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Dejea H, Pierantoni M, Orozco GA, B Wrammerfors ET, Gstöhl SJ, Schlepütz CM, Isaksson H. In Situ Loading and Time-Resolved Synchrotron-Based Phase Contrast Tomography for the Mechanical Investigation of Connective Knee Tissues: A Proof-of-Concept Study. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2308811. [PMID: 38520713 DOI: 10.1002/advs.202308811] [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: 11/16/2023] [Revised: 02/26/2024] [Indexed: 03/25/2024]
Abstract
Articular cartilage and meniscus transfer and distribute mechanical loads in the knee joint. Degeneration of these connective tissues occurs during the progression of knee osteoarthritis, which affects their composition, microstructure, and mechanical properties. A deeper understanding of disease progression can be obtained by studying them simultaneously. Time-resolved synchrotron-based X-ray phase-contrast tomography (SR-PhC-µCT) allows to capture the tissue dynamics. This proof-of-concept study presents a rheometer setup for simultaneous in situ unconfined compression and SR-PhC-µCT of connective knee tissues. The microstructural response of bovine cartilage (n = 16) and meniscus (n = 4) samples under axial continuously increased strain, or two steps of 15% strain (stress-relaxation) is studied. The chondrocyte distribution in cartilage and the collagen fiber orientation in the meniscus are assessed. Variations in chondrocyte density reveal an increase in the top 40% of the sample during loading, compared to the lower half. Meniscus collagen fibers reorient perpendicular to the loading direction during compression and partially redisperse during relaxation. Radiation damage, image repeatability, and image quality assessments show little to no effects on the results. In conclusion, this approach is highly promising for future studies of human knee tissues to understand their microstructure, mechanical response, and progression in degenerative diseases.
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Affiliation(s)
- Hector Dejea
- Department of Biomedical Engineering, Lund University, Box 118, Lund, 221 00, Sweden
- MAX IV Laboratory, Lund University, Lund, 224 84, Sweden
| | - Maria Pierantoni
- Department of Biomedical Engineering, Lund University, Box 118, Lund, 221 00, Sweden
| | - Gustavo A Orozco
- Department of Biomedical Engineering, Lund University, Box 118, Lund, 221 00, Sweden
| | | | - Stefan J Gstöhl
- Swiss Light Source, Paul Scherrer Institute, Villigen PSI, 5232, Switzerland
| | | | - Hanna Isaksson
- Department of Biomedical Engineering, Lund University, Box 118, Lund, 221 00, Sweden
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Górski K, Borowska M, Stefanik E, Polkowska I, Turek B, Bereznowski A, Domino M. Application of Two-Dimensional Entropy Measures to Detect the Radiographic Signs of Tooth Resorption and Hypercementosis in an Equine Model. Biomedicines 2022; 10:2914. [PMID: 36428482 PMCID: PMC9687516 DOI: 10.3390/biomedicines10112914] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2022] [Revised: 10/28/2022] [Accepted: 11/11/2022] [Indexed: 11/16/2022] Open
Abstract
Dental disorders are a serious health problem in equine medicine, their early recognition benefits the long-term general health of the horse. Most of the initial signs of Equine Odontoclastic Tooth Resorption and Hypercementosis (EOTRH) syndrome concern the alveolar aspect of the teeth, thus, the need for early recognition radiographic imaging. This study is aimed to evaluate the applicability of entropy measures to quantify the radiological signs of tooth resorption and hypercementosis as well as to enhance radiographic image quality in order to facilitate the identification of the signs of EOTRH syndrome. A detailed examination of the oral cavity was performed in eighty horses. Each evaluated incisor tooth was assigned to one of four grade-related EOTRH groups (0-3). Radiographs of the incisor teeth were taken and digitally processed. For each radiograph, two-dimensional sample (SampEn2D), fuzzy (FuzzEn2D), permutation (PermEn2D), dispersion (DispEn2D), and distribution (DistEn2D) entropies were measured after image filtering was performed using Normalize, Median, and LaplacianSharpening filters. Moreover, the similarities between entropy measures and selected Gray-Level Co-occurrence Matrix (GLCM) texture features were investigated. Among the 15 returned measures, DistEn2D was EOTRH grade-related. Moreover, DistEn2D extracted after Normalize filtering was the most informative. The EOTRH grade-related similarity between DistEn2D and Difference Entropy (GLCM) confirms the higher irregularity and complexity of incisor teeth radiographs in advanced EOTRH syndrome, demonstrating the greatest sensitivity (0.50) and specificity (0.95) of EOTRH 3 group detection. An application of DistEn2D to Normalize filtered incisor teeth radiographs enables the identification of the radiological signs of advanced EOTRH with higher accuracy than the previously used entropy-related GLCM texture features.
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Affiliation(s)
- Kamil Górski
- Department of Large Animal Diseases and Clinic, Institute of Veterinary Medicine, Warsaw University of Life Sciences, 02-787 Warsaw, Poland; (E.S.); (B.T.)
| | - Marta Borowska
- Institute of Biomedical Engineering, Faculty of Mechanical Engineering, Białystok University of Technology, 15-351 Bialystok, Poland;
| | - Elżbieta Stefanik
- Department of Large Animal Diseases and Clinic, Institute of Veterinary Medicine, Warsaw University of Life Sciences, 02-787 Warsaw, Poland; (E.S.); (B.T.)
| | - Izabela Polkowska
- Department and Clinic of Animal Surgery, Faculty of Veterinary Medicine, University of Life Sciences, 20-950 Lublin, Poland;
| | - Bernard Turek
- Department of Large Animal Diseases and Clinic, Institute of Veterinary Medicine, Warsaw University of Life Sciences, 02-787 Warsaw, Poland; (E.S.); (B.T.)
| | - Andrzej Bereznowski
- Division of Veterinary Epidemiology and Economics, Institute of Veterinary Medicine, Warsaw University of Life Sciences, Nowoursynowska 159c, 02-776 Warsaw, Poland;
| | - Małgorzata Domino
- Department of Large Animal Diseases and Clinic, Institute of Veterinary Medicine, Warsaw University of Life Sciences, 02-787 Warsaw, Poland; (E.S.); (B.T.)
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Selection of Filtering and Image Texture Analysis in the Radiographic Images Processing of Horses' Incisor Teeth Affected by the EOTRH Syndrome. SENSORS 2022; 22:s22082920. [PMID: 35458905 PMCID: PMC9030967 DOI: 10.3390/s22082920] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 04/05/2022] [Accepted: 04/08/2022] [Indexed: 12/19/2022]
Abstract
Equine odontoclastic tooth resorption and hypercementosis (EOTRH) is one of the horses’ dental diseases, mainly affecting the incisor teeth. An increase in the incidence of aged horses and a painful progressive course of the disease create the need for improved early diagnosis. Besides clinical findings, EOTRH recognition is based on the typical radiographic findings, including levels of dental resorption and hypercementosis. This study aimed to introduce digital processing methods to equine dental radiographic images and identify texture features changing with disease progression. The radiographs of maxillary incisor teeth from 80 horses were obtained. Each incisor was annotated by separate masks and clinically classified as 0, 1, 2, or 3 EOTRH degrees. Images were filtered by Mean, Median, Normalize, Bilateral, Binomial, CurvatureFlow, LaplacianSharpening, DiscreteGaussian, and SmoothingRecursiveGaussian filters independently, and 93 features of image texture were extracted using First Order Statistics (FOS), Gray Level Co-occurrence Matrix (GLCM), Neighbouring Gray Tone Difference Matrix (NGTDM), Gray Level Dependence Matrix (GLDM), Gray Level Run Length Matrix (GLRLM), and Gray Level Size Zone Matrix (GLSZM) approaches. The most informative processing was selected. GLCM and GLRLM return the most favorable features for the quantitative evaluation of radiographic signs of the EOTRH syndrome, which may be supported by filtering by filters improving the edge delimitation.
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Jialin SMS, Xiang LMD, Jianquan ZMD, Jiaqi ZMD, Lulu ZMS. Quantitative Evaluation of Cirrhosis by Geometrical Characteristics of Hepatic Capsule Based on High-frequency Ultrasound Imaging: an Experimental Study. ADVANCED ULTRASOUND IN DIAGNOSIS AND THERAPY 2021. [DOI: 10.37015/audt.2021.200073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
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Convolutional neuronal networks combined with X-ray phase-contrast imaging for a fast and observer-independent discrimination of cartilage and liver diseases stages. Sci Rep 2020; 10:20007. [PMID: 33203975 PMCID: PMC7673137 DOI: 10.1038/s41598-020-76937-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2020] [Accepted: 10/27/2020] [Indexed: 12/19/2022] Open
Abstract
We applied transfer learning using Convolutional Neuronal Networks to high resolution X-ray phase contrast computed tomography datasets and tested the potential of the systems to accurately classify Computed Tomography images of different stages of two diseases, i.e. osteoarthritis and liver fibrosis. The purpose is to identify a time-effective and observer-independent methodology to identify pathological conditions. Propagation-based X-ray phase contrast imaging WAS used with polychromatic X-rays to obtain a 3D visualization of 4 human cartilage plugs and 6 rat liver samples with a voxel size of 0.7 × 0.7 × 0.7 µm3 and 2.2 × 2.2 × 2.2 µm3, respectively. Images with a size of 224 × 224 pixels are used to train three pre-trained convolutional neuronal networks for data classification, which are the VGG16, the Inception V3, and the Xception networks. We evaluated the performance of the three systems in terms of classification accuracy and studied the effect of the variation of the number of inputs, training images and of iterations. The VGG16 network provides the highest classification accuracy when the training and the validation-test of the network are performed using data from the same samples for both the cartilage (99.8%) and the liver (95.5%) datasets. The Inception V3 and Xception networks achieve an accuracy of 84.7% (43.1%) and of 72.6% (53.7%), respectively, for the cartilage (liver) images. By using data from different samples for the training and validation-test processes, the Xception network provided the highest test accuracy for the cartilage dataset (75.7%), while for the liver dataset the VGG16 network gave the best results (75.4%). By using convolutional neuronal networks we show that it is possible to classify large datasets of biomedical images in less than 25 min on a 8 CPU processor machine providing a precise, robust, fast and observer-independent method for the discrimination/classification of different stages of osteoarthritis and liver diseases.
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Rytky SJO, Tiulpin A, Frondelius T, Finnilä MAJ, Karhula SS, Leino J, Pritzker KPH, Valkealahti M, Lehenkari P, Joukainen A, Kröger H, Nieminen HJ, Saarakkala S. Automating three-dimensional osteoarthritis histopathological grading of human osteochondral tissue using machine learning on contrast-enhanced micro-computed tomography. Osteoarthritis Cartilage 2020; 28:1133-1144. [PMID: 32437969 DOI: 10.1016/j.joca.2020.05.002] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/22/2019] [Revised: 04/16/2020] [Accepted: 05/01/2020] [Indexed: 02/02/2023]
Abstract
OBJECTIVE To develop and validate a machine learning (ML) approach for automatic three-dimensional (3D) histopathological grading of osteochondral samples imaged with contrast-enhanced micro-computed tomography (CEμCT). DESIGN A total of 79 osteochondral cores from 24 total knee arthroplasty patients and two asymptomatic donors were imaged using CEμCT with phosphotungstic acid -staining. Volumes-of-interest (VOI) in surface (SZ), deep (DZ) and calcified (CZ) zones were extracted depth-wise and subjected to dimensionally reduced Local Binary Pattern -textural feature analysis. Regularized linear and logistic regression (LR) models were trained zone-wise against the manually assessed semi-quantitative histopathological CEμCT grades (diameter = 2 mm samples). Models were validated using nested leave-one-out cross-validation and an independent test set (4 mm samples). The performance was primarily assessed using Mean Squared Error (MSE) and Average Precision (AP, confidence intervals are given in square brackets). RESULTS Highest performance on cross-validation was observed for SZ, both on linear regression (MSE = 0.49, 0.69 and 0.71 for SZ, DZ and CZ, respectively) and LR (AP = 0.9 [0.77-0.99], 0.46 [0.28-0.67] and 0.65 [0.41-0.85] for SZ, DZ and CZ, respectively). The test set evaluations yielded increased MSE on all zones. For LR, the performance was also best for the SZ (AP = 0.85 [0.73-0.93], 0.82 [0.70-0.92] and 0.8 [0.67-0.9], for SZ, DZ and CZ, respectively). CONCLUSION We present the first ML-based automatic 3D histopathological osteoarthritis (OA) grading method which also adequately perform on grading unseen data, especially in SZ. After further development, the method could potentially be applied by OA researchers since the grading software and all source codes are publicly available.
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Affiliation(s)
- S J O Rytky
- Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland.
| | - A Tiulpin
- Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland; Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland.
| | - T Frondelius
- Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland.
| | - M A J Finnilä
- Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland; Medical Research Center, University of Oulu, Oulu, Finland.
| | - S S Karhula
- Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland; Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland.
| | - J Leino
- Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland.
| | - K P H Pritzker
- Department of Laboratory Medicine and Pathobiology, Surgery University of Toronto, Toronto, Ontario, Canada; Mount Sinai Hospital, Toronto, Ontario, Canada.
| | - M Valkealahti
- Department of Surgery and Intensive Care, Oulu University Hospital, Oulu, Finland.
| | - P Lehenkari
- Medical Research Center, University of Oulu, Oulu, Finland; Department of Surgery and Intensive Care, Oulu University Hospital, Oulu, Finland; Cancer and Translational Medical Research Unit, Faculty of Medicine, University of Oulu, Oulu, Finland.
| | - A Joukainen
- Department of Orthopaedics, Traumatology and Hand Surgery, Kuopio University Hospital, Kuopio, Finland.
| | - H Kröger
- Department of Orthopaedics, Traumatology and Hand Surgery, Kuopio University Hospital, Kuopio, Finland.
| | - H J Nieminen
- Dept. of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland; Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland.
| | - S Saarakkala
- Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland; Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland.
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Zhang R, Cheng C, Zhao X, Li X. Multiscale Mask R-CNN-Based Lung Tumor Detection Using PET Imaging. Mol Imaging 2020; 18:1536012119863531. [PMID: 31364467 PMCID: PMC6669841 DOI: 10.1177/1536012119863531] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Abstract
Positron emission tomography (PET) imaging serves as one of the most competent methods for the diagnosis of various malignancies, such as lung tumor. However, with an elevation in the utilization of PET scan, radiologists are overburdened considerably. Consequently, a new approach of “computer-aided diagnosis” is being contemplated to curtail the heavy workloads. In this article, we propose a multiscale Mask Region–Based Convolutional Neural Network (Mask R-CNN)–based method that uses PET imaging for the detection of lung tumor. First, we produced 3 models of Mask R-CNN for lung tumor candidate detection. These 3 models were generated by fine-tuning the Mask R-CNN using certain training data that consisted of images from 3 different scales. Each of the training data set included 594 slices with lung tumor. These 3 models of Mask R-CNN models were then integrated using weighted voting strategy to diminish the false-positive outcomes. A total of 134 PET slices were employed as test set in this experiment. The precision, recall, and F score values of our proposed method were 0.90, 1, and 0.95, respectively. Experimental results exhibited strong conviction about the effectiveness of this method in detecting lung tumors, along with the capability of identifying a healthy chest pattern and reducing incorrect identification of tumors to a large extent.
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Affiliation(s)
- Rui Zhang
- 1 Department of Digital Media, Shenzhen Institute of Information Technology, Shenzhen, Guangdong, China
| | - Chao Cheng
- 2 Department of Nuclear Medicine, Changhai Hospital, Shanghai, People's Republic of China
| | - Xuehua Zhao
- 1 Department of Digital Media, Shenzhen Institute of Information Technology, Shenzhen, Guangdong, China
| | - Xuechen Li
- 3 College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, People's Republic of China
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8
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Huynh RN, Pesante B, Nehmetallah G, Raub CB. Polarized reflectance from articular cartilage depends upon superficial zone collagen network microstructure. BIOMEDICAL OPTICS EXPRESS 2019; 10:5518-5534. [PMID: 31799028 PMCID: PMC6865123 DOI: 10.1364/boe.10.005518] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/17/2019] [Revised: 09/09/2019] [Accepted: 09/10/2019] [Indexed: 05/02/2023]
Abstract
Polarized reflectance from articular cartilage involves light scattering dependent on surface features, sub-surface optical properties, and collagen birefringence. To understand how surface roughness, zonal collagen microstructure, and chondrocyte organization contribute to polarized reflectance signals, experiments were conducted on bovine cartilage explants and osteochondral cores to compare polarized reflectance texture with split lines and relate these signals to cartilage zonal features and chondrocyte distribution. Texture parameter sensitivity to articular surface damage was determined from polarized reflectance maps and optimized to detect surface damage. Results indicate that polarized reflectance texture predominantly derives from the superficial zone collagen network, while the parameter average value also depends on surface roughness and total cartilage thickness.
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Affiliation(s)
- R. N. Huynh
- Department of Biomedical Engineering, The Catholic University of America, 620 Michigan Ave NE., Washington, DC 20064, USA
| | - B. Pesante
- Department of Biomedical Engineering, The Catholic University of America, 620 Michigan Ave NE., Washington, DC 20064, USA
| | - G. Nehmetallah
- Department of Electrical Engineering and Computer Science, The Catholic University of America, 620 Michigan Ave NE., Washington, DC 20064, USA
| | - C. B. Raub
- Department of Biomedical Engineering, The Catholic University of America, 620 Michigan Ave NE., Washington, DC 20064, USA
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9
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Cabitza F, Locoro A, Banfi G. Machine Learning in Orthopedics: A Literature Review. Front Bioeng Biotechnol 2018; 6:75. [PMID: 29998104 PMCID: PMC6030383 DOI: 10.3389/fbioe.2018.00075] [Citation(s) in RCA: 114] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2018] [Accepted: 05/23/2018] [Indexed: 12/12/2022] Open
Abstract
In this paper we present the findings of a systematic literature review covering the articles published in the last two decades in which the authors described the application of a machine learning technique and method to an orthopedic problem or purpose. By searching both in the Scopus and Medline databases, we retrieved, screened and analyzed the content of 70 journal articles, and coded these resources following an iterative method within a Grounded Theory approach. We report the survey findings by outlining the articles' content in terms of the main machine learning techniques mentioned therein, the orthopedic application domains, the source data and the quality of their predictive performance.
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Affiliation(s)
- Federico Cabitza
- Dipartimento di Informatica, Sistemistica e Comunicazione, Universitá degli Studi di Milano-Bicocca, Milan, Italy
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
| | | | - Giuseppe Banfi
- Dipartimento di Informatica, Sistemistica e Comunicazione, Universitá degli Studi di Milano-Bicocca, Milan, Italy
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10
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Abidin AZ, Deng B, DSouza AM, Nagarajan MB, Coan P, Wismüller A. Deep transfer learning for characterizing chondrocyte patterns in phase contrast X-Ray computed tomography images of the human patellar cartilage. Comput Biol Med 2018; 95:24-33. [PMID: 29433038 PMCID: PMC5869140 DOI: 10.1016/j.compbiomed.2018.01.008] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2017] [Revised: 01/22/2018] [Accepted: 01/23/2018] [Indexed: 10/18/2022]
Abstract
Phase contrast X-ray computed tomography (PCI-CT) has been demonstrated to be effective for visualization of the human cartilage matrix at micrometer resolution, thereby capturing osteoarthritis induced changes to chondrocyte organization. This study aims to systematically assess the efficacy of deep transfer learning methods for classifying between healthy and diseased tissue patterns. We extracted features from two different convolutional neural network architectures, CaffeNet and Inception-v3 for characterizing such patterns. These features were quantitatively evaluated in a classification task measured by the area (AUC) under the Receiver Operating Characteristic (ROC) curve as well as qualitative visualization through a dimension reduction approach t-Distributed Stochastic Neighbor Embedding (t-SNE). The best classification performance, for CaffeNet, was observed when using features from the last convolutional layer and the last fully connected layer (AUCs >0.91). Meanwhile, off-the-shelf features from Inception-v3 produced similar classification performance (AUC >0.95). Visualization of features from these layers further confirmed adequate characterization of chondrocyte patterns for reliably distinguishing between healthy and osteoarthritic tissue classes. Such techniques, can be potentially used for detecting the presence of osteoarthritis related changes in the human patellar cartilage.
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Affiliation(s)
- Anas Z Abidin
- Department of Biomedical Engineering, University of Rochester Medical Center, Rochester, NY, USA.
| | - Botao Deng
- Department of Electrical Engineering, University of Rochester Medical Center, Rochester, NY, USA
| | - Adora M DSouza
- Department of Electrical Engineering, University of Rochester Medical Center, Rochester, NY, USA
| | - Mahesh B Nagarajan
- Department of Radiological Sciences, University of California Los Angeles, Los Angeles, USA
| | - Paola Coan
- European Synchrotron Radiation Facility, Grenoble, France; Faculty of Medicine and Institute of Clinical Radiology, Ludwig Maximilians University, Munich Germany
| | - Axel Wismüller
- Department of Biomedical Engineering, University of Rochester Medical Center, Rochester, NY, USA; Department of Imaging Sciences, University of Rochester Medical Center, Rochester, NY, USA; Department of Electrical Engineering, University of Rochester Medical Center, Rochester, NY, USA; Faculty of Medicine and Institute of Clinical Radiology, Ludwig Maximilians University, Munich Germany
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Nelson BB, Kawcak CE, Barrett MF, McIlwraith CW, Grinstaff MW, Goodrich LR. Recent advances in articular cartilage evaluation using computed tomography and magnetic resonance imaging. Equine Vet J 2018; 50:564-579. [DOI: 10.1111/evj.12808] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2017] [Accepted: 01/09/2018] [Indexed: 12/18/2022]
Affiliation(s)
- B. B. Nelson
- Gail Holmes Equine Orthopaedic Research Center Department of Clinical Sciences College of Veterinary Medicine and Biomedical Sciences, Colorado State University Fort Collins Colorado USA
| | - C. E. Kawcak
- Gail Holmes Equine Orthopaedic Research Center Department of Clinical Sciences College of Veterinary Medicine and Biomedical Sciences, Colorado State University Fort Collins Colorado USA
| | - M. F. Barrett
- Gail Holmes Equine Orthopaedic Research Center Department of Clinical Sciences College of Veterinary Medicine and Biomedical Sciences, Colorado State University Fort Collins Colorado USA
- Department of Environmental and Radiological Health Sciences Colorado State University Fort Collins Colorado USA
| | - C. W. McIlwraith
- Gail Holmes Equine Orthopaedic Research Center Department of Clinical Sciences College of Veterinary Medicine and Biomedical Sciences, Colorado State University Fort Collins Colorado USA
| | - M. W. Grinstaff
- Departments of Biomedical Engineering, Chemistry and Medicine Boston University Boston Massachusetts USA
| | - L. R. Goodrich
- Gail Holmes Equine Orthopaedic Research Center Department of Clinical Sciences College of Veterinary Medicine and Biomedical Sciences, Colorado State University Fort Collins Colorado USA
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Lakin BA, Snyder BD, Grinstaff MW. Assessing Cartilage Biomechanical Properties: Techniques for Evaluating the Functional Performance of Cartilage in Health and Disease. Annu Rev Biomed Eng 2017; 19:27-55. [DOI: 10.1146/annurev-bioeng-071516-044525] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Benjamin A. Lakin
- Center for Advanced Orthopedic Studies, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts 02215
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts 02215
| | - Brian D. Snyder
- Center for Advanced Orthopedic Studies, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts 02215
- Orthopedic Center, Children's Hospital, Boston, Massachusetts 02115
| | - Mark W. Grinstaff
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts 02215
- Department of Chemistry, Boston University, Boston, Massachusetts 02215
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13
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Nagarajan MB, Coan P, Huber MB, Diemoz PC, Wismüller A. Volumetric quantitative characterization of human patellar cartilage with topological and geometrical features on phase-contrast X-ray computed tomography. Med Biol Eng Comput 2015; 53:1211-20. [PMID: 26142112 PMCID: PMC4630098 DOI: 10.1007/s11517-015-1340-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2014] [Accepted: 06/22/2015] [Indexed: 01/19/2023]
Abstract
Phase-contrast X-ray computed tomography (PCI-CT) has attracted significant interest in recent years for its ability to provide significantly improved image contrast in low absorbing materials such as soft biological tissue. In the research context of cartilage imaging, previous studies have demonstrated the ability of PCI-CT to visualize structural details of human patellar cartilage matrix and capture changes to chondrocyte organization induced by osteoarthritis. This study evaluates the use of geometrical and topological features for volumetric characterization of such chondrocyte patterns in the presence (or absence) of osteoarthritic damage. Geometrical features derived from the scaling index method (SIM) and topological features derived from Minkowski Functionals were extracted from 1392 volumes of interest (VOI) annotated on PCI-CT images of ex vivo human patellar cartilage specimens. These features were subsequently used in a machine learning task with support vector regression to classify VOIs as healthy or osteoarthritic; classification performance was evaluated using the area under the receiver operating characteristic curve (AUC). Our results show that the classification performance of SIM-derived geometrical features (AUC: 0.90 ± 0.09) is significantly better than Minkowski Functionals volume (AUC: 0.54 ± 0.02), surface (AUC: 0.72 ± 0.06), mean breadth (AUC: 0.74 ± 0.06) and Euler characteristic (AUC: 0.78 ± 0.04) (p < 10(-4)). These results suggest that such geometrical features can provide a detailed characterization of the chondrocyte organization in the cartilage matrix in an automated manner, while also enabling classification of cartilage as healthy or osteoarthritic with high accuracy. Such features could potentially serve as diagnostic imaging markers for evaluating osteoarthritis progression and its response to different therapeutic intervention strategies.
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Affiliation(s)
- Mahesh B Nagarajan
- Departments of Imaging Sciences and Biomedical Engineering, University of Rochester, Rochester, NY, USA.
| | - Paola Coan
- Faculty of Medicine & Institute of Clinical Radiology, Ludwig Maximilians University, 80336, Munich, Germany
- Department of Physics, Ludwig Maximilians University, 85748, Munich, Germany
- European Synchrotron Radiation Facility, 38000, Grenoble, France
| | - Markus B Huber
- Departments of Imaging Sciences and Biomedical Engineering, University of Rochester, Rochester, NY, USA
| | - Paul C Diemoz
- Department of Physics, Ludwig Maximilians University, 85748, Munich, Germany
- European Synchrotron Radiation Facility, 38000, Grenoble, France
| | - Axel Wismüller
- Departments of Imaging Sciences and Biomedical Engineering, University of Rochester, Rochester, NY, USA
- Faculty of Medicine & Institute of Clinical Radiology, Ludwig Maximilians University, 80336, Munich, Germany
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Nagarajan MB, Coan P, Huber MB, Diemoz PC, Wismüller A. Integrating dimension reduction and out-of-sample extension in automated classification of ex vivo human patellar cartilage on phase contrast X-ray computed tomography. PLoS One 2015; 10:e0117157. [PMID: 25710875 PMCID: PMC4339581 DOI: 10.1371/journal.pone.0117157] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2014] [Accepted: 12/18/2014] [Indexed: 11/28/2022] Open
Abstract
Phase contrast X-ray computed tomography (PCI-CT) has been demonstrated as a novel imaging technique that can visualize human cartilage with high spatial resolution and soft tissue contrast. Different textural approaches have been previously investigated for characterizing chondrocyte organization on PCI-CT to enable classification of healthy and osteoarthritic cartilage. However, the large size of feature sets extracted in such studies motivates an investigation into algorithmic feature reduction for computing efficient feature representations without compromising their discriminatory power. For this purpose, geometrical feature sets derived from the scaling index method (SIM) were extracted from 1392 volumes of interest (VOI) annotated on PCI-CT images of ex vivo human patellar cartilage specimens. The extracted feature sets were subject to linear and non-linear dimension reduction techniques as well as feature selection based on evaluation of mutual information criteria. The reduced feature set was subsequently used in a machine learning task with support vector regression to classify VOIs as healthy or osteoarthritic; classification performance was evaluated using the area under the receiver-operating characteristic (ROC) curve (AUC). Our results show that the classification performance achieved by 9-D SIM-derived geometric feature sets (AUC: 0.96 ± 0.02) can be maintained with 2-D representations computed from both dimension reduction and feature selection (AUC values as high as 0.97 ± 0.02). Thus, such feature reduction techniques can offer a high degree of compaction to large feature sets extracted from PCI-CT images while maintaining their ability to characterize the underlying chondrocyte patterns.
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Affiliation(s)
- Mahesh B. Nagarajan
- Departments of Imaging Sciences and Biomedical Engineering, University of Rochester Medical Center, Rochester, New York, USA
- * E-mail:
| | - Paola Coan
- Faculty of Medicine and Institute of Clinical Radiology, Ludwig Maximilian University, Munich, Germany
- Faculty of Physics, Ludwig Maximilian University, Munich, Germany
- European Synchrotron Radiation Facility, Grenoble, France
| | - Markus B. Huber
- Departments of Imaging Sciences and Biomedical Engineering, University of Rochester Medical Center, Rochester, New York, USA
| | - Paul C. Diemoz
- Faculty of Physics, Ludwig Maximilian University, Munich, Germany
- European Synchrotron Radiation Facility, Grenoble, France
| | - Axel Wismüller
- Departments of Imaging Sciences and Biomedical Engineering, University of Rochester Medical Center, Rochester, New York, USA
- Faculty of Medicine and Institute of Clinical Radiology, Ludwig Maximilian University, Munich, Germany
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Nagarajan MB, De T, Lochmüller EM, Eckstein F, Wismüller A. Using Anisotropic 3D Minkowski Functionals for Trabecular Bone Characterization and Biomechanical Strength Prediction in Proximal Femur Specimens. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2014; 9038. [PMID: 29170581 DOI: 10.1117/12.2044352] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
The ability of Anisotropic Minkowski Functionals (AMFs) to capture local anisotropy while evaluating topological properties of the underlying gray-level structures has been previously demonstrated. We evaluate the ability of this approach to characterize local structure properties of trabecular bone micro-architecture in ex vivo proximal femur specimens, as visualized on multi-detector CT, for purposes of biomechanical bone strength prediction. To this end, volumetric AMFs were computed locally for each voxel of volumes of interest (VOI) extracted from the femoral head of 146 specimens. The local anisotropy captured by such AMFs was quantified using a fractional anisotropy measure; the magnitude and direction of anisotropy at every pixel was stored in histograms that served as a feature vectors that characterized the VOIs. A linear multi-regression analysis algorithm was used to predict the failure load (FL) from the feature sets; the predicted FL was compared to the true FL determined through biomechanical testing. The prediction performance was measured by the root mean square error (RMSE) for each feature set. The best prediction performance was obtained from the fractional anisotropy histogram of AMF Euler Characteristic (RMSE = 1.01 ± 0.13), which was significantly better than MDCT-derived mean BMD (RMSE = 1.12 ± 0.16, p<0.05). We conclude that such anisotropic Minkowski Functionals can capture valuable information regarding regional trabecular bone quality and contribute to improved bone strength prediction, which is important for improving the clinical assessment of osteoporotic fracture risk.
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Affiliation(s)
- Mahesh B Nagarajan
- Departments of Biomedical Engineering & Imaging Sciences, University of Rochester, USA
| | - Titas De
- Department of Electrical & Computer Engineering, University of Rochester, USA
| | | | - Felix Eckstein
- Institute of Anatomy, Paracelsus Medical University Salzburg, Salzburg, Austria
| | - Axel Wismüller
- Departments of Biomedical Engineering & Imaging Sciences, University of Rochester, USA
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Wang X, Nagarajan MB, Conover D, Ning R, O'Connell A, Wismüller A. Investigating the use of texture features for analysis of breast lesions on contrast-enhanced cone beam CT. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2014; 9038. [PMID: 29170583 DOI: 10.1117/12.2042397] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
Cone beam computed tomography (CBCT) has found use in mammography for imaging the entire breast with sufficient spatial resolution at a radiation dose within the range of that of conventional mammography. Recently, enhancement of lesion tissue through the use of contrast agents has been proposed for cone beam CT. This study investigates whether the use of such contrast agents improves the ability of texture features to differentiate lesion texture from healthy tissue on CBCT in an automated manner. For this purpose, 9 lesions were annotated by an experienced radiologist on both regular and contrast-enhanced CBCT images using two-dimensional (2D) square ROIs. These lesions were then segmented, and each pixel within the lesion ROI was assigned a label - lesion or non-lesion, based on the segmentation mask. On both sets of CBCT images, four three-dimensional (3D) Minkowski Functionals were used to characterize the local topology at each pixel. The resulting feature vectors were then used in a machine learning task involving support vector regression with a linear kernel (SVRlin) to classify each pixel as belonging to the lesion or non-lesion region of the ROI. Classification performance was assessed using the area under the receiver-operating characteristic (ROC) curve (AUC). Minkowski Functionals derived from contrast-enhanced CBCT images were found to exhibit significantly better performance at distinguishing between lesion and non-lesion areas within the ROI when compared to those extracted from CBCT images without contrast enhancement (p < 0.05). Thus, contrast enhancement in CBCT can improve the ability of texture features to distinguish lesions from surrounding healthy tissue.
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Affiliation(s)
- Xixi Wang
- Department of Biomedical Engineering, University of Rochester, NY, USA
| | | | - David Conover
- Department of Imaging Sciences, University of Rochester, NY, USA.,Koning Corporation, Rochester, NY, USA
| | - Ruola Ning
- Department of Imaging Sciences, University of Rochester, NY, USA.,Koning Corporation, Rochester, NY, USA
| | - Avice O'Connell
- Department of Imaging Sciences, University of Rochester, NY, USA
| | - Axel Wismüller
- Department of Biomedical Engineering, University of Rochester, NY, USA.,Department of Imaging Sciences, University of Rochester, NY, USA
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Yang CC, Nagarajan MB, Huber MB, Carballido-Gamio J, Bauer JS, Baum T, Eckstein F, Lochmüller EM, Link TM, Wismüller A. Predicting the Biomechanical Strength of Proximal Femur Specimens with Minkowski Functionals and Support Vector Regression. ACTA ACUST UNITED AC 2014; 9038. [PMID: 29170582 DOI: 10.1117/12.2041782] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
Regional trabecular bone quality estimation for purposes of femoral bone strength prediction is important for improving the clinical assessment of osteoporotic fracture risk. In this study, we explore the ability of 3D Minkowski Functionals derived from multi-detector computed tomography (MDCT) images of proximal femur specimens in predicting their corresponding biomechanical strength. MDCT scans were acquired for 50 proximal femur specimens harvested from human cadavers. An automated volume of interest (VOI)-fitting algorithm was used to define a consistent volume in the femoral head of each specimen. In these VOIs, the trabecular bone micro-architecture was characterized by statistical moments of its BMD distribution and by topological features derived from Minkowski Functionals. A linear multi-regression analysis and a support vector regression (SVR) algorithm with a linear kernel were used to predict the failure load (FL) from the feature sets; the predicted FL was compared to the true FL determined through biomechanical testing. The prediction performance was measured by the root mean square error (RMSE) for each feature set. The best prediction result was obtained from the Minkowski Functional surface used in combination with SVR, which had the lowest prediction error (RMSE = 0.939 ± 0.345) and which was significantly lower than mean BMD (RMSE = 1.075 ± 0.279, p<0.005). Our results indicate that the biomechanical strength prediction can be significantly improved in proximal femur specimens with Minkowski Functionals extracted from on MDCT images used in conjunction with support vector regression.
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Affiliation(s)
- Chien-Chun Yang
- Departments of Imaging Sciences & Biomedical Engineering, University of Rochester, New York, United States
| | - Mahesh B Nagarajan
- Departments of Imaging Sciences & Biomedical Engineering, University of Rochester, New York, United States
| | - Markus B Huber
- Departments of Imaging Sciences & Biomedical Engineering, University of Rochester, New York, United States
| | - Julio Carballido-Gamio
- Musculoskeletal and Quantitative Imaging Research, Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, United States
| | - Jan S Bauer
- Institute of Diagnostic Radiology, Technical University of Munich, Munich, Germany
| | - Thomas Baum
- Institute of Diagnostic Radiology, Technical University of Munich, Munich, Germany
| | - Felix Eckstein
- Institute of Anatomy, Paracelsus Medical University Salzburg, Salzburg, Austria
| | | | - Thomas M Link
- Musculoskeletal and Quantitative Imaging Research, Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, United States
| | - Axel Wismüller
- Departments of Imaging Sciences & Biomedical Engineering, University of Rochester, New York, United States
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Nagarajan MB, Coan P, Huber MB, Diemoz PC, Wismüller A. Phase contrast imaging X-ray computed tomography: Quantitative characterization of human patellar cartilage matrix with topological and geometrical features. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2014; 9038. [PMID: 28835728 DOI: 10.1117/12.2042395] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
Current assessment of cartilage is primarily based on identification of indirect markers such as joint space narrowing and increased subchondral bone density on x-ray images. In this context, phase contrast CT imaging (PCI-CT) has recently emerged as a novel imaging technique that allows a direct examination of chondrocyte patterns and their correlation to osteoarthritis through visualization of cartilage soft tissue. This study investigates the use of topological and geometrical approaches for characterizing chondrocyte patterns in the radial zone of the knee cartilage matrix in the presence and absence of osteoarthritic damage. For this purpose, topological features derived from Minkowski Functionals and geometric features derived from the Scaling Index Method (SIM) were extracted from 842 regions of interest (ROI) annotated on PCI-CT images of healthy and osteoarthritic specimens of human patellar cartilage. The extracted features were then used in a machine learning task involving support vector regression to classify ROIs as healthy or osteoarthritic. Classification performance was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC). The best classification performance was observed with high-dimensional geometrical feature vectors derived from SIM (0.95 ± 0.06) which outperformed all Minkowski Functionals (p < 0.001). These results suggest that such quantitative analysis of chondrocyte patterns in human patellar cartilage matrix involving SIM-derived geometrical features can distinguish between healthy and osteoarthritic tissue with high accuracy.
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Affiliation(s)
- Mahesh B Nagarajan
- Departments of Biomedical Engineering & Imaging Sciences, University of Rochester, New York, United States
| | - Paola Coan
- Faculty of Medicine & Institute of Clinical Radiology, Ludwig Maximilians University, Munich Germany.,Faculty of Physics, Ludwig Maximilians University, Munich 85748 Germany.,European Synchrotron Radiation Facility, Grenoble, France
| | - Markus B Huber
- Departments of Biomedical Engineering & Imaging Sciences, University of Rochester, New York, United States
| | - Paul C Diemoz
- Faculty of Physics, Ludwig Maximilians University, Munich 85748 Germany.,European Synchrotron Radiation Facility, Grenoble, France
| | - Axel Wismüller
- Departments of Biomedical Engineering & Imaging Sciences, University of Rochester, New York, United States.,Faculty of Medicine & Institute of Clinical Radiology, Ludwig Maximilians University, Munich Germany
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