Bones are always wrapped by soft tissues. As a result, bones in their X-ray images are obscured and become unclear. In this paper, we tackle this problem and propose a novel task to virtually decompose the soft tissue and bone by image processing algorithms. This task is fundamentally different from segmentation because the decomposed images share the same imaging domain. Our decomposition task is also fundamentally different from the conventional image enhancement. We propose a new mathematical model for such decomposition. Our model is ill-posed and thus it requires some priors. With proper assumptions, our model can be solved by solving a standard Laplace equation. The resulting bone image is theoretically guaranteed to have better contrast than the original input image. Therefore, the details of bones get enhanced and become clearer. Several numerical experiments confirm the effective and efficiency of our method. Our approach is important for clinical diagnosis, surgery planning, recognition, deep learning, etc.
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 …
National Library of Medicine, National Institutes of Health（美国国立卫生研究院国家医学图书馆）
Chest X-ray (CXR) is a widely performed radiology examination that helps to detect abnormalities in the tissues and organs in the thoracic cavity. Detecting pulmonary abnormalities like COVID-19 may become difficult due to that they are obscured by the presence of bony structures like the ribs and the clavicles, thereby resulting in screening/diagnostic misinterpretations. Automated bone suppression methods would help suppress these bony structures and increase soft tissue visibility. In this study, we propose to build an ensemble of convolutional neural network models to suppress bones in fro…
As bone and air produce weak signals with conventional MR sequences, segmentation of these tissues particularly difficult in MRI. We propose to integrate patch-based anatomical signatures and an auto-context model into a machine learning framework to iteratively segment MRI into air, bone and soft tissue. The proposed semantic classification random forest (SCRF) method consists of a training stage and a segmentation stage. During training stage, patch-based anatomical features were extracted from registered MRI-CT training images, and the most informative features were identified to train a se…
National University of the South（南方国立大学）；National Scientific and Technical Research Council（国家科学技术研究委员会）；University of Jaén（哈恩大学）；University of Valladolid（巴利亚多利德大学）；Universitätsinstitut für Radiologie, Neuroradiologie und Nuklearmedizin（大学放射学、神经放射学与核医学研究所）
We propose a 3D neural network with specific loss functions for quantitative computed tomography (QCT) noise reduction to compute micro-structural parameters such as tissue mineral density (TMD) and bone volume ratio (BV/TV) with significantly higher accuracy than using no or standard noise reduction filters. The vertebra-phantom study contained high resolution peripheral and clinical CT scans with simulated in vivo CT noise and nine repetitions of three different tube currents (100, 250 and 360 mAs). Five-fold cross validation was performed on 20466 purely spongy pairs of noisy and ground-tru…
Segmentation of thigh tissues (muscle, fat, inter-muscular adipose tissue (IMAT), bone, and bone marrow) from magnetic resonance imaging (MRI) scans is useful for clinical and research investigations in various conditions such as aging, diabetes mellitus, obesity, metabolic syndrome, and their associated comorbidities. Towards a fully automated, robust, and precise quantification of thigh tissues, herein we designed a novel semi-supervised segmentation algorithm based on deep network architectures. Built upon Tiramisu segmentation engine, our proposed deep networks use variational and speciall…
BS-Diff: Effective Bone Suppression Using Conditional Diffusion Models from Chest X-Ray Images
Hangzhou Dianzi University（杭州电子科技大学）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Sanda University（上海杉达学院）；National Cancer Center, Chinese Academy of Medical Sciences（中国医学科学院国家癌症中心）；Peking Union Medical College（北京协和医学院）；Yunnan University（云南大学）；Shenzhen University（深圳大学）；Shenzhen Research Institute of Big Data（深圳大数据研究院）；Hangzhou Institute of Advanced Technology（杭州高等研究院）
BS-LDM: Effective Bone Suppression in High-Resolution Chest X-Ray Images with Conditional Latent Diffusion Models
Hangzhou Dianzi University（杭州电子科技大学）；National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital&Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College（中国医学科学院国家癌症中心/国家临床医学研究中心/癌症医院及深圳医院）；Central South University（中南大学）；Yunnan University（云南大学）；School of Biomedical Engineering, Shenzhen University（深圳大学生物医学工程学院）；Shenzhen Research Institute of Big Data（深圳大数据研究院）
A solution for co-locating 2D histology images in 3D for histology-to-CT and MR image registration: closing the loop for bone sarcoma treatment planning
Purpose: To demonstrate an ultrashort echo time magnetic resonance fingerprinting (UTE-MRF) method that can simultaneously quantify tissue relaxometries for muscle and bone in musculoskeletal systems and tissue components in brain and therefore can synthesize pseudo-CT images. Methods: A FISP-MRF sequence with half pulse excitation and half spoke radial acquisition was designed to sample fast T2 decay signals. Sinusoidal echo time (TE) pattern was applied to enhance MRF sensitivity for tissues with short and ultrashort T2 values. The performance of UTE-MRF was evaluated via simulations, phanto…
University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；Houston Methodist Hospital（休斯顿卫理公会医院）
We propose a multi-stage coarse-to-fine CNN-based framework, called SkullEngine, for high-resolution segmentation and large-scale landmark detection through a collaborative, integrated, and scalable JSD model and three segmentation and landmark detection refinement models. We evaluated our framework on a clinical dataset consisting of 170 CBCT/CT images for the task of segmenting 2 bones (midface and mandible) and detecting 175 clinically common landmarks on bones, teeth, and soft tissues.
PINNOCHIO: Physics-Informed Neural Network for Coupled Hyperelastic Interface-Volume Simulation in Orthognathic Surgery
Department of Biomedical Engineering and Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute（生物医学工程系和生物技术与跨学科研究中心，伦塞拉尔理工学院）；Department of Oral and Maxillofacial Surgery, Houston Methodist Research Institute（口腔颌面外科系，休斯顿方法主义研究学院）；Department of Neurosurgery, Baylor College of Medicine（神经外科系，贝勒医学院）
Image Sciences Institute, University Medical Center Utrecht（乌得勒支大学医学中心影像科学研究所）；Department of Orthopedics, University Medical Center Utrecht（乌得勒支大学医学中心骨科）；MRIguidance B.V.（MRIguidance有限公司（乌得勒支））；University Medical Center Utrecht（乌得勒支大学医学中心）
Purpose: To assess the feasibility of deep learning-based high resolution synthetic CT generation from MRI scans of the lower arm for orthopedic applications. Methods: A conditional Generative Adversarial Network was trained to synthesize CT images from multi-echo MR images. A training set of MRI and CT scans of 9 ex vivo lower arms was acquired and the CT images were registered to the MRI images. Three-fold cross-validation was applied to generate independent results for the entire dataset. The synthetic CT images were quantitatively evaluated with the mean absolute error metric, and Dice sim…
Imaging Biomarkers and Computer-aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center（美国国立卫生研究院临床中心放射学与成像科学部成像生物标志物和计算机辅助诊断实验室）；Ping An Technology Co., Ltd.（平安科技有限公司）
Dual-energy (DE) chest radiography provides the capability of selectively imaging two clinically relevant materials, namely soft tissues, and osseous structures, to better characterize a wide variety of thoracic pathology and potentially improve diagnosis in posteroanterior (PA) chest radiographs. However, DE imaging requires specialized hardware and a higher radiation dose than conventional radiography, and motion artifacts sometimes happen due to involuntary patient motion. In this work, we learn the mapping between conventional radiographs and bone suppressed radiographs. Specifically, we p…
Intensity Field Decomposition for Tissue-Guided Neural Tomography
School of Stomatology, Wuhan University（武汉大学口腔医学院）；School of Computer Science, Wuhan University（武汉大学计算机学院）；School of Automation Science and Engineering, South China University of Technology（华南理工大学自动化科学与工程学院）；Pazhou Laboratory（琶洲实验室）
Compressing and expanding (companding) HDR computerized tomography (CT) images to a single LDR image is still a relevant challenge. Besides the general need for simplification of the window setting method for the purpose of diagnosis, there are specific clinical needs for the resection of malignant lesions in the mandible, for example, where the lesions may exist in both the soft tissue and the bone. A successful medical algorithm has to take into account the requirement to variously expose the same range of specific gray levels, when they are presented by different body tissues. As a solution…
University of California, Los Angeles（加利福尼亚大学洛杉矶分校）
Panoramic X-ray (PX) provides a 2D picture of the patient's mouth in a panoramic view to help dentists observe the invisible disease inside the gum. However, it provides limited 2D information compared with cone-beam computed tomography (CBCT), another dental imaging method that generates a 3D picture of the oral cavity but with more radiation dose and a higher price. Consequently, it is of great interest to reconstruct the 3D structure from a 2D X-ray image, which can greatly explore the application of X-ray imaging in dental surgeries. In this paper, we propose a framework, named Oral-3D, to…
Virtual staining for 3D X-ray histology of bone implants
Institute of Materials Physics（材料物理研究所）；Helmholtz-Zentrum Hereon（海德堡研究中心）；Institute of Metallic Biomaterials（金属生物材料研究所）；Bruker Daltonics SPR（布鲁克尔达特利克斯SPR）；Data-Driven Analysis and Design of Materials（数据驱动的材料分析与设计）；University of Rostock（罗斯托克大学）
École de Technologie Supérieure（高等技术学院）；CHU Sainte-Justine Research Center（圣茹斯特医院研究中心）；Polytechnique Montréal（蒙特利尔理工学院）
Vertebral bone segmentation from magnetic resonance (MR) images is a challenging task. Due to the inherent nature of the modality to emphasize soft tissues of the body, common thresholding algorithms are ineffective in detecting bones in MR images. On the other hand, it is relatively easier to segment bones from CT images because of the high contrast between bones and the surrounding regions. For this reason, we perform a cross-modality synthesis between MR and CT domains for simple thresholding-based segmentation of the vertebral bones. However, this implicitly assumes the availability of pai…
Purpose: Body composition is known to be associated with many diseases including diabetes, cancers and cardiovascular diseases. In this paper, we developed a fully automatic body tissue decomposition procedure to segment three major compartments that are related to body composition analysis - subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT) and muscle. Three additional compartments - the ventral cavity, lung and bones were also segmented during the segmentation process to assist segmentation of the major compartments. Methods: A convolutional neural network (CNN) model with den…
