…
Prototypical few-shot segmentation for cross-institution male pelvic structures with spatial registration
University of Oxford（牛津大学）；University College London（伦敦大学学院）；InstaDeep Ltd.（InstaDeep有限公司）；UCL Centre for Medical Image Computing（伦敦大学学院医学影像计算中心）；Department of Engineering Science, University of Oxford（牛津大学工程科学系）；Department of Medical Physics and Biomedical Engineering, UCL（伦敦大学学院医疗物理与生物医学工程系）；Wellcome / EPSRC Centre for Interventional and Surgical Sciences（惠康/英国工程与物理科学研究委员会介入与外科科学中心）；Active Vision Laboratory, Department of Engineering Science, University of Oxford（牛津大学工程科学系主动视觉实验室）
Johns Hopkins University（约翰斯·霍普金斯大学）；Johns Hopkins Medical Institutes（约翰斯·霍普金斯医学院）；Konkuk University Medical Center（建国大学医疗中心）；Konkuk University School of Medicine（建国大学医学院）
Quantitative bone single-photon emission computed tomography (QBSPECT) has the potential to provide a better quantitative assessment of bone metastasis than planar bone scintigraphy due to its ability to better quantify activity in overlapping structures. An important element of assessing response of bone metastasis is accurate image segmentation. However, limited by the properties of QBSPECT images, the segmentation of anatomical regions-of-interests (ROIs) still relies heavily on the manual delineation by experts. This work proposes a fast and robust automated segmentation method for partiti…
Johns Hopkins University（约翰斯·霍普金斯大学）；University of British Columbia（不列颠哥伦比亚大学）
Segmenting both bone surface and the corresponding acoustic shadow are fundamental tasks in ultrasound (US) guided orthopedic procedures. However, these tasks are challenging due to minimal and blurred bone surface response in US images, cross-machine discrepancy, imaging artifacts, and low signal-to-noise ratio. Notably, bone shadows are caused by a significant acoustic impedance mismatch between the soft tissue and bone surfaces. To leverage this mutual information between these highly related tasks, we propose a single end-to-end network with a shared transformer-based encoder and task inde…
Huazhong University of Science and Technology（华中科技大学）
In the detection of myeloproliferative, the number of cells in each type of bone marrow cells (BMC) is an important parameter for the evaluation. In this study, we propose a new counting method, which also consists of three modules including localization, segmentation and classification. The localization of BMC is achieved from a color transformation enhanced BMC sample image and stepwise averaging method (SAM). In the nucleus segmentation, both SAM and Otsu's method will be applied to obtain a weighted threshold for segmenting the patch into nucleus and non-nucleus. In the cytoplasm segmentat…
X-ray image guidance enables percutaneous alternatives to complex procedures. Unfortunately, the indirect view onto the anatomy in addition to projective simplification substantially increase the task-load for the surgeon. Additional 3D information such as knowledge of anatomical landmarks can benefit surgical decision making in complicated scenarios. Automatic detection of these landmarks in transmission imaging is challenging since image-domain features characteristic to a certain landmark change substantially depending on the viewing direction. Consequently and to the best of our knowledge,…
Johns Hopkins University（约翰斯·霍普金斯大学）；University of British Columbia（不列颠哥伦比亚大学）
Due to imaging artifacts and low signal-to-noise ratio in ultrasound images, automatic bone surface segmentation networks often produce fragmented predictions that can hinder the success of ultrasound-guided computer-assisted surgical procedures. Existing pixel-wise predictions often fail to capture the accurate topology of bone tissues due to a lack of supervision to enforce connectivity. In this work, we propose an orientation-guided graph convolutional network to improve connectivity while segmenting the bone surface. We also propose an additional supervision on the orientation of the bone …
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…
In this work it is proposed a medical image segmentation pipeline for accurate bone segmentation from CT imaging. It is a two-step methodology, with a pre-segmentation step and a segmentation refinement step. First, the user performs a rough segmenting of the desired region of interest. Next, a fully automatic refinement step is applied to the pre-segmented data. The automatic segmentation refinement is composed by several sub-stpng, namely image deconvolution, image cropping and interpolation. The user-defined pre-segmentation is then refined over the deconvolved, cropped, and up-sampled vers…
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…
Concordia Institute for Information Systems Engineering（康考迪亚大学信息系统工程研究所）；Concordia University（康考迪亚大学）
Bone age is an important measure for assessing the skeletal and biological maturity of children. Delayed or increased bone age is a serious concern for pediatricians, and needs to be accurately assessed in a bid to determine whether bone maturity is occurring at a rate consistent with chronological age. In this paper, we introduce a unified deep learning framework for bone age assessment using instance segmentation and ridge regression. The proposed approach consists of two integrated stages. In the first stage, we employ an image annotation and segmentation model to annotate and segment the h…
Instituto de Biomecánica de València (IBV)（瓦伦西亚生物力学研究所）；Universitat Politècnica de València (UPV)（瓦伦西亚理工大学）
This preliminary study focuses on the development of a medical image segmentation algorithm based on artificial intelligence for calculating bone growth in contact with metallic implants. %as a result of the problem of estimating the growth of new bone tissue due to artifacts. %the presence of various types of distortions and errors, known as artifacts. Two databases consisting of computerized microtomography images have been used throughout this work: 100 images for training and 196 images for testing. Both bone and implant tissue were manually segmented in the training data set. The type of …
Contrastive uncertainty learning for robust multi-structure segmentation in lower-pelvic MR
UCL Hawkes Institute, University College London（伦敦大学学院霍克斯研究所）；Department of Medical Physics and Biomedical Engineering, University College London（伦敦大学学院医学物理与生物医学工程系）；Centre for Bioengineering, School of Engineering and Materials Science, Queen Mary University of London（伦敦大学玛丽女王学院工程与材料科学学院生物工程中心）；Digital Environment Research Institute, Queen Mary University of London（伦敦大学玛丽女王学院数字环境研究所）；Department of Computer Science, University College London（伦敦大学学院计算机科学系）
Nara Institute of Science and Technology（奈良先端科学技术大学院大学）；Osaka University Graduate School of Medicine（大阪大学大学院医学系研究科）；Ehime University Graduate School of Medicine（爱媛大学大学院医学系研究科）
We propose a method for estimating the bone mineral density (BMD) from a plain x-ray image. Dual-energy X-ray absorptiometry (DXA) and quantitative computed tomography (QCT) provide high accuracy in diagnosing osteoporosis; however, these modalities require special equipment and scan protocols. Measuring BMD from an x-ray image provides an opportunistic screening, which is potentially useful for early diagnosis. The previous methods that directly learn the relationship between x-ray images and BMD require a large training dataset to achieve high accuracy because of large intensity variations i…
