1
|
Silveira A, Greving I, Longo E, Scheel M, Weitkamp T, Fleck C, Shahar R, Zaslansky P. Deep learning to overcome Zernike phase-contrast nanoCT artifacts for automated micro-nano porosity segmentation in bone. JOURNAL OF SYNCHROTRON RADIATION 2024; 31:136-149. [PMID: 38095668 PMCID: PMC10833422 DOI: 10.1107/s1600577523009852] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 11/13/2023] [Indexed: 01/09/2024]
Abstract
Bone material contains a hierarchical network of micro- and nano-cavities and channels, known as the lacuna-canalicular network (LCN), that is thought to play an important role in mechanobiology and turnover. The LCN comprises micrometer-sized lacunae, voids that house osteocytes, and submicrometer-sized canaliculi that connect bone cells. Characterization of this network in three dimensions is crucial for many bone studies. To quantify X-ray Zernike phase-contrast nanotomography data, deep learning is used to isolate and assess porosity in artifact-laden tomographies of zebrafish bones. A technical solution is proposed to overcome the halo and shade-off domains in order to reliably obtain the distribution and morphology of the LCN in the tomographic data. Convolutional neural network (CNN) models are utilized with increasing numbers of images, repeatedly validated by `error loss' and `accuracy' metrics. U-Net and Sensor3D CNN models were trained on data obtained from two different synchrotron Zernike phase-contrast transmission X-ray microscopes, the ANATOMIX beamline at SOLEIL (Paris, France) and the P05 beamline at PETRA III (Hamburg, Germany). The Sensor3D CNN model with a smaller batch size of 32 and a training data size of 70 images showed the best performance (accuracy 0.983 and error loss 0.032). The analysis procedures, validated by comparison with human-identified ground-truth images, correctly identified the voids within the bone matrix. This proposed approach may have further application to classify structures in volumetric images that contain non-linear artifacts that degrade image quality and hinder feature identification.
Collapse
Affiliation(s)
- Andreia Silveira
- Department for Restorative, Preventive and Pediatric Dentistry, Charité-Universitaetsmedizin, Berlin, Germany
| | - Imke Greving
- Institute of Materials Physics, Helmholtz-Zentrum Hereon, Geesthacht, Germany
| | - Elena Longo
- Elettra – Sincrotrone Trieste SCpA, Basovizza, Trieste, Italy
| | | | | | - Claudia Fleck
- Fachgebiet Werkstofftechnik / Chair of Materials Science and Engineering, Institute of Materials Science and Technology, Faculty III Process Sciences, Technische Universität Berlin, Berlin, Germany
| | - Ron Shahar
- Koret School of Veterinary Medicine, The Robert H. Smith Faculty of Agriculture, Food and Environmental Sciences, Hebrew University of Jerusalem, Rehovot, Israel
| | - Paul Zaslansky
- Department for Restorative, Preventive and Pediatric Dentistry, Charité-Universitaetsmedizin, Berlin, Germany
| |
Collapse
|
2
|
Bruns S, Krüger D, Galli S, Wieland DF, Hammel JU, Beckmann F, Wennerberg A, Willumeit-Römer R, Zeller-Plumhoff B, Moosmann J. On the material dependency of peri-implant morphology and stability in healing bone. Bioact Mater 2023; 28:155-166. [PMID: 37250865 PMCID: PMC10212791 DOI: 10.1016/j.bioactmat.2023.05.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 04/07/2023] [Accepted: 05/09/2023] [Indexed: 05/31/2023] Open
Abstract
The microstructural architecture of remodeled bone in the peri-implant region of screw implants plays a vital role in the distribution of strain energy and implant stability. We present a study in which screw implants made from titanium, polyetheretherketone and biodegradable magnesium-gadolinium alloys were implanted into rat tibia and subjected to a push-out test four, eight and twelve weeks after implantation. Screws were 4 mm in length and with an M2 thread. The loading experiment was accompanied by simultaneous three-dimensional imaging using synchrotron-radiation microcomputed tomography at 5 μm resolution. Bone deformation and strains were tracked by applying optical flow-based digital volume correlation to the recorded image sequences. Implant stabilities measured for screws of biodegradable alloys were comparable to pins whereas non-degradable biomaterials experienced additional mechanical stabilization. Peri-implant bone morphology and strain transfer from the loaded implant site depended heavily on the biomaterial utilized. Titanium implants stimulated rapid callus formation displaying a consistent monomodal strain profile whereas the bone volume fraction in the vicinity of magnesium-gadolinium alloys exhibited a minimum close to the interface of the implant and less ordered strain transfer. Correlations in our data suggest that implant stability benefits from disparate bone morphological properties depending on the biomaterial utilized. This leaves the choice of biomaterial as situational depending on local tissue properties.
Collapse
Affiliation(s)
- Stefan Bruns
- Institute of Metallic Biomaterials, Helmholtz-Zentrum Hereon, Max-Planck-Str. 1, 21502, Geesthacht, Germany
| | - Diana Krüger
- Institute of Metallic Biomaterials, Helmholtz-Zentrum Hereon, Max-Planck-Str. 1, 21502, Geesthacht, Germany
| | - Silvia Galli
- University of Malmö, Faculty of Odontology, Department of Prosthodontics, Carl Gustafs Väg 34, Klerken, 20506, Malmö, Sweden
| | - D.C. Florian Wieland
- Institute of Metallic Biomaterials, Helmholtz-Zentrum Hereon, Max-Planck-Str. 1, 21502, Geesthacht, Germany
| | - Jörg U. Hammel
- Institute of Materials Physics, Helmholtz-Zentrum Hereon, Max-Planck-Str. 1, 21502, Geesthacht, Germany
| | - Felix Beckmann
- Institute of Materials Physics, Helmholtz-Zentrum Hereon, Max-Planck-Str. 1, 21502, Geesthacht, Germany
| | - Ann Wennerberg
- University of Gothenburg, Institute of Odontology, Department of Prosthodontics, Medicinaregatan 12 f, 41390, Göteborg, Sweden
| | - Regine Willumeit-Römer
- Institute of Metallic Biomaterials, Helmholtz-Zentrum Hereon, Max-Planck-Str. 1, 21502, Geesthacht, Germany
| | - Berit Zeller-Plumhoff
- Institute of Metallic Biomaterials, Helmholtz-Zentrum Hereon, Max-Planck-Str. 1, 21502, Geesthacht, Germany
| | - Julian Moosmann
- Institute of Materials Physics, Helmholtz-Zentrum Hereon, Max-Planck-Str. 1, 21502, Geesthacht, Germany
| |
Collapse
|
3
|
Bo T, Lin Y, Han J, Hao Z, Liu J. Machine learning-assisted data filtering and QSAR models for prediction of chemical acute toxicity on rat and mouse. JOURNAL OF HAZARDOUS MATERIALS 2023; 452:131344. [PMID: 37027914 DOI: 10.1016/j.jhazmat.2023.131344] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Revised: 03/20/2023] [Accepted: 03/31/2023] [Indexed: 05/03/2023]
Abstract
Machine learning (ML) methods provide a new opportunity to build quantitative structure-activity relationship (QSAR) models for predicting chemicals' toxicity based on large toxicity data sets, but they are limited in insufficient model robustness due to poor data set quality for chemicals with certain structures. To address this issue and improve model robustness, we built a large data set on rat oral acute toxicity for thousands of chemicals, then used ML to filter chemicals favorable for regression models (CFRM). In comparison to chemicals not favorable for regression models (CNRM), CFRM accounted for 67% of chemicals in the original data set, and had a higher structural similarity and a smaller toxicity distribution in 2-4 log10 (mg/kg). The performance of established regression models for CFRM was greatly improved, with root-mean-square deviations (RMSE) in the range of 0.45-0.48 log10 (mg/kg). Classification models were built for CNRM using all chemicals in the original data set, and the area under receiver operating characteristic (AUROC) reached 0.75-0.76. The proposed strategy was successfully applied to a mouse oral acute data set, yielding RMSE and AUROC in the range of 0.36-0.38 log10 (mg/kg) and 0.79, respectively.
Collapse
Affiliation(s)
- Tao Bo
- School of Environment, Hangzhou Institute for Advanced Study, UCAS, Hangzhou 310024, China; State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, P.O. Box 2871, Beijing 100085, China
| | - Yaohui Lin
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, P.O. Box 2871, Beijing 100085, China; Key Laboratory for Analytical Science of Food Safety and Biology of MOE, Fujian Provincial Key Lab of Analysis and Detection for Food Safety, College of Chemistry, Fuzhou University, Fuzhou, Fujian 350116, China
| | - Jinglong Han
- State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology Shenzhen, Shenzhen 518055, China
| | - Zhineng Hao
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, P.O. Box 2871, Beijing 100085, China.
| | - Jingfu Liu
- School of Environment, Hangzhou Institute for Advanced Study, UCAS, Hangzhou 310024, China; State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, P.O. Box 2871, Beijing 100085, China.
| |
Collapse
|
4
|
Flenner S, Hagemann J, Wittwer F, Longo E, Kubec A, Rothkirch A, David C, Müller M, Greving I. Hard X-ray full-field nanoimaging using a direct photon-counting detector. JOURNAL OF SYNCHROTRON RADIATION 2023; 30:390-399. [PMID: 36891852 PMCID: PMC10000802 DOI: 10.1107/s1600577522012103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 12/22/2022] [Indexed: 06/18/2023]
Abstract
Full-field X-ray nanoimaging is a widely used tool in a broad range of scientific areas. In particular, for low-absorbing biological or medical samples, phase contrast methods have to be considered. Three well established phase contrast methods at the nanoscale are transmission X-ray microscopy with Zernike phase contrast, near-field holography and near-field ptychography. The high spatial resolution, however, often comes with the drawback of a lower signal-to-noise ratio and significantly longer scan times, compared with microimaging. In order to tackle these challenges a single-photon-counting detector has been implemented at the nanoimaging endstation of the beamline P05 at PETRA III (DESY, Hamburg) operated by Helmholtz-Zentrum Hereon. Thanks to the long sample-to-detector distance available, spatial resolutions of below 100 nm were reached in all three presented nanoimaging techniques. This work shows that a single-photon-counting detector in combination with a long sample-to-detector distance allows one to increase the time resolution for in situ nanoimaging, while keeping a high signal-to-noise level.
Collapse
Affiliation(s)
- Silja Flenner
- Helmholtz-Zentrum Hereon, Max-Planck-Strasse 1, 21502 Geesthacht, Germany
| | - Johannes Hagemann
- Center for X-ray and Nano Science – CXNS, Deutsches Elektronen-Synchrotron – DESY, Notkestraße 85, 22607 Hamburg, Germany
| | - Felix Wittwer
- Center for X-ray and Nano Science – CXNS, Deutsches Elektronen-Synchrotron – DESY, Notkestraße 85, 22607 Hamburg, Germany
| | - Elena Longo
- Helmholtz-Zentrum Hereon, Max-Planck-Strasse 1, 21502 Geesthacht, Germany
| | - Adam Kubec
- Paul Scherrer Institut, Forschungsstrasse 111, 5232 Villigen, Switzerland
| | - André Rothkirch
- Center for X-ray and Nano Science – CXNS, Deutsches Elektronen-Synchrotron – DESY, Notkestraße 85, 22607 Hamburg, Germany
| | - Christian David
- Paul Scherrer Institut, Forschungsstrasse 111, 5232 Villigen, Switzerland
| | - Martin Müller
- Helmholtz-Zentrum Hereon, Max-Planck-Strasse 1, 21502 Geesthacht, Germany
| | - Imke Greving
- Helmholtz-Zentrum Hereon, Max-Planck-Strasse 1, 21502 Geesthacht, Germany
| |
Collapse
|
5
|
Hao G, Roberts EJ, Chavez T, Zhao Z, Holman EA, Yanxon H, Green A, Krishnan H, Ushizima D, McReynolds D, Schwarz N, Zwart PH, Hexemer A, Parkinson DY. Deploying Machine Learning Based Segmentation for Scientific Imaging Analysis at Synchrotron Facilities. IS&T INTERNATIONAL SYMPOSIUM ON ELECTRONIC IMAGING 2023; 35:IPAS-290. [PMID: 38130938 PMCID: PMC10735246 DOI: 10.2352/ei.2023.35.9.ipas-290] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2023]
Abstract
Scientific user facilities present a unique set of challenges for image processing due to the large volume of data generated from experiments and simulations. Furthermore, developing and implementing algorithms for real-time processing and analysis while correcting for any artifacts or distortions in images remains a complex task, given the computational requirements of the processing algorithms. In a collaborative effort across multiple Department of Energy national laboratories, the "MLExchange" project is focused on addressing these challenges. MLExchange is a Machine Learning framework deploying interactive web interfaces to enhance and accelerate data analysis. The platform allows users to easily upload, visualize, label, and train networks. The resulting models can be deployed on real data while both results and models could be shared with the scientists. The MLExchange web-based application for image segmentation allows for training, testing, and evaluating multiple machine learning models on hand-labeled tomography data. This environment provides users with an intuitive interface for segmenting images using a variety of machine learning algorithms and deep-learning neural networks. Additionally, these tools have the potential to overcome limitations in traditional image segmentation techniques, particularly for complex and low-contrast images.
Collapse
Affiliation(s)
- Guanhua Hao
- Advanced Light Source (ALS), Lawrence Berkeley National Laboratory; Berkeley, CA 94720
| | - Eric J. Roberts
- Center for Advanced Mathematics for Energy Research Applications (CAMERA), Lawrence Berkeley National Laboratory; Berkeley, CA 94720
- Molecular Biophysics and Integrated Bioimaging (MBIB), Lawrence Berkeley National Laboratory; Berkeley, CA 94720
| | - Tanny Chavez
- Advanced Light Source (ALS), Lawrence Berkeley National Laboratory; Berkeley, CA 94720
| | - Zhuowen Zhao
- Advanced Light Source (ALS), Lawrence Berkeley National Laboratory; Berkeley, CA 94720
| | - Elizabeth A. Holman
- Advanced Light Source (ALS), Lawrence Berkeley National Laboratory; Berkeley, CA 94720
| | - Howard Yanxon
- Advanced Photon Source (APS), Argonne National Laboratory; Lemont, IL 60439
| | - Adam Green
- Advanced Light Source (ALS), Lawrence Berkeley National Laboratory; Berkeley, CA 94720
| | - Harinarayan Krishnan
- Advanced Light Source (ALS), Lawrence Berkeley National Laboratory; Berkeley, CA 94720
- Center for Advanced Mathematics for Energy Research Applications (CAMERA), Lawrence Berkeley National Laboratory; Berkeley, CA 94720
| | - Daniela Ushizima
- Center for Advanced Mathematics for Energy Research Applications (CAMERA), Lawrence Berkeley National Laboratory; Berkeley, CA 94720
- Computational Research Division (CRD), Lawrence Berkeley National Laboratory; Berkeley, CA 94720
| | - Dylan McReynolds
- Advanced Light Source (ALS), Lawrence Berkeley National Laboratory; Berkeley, CA 94720
| | - Nicholas Schwarz
- Advanced Photon Source (APS), Argonne National Laboratory; Lemont, IL 60439
| | - Petrus H. Zwart
- Center for Advanced Mathematics for Energy Research Applications (CAMERA), Lawrence Berkeley National Laboratory; Berkeley, CA 94720
- Molecular Biophysics and Integrated Bioimaging (MBIB), Lawrence Berkeley National Laboratory; Berkeley, CA 94720
| | - Alexander Hexemer
- Advanced Light Source (ALS), Lawrence Berkeley National Laboratory; Berkeley, CA 94720
| | - Dilworth Y. Parkinson
- Advanced Light Source (ALS), Lawrence Berkeley National Laboratory; Berkeley, CA 94720
| |
Collapse
|
6
|
Performance Evaluation of Deep Neural Network Model for Coherent X-ray Imaging. AI 2022. [DOI: 10.3390/ai3020020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
We present a supervised deep neural network model for phase retrieval of coherent X-ray imaging and evaluate the performance. A supervised deep-learning-based approach requires a large amount of pre-training datasets. In most proposed models, the various experimental uncertainties are not considered when the input dataset, corresponding to the diffraction image in reciprocal space, is generated. We explore the performance of the deep neural network model, which is trained with an ideal quality of dataset, when it faces real-like corrupted diffraction images. We focus on three aspects of data qualities such as a detection dynamic range, a degree of coherence and noise level. The investigation shows that the deep neural network model is robust to a limited dynamic range and partially coherent X-ray illumination in comparison to the traditional phase retrieval, although it is more sensitive to the noise than the iteration-based method. This study suggests a baseline capability of the supervised deep neural network model for coherent X-ray imaging in preparation for the deployment to the laboratory where diffraction images are acquired.
Collapse
|