Convolutional neuronal networks combined with X-ray phase-contrast imaging for a fast and observer-independent discrimination of cartilage and liver diseases stages

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 0.7x0.7x0.7 mu m(3) and 2.2x2.2x2.2 mu m(3), respectively. Images with a size of 224x224 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

A multi-scale imaging approach to understand osteoarthritis development

X-ray phase-contrast imaging is an innovative and advanced imaging method. Contrary to conventional radiology, where the image contrast is primarily determined by X-ray attenuation, phase-contrast images contain additional information generated by the phase shifts or refraction of the X-rays passing through matter. The refractive effect on tissue samples is orders of magnitude higher than the absorption effect in the X-ray energy range used in biomedical imaging. This technique makes it possible to produce excellent and enhanced image contrast, particularly when examining soft biological tissues or features with similar X-ray attenuation properties. In combination with high spatial resolution detector technology and computer tomography, X-ray phase-contrast imaging has been proved to be a powerful method to examine tissue morphology and the evolution of pathologies three-dimensionally, with great detail and without the need of contrast agents. This Thesis work has focused on developing an accurate, multi-scale X-ray-based methodology for imaging and characterizing the early stages of osteoarthritis. X-ray phase-contrast images acquired at different spatial resolutions provide unprecedented insights into cartilage and the development of its degeneration, i.e., osteoarthritis. Other types of X-ray phase-contrast imaging techniques and setups using spatial resolutions ranging from micrometer down to nanometer were applied. Lower spatial resolutions allow large sample coverage and comprehensive representations, while the nanoscale analysis provides a precise depiction of anatomical details and pathological signs. X-ray phase-contrast results are correlated to data obtained, on the same specimens, by standard laboratory methods, such as histology and transmission electron microscopy. Furthermore, X-ray phase-contrast images of cartilage were acquired using different X-ray sources and results were compared in terms of image quality. It was shown that with the use of synchrotron radiation, more detailed images and much faster data acquisitions could be achieved. A second focus in this Thesis work has been the investigation of the reaction of healthy and degenerated cartilage under different physical pressures, simulating the different levels of stress to which the tissue is subject during daily movements. A specifically designed setup was used to dynamically study cartilage response to varying pressures with X-ray phase-contrast micro-computed tomography, and a fully volumetric and quantitative methodology to accurately describe the tissue morphological variations. This study revealed changes in the behavior of the cartilage cell structure, which differ between normal and osteoarthritic cartilage tissues. The third focus of this Thesis is the realization of an automated evaluation procedure for the discrimination of healthy and cartilage images with osteoarthritis. In recent years, developments in neural networks have shown that they are excellently suited for image classification tasks. The transfer learning method was applied, in which a pre-trained neural network with cartilage images is further trained and then used for classification. This enables a fast, robust and automated grouping of images with pathological findings. A neural network constructed in this way could be used as a supporting instrument in pathology. X-ray phase-contrast imaging computed tomography can provide a powerful tool for a fully 3D, highly accurate and quantitative depiction and characterization of healthy and early stage-osteoarthritic cartilage, supporting the understanding of the development of osteoarthritis.Röntgen-Phasenkontrast-Bildgebung ist eine innovative und weiterführende Bildgebungsmethode. Im Gegensatz zu herkömlichen Absorptions-Röntgenaufnahmen, wie sie in der Radiologie verwendet werden, wird der Kontrast bei dieser Methode aus dem Effekt der Phasenverschiebung oder auch Brechung der Röngtenstrahlen gebildet. Der Brechungseffekt bei Gewebeproben ist um ein Vielfaches höher als der Absorptionseffekt des elektromagnetischen Spektrums der Röntgenstrahlen. Diese Methode ermöglicht die Darstellung von großen Kontraste im Gewebe. Unter Verwendung eines hochauflösenden Detektors und in Kombination mit der Computer-Tomographie, ist Phasenkontrast-Bildgebung eine sehr gute Methode um Knorpelgewebe und Arthrose im Knorpel zu untersuchen. Diese Arbeit beschreibt primär ein Verfahren zur Darstellung arthrotischen Knorpels im Anfangsstadium. Die mit verschiedenen Auflösungen und 3D-Phasen-Kontrast-Methoden produzierten Aufnahmen ermöglichen einen noch nie dagewesenen Einblick in den Knorpel und die Entwicklung von Arthrose im Anfangsstadium. Hierbei kam die propagationsbasierte Phasenkontrastmethode mit einer Auflösung im mikrometer Bereich und die (Nano)-Holotomographie-Methode mit einer Auflösung im Submicrometer Bereich zum Einsatz. Durch Auflösung im mikrometer Bereich kann ein großes Volumen im Knorpel gescannt werden, während die Nano-Holotomographie Methode eine sehr große Detailauflösung aufweißt. Die Phasenkontrast-Aufnahmen werden mit zwei anderen wissenschaftlichen Methoden verglichen: mikroskopische Abbildungen histologisch aufgearbeiteter Knorpelproben und Aufnahmen eines Transmissionselektroskop zeigen sehr große Übereinstimmungen zur Röntgen-Phasenkontrast-Bildgebung. Desweiteren wurden Phasenkontrast-Aufnahmen von Knorpel aus unterschiedlichen Röntgenquellen verglichen. Hierbei zeigte sich, dass mit Hilfe des Teilchenbeschleunigers (Synchrotron) detailreichere und schnellere Aufnahmen erzielt werden können. Bilder aus Flüssig-Metall-Quellen zeigen sich durchaus von guter Qualität, erfordern jedoch sehr lange Aufnahmezeiten. In dieser Arbeit wird zudem das Verhalten von Knorpelgewebe, welches ein Anfangsstadium von Arthrose aufweist, unter physikalischem Druck untersucht. Hierfür wurden 3D-Computertomographie-Aufnahmen von komprimiertem Knorpelgewebe angefertig und mit Aufnahmen ohne Komprimierung verglichen. Ein quantitativer Vergleich machte Veränderungen des Verhaltens der Knorpelzellstruktur (Chondronen) sichtbar. Es konnte gezeigt werden, dass Chondrone bei arthrotischem Knorpel ein verändertes Kompressionsverhalten haben. Der dritte Fokus dieser Arbeit liegt auf der automatisierten Auswertung von Aufnahmen gesunden und arthrotischen Knorpelgewebes. Die Entwicklungen im Bereich der Neuronale Netze zeigten in den letzten Jahren, dass diese sich hervoragend für Bildklassifizierungsaufgaben eignen. Es wurde die Methode des transferierenden Lernens angewandt, bei der ein vortrainiertes Neuronales Netz mit Knorpelbildern weitertrainiert und anschließend zur Klassifizierung eingesetzt wird. Dadurch ist eine schnelle, robuste und automatisierte Gruppierung von Bildern mit pathologischen Befunden möglich. Ein derart konstruiertes Neuronales Netz könnte als unterstützendes Instrument in der Pathologie angewandt werden. Röntgen-Phasenkontrast-CT kann ein leistungsstarkes Werkzeug für eine umfassende, hochpräzise und quantitative 3D-Darstellung und Charakterisierung von gesundem Knorpel und athrotischem Knorpel im Frühstadium bieten, um das Verständnis der Entwicklung von Osteoarthritis zu erweitern

Developing a cationic contrast agent for computed tomographic imaging of articular cartilage and synthetic biolubricants for early diagnosis and treatment of osteoarthritis

Osteoarthritis (OA) causes debilitating pain for millions of people, yet OA is typically diagnosed late in the disease process after severe damage to the articular cartilage has occurred and few treatment options exist. Furthermore, destructive techniques are required to measure cartilage biochemical and mechanical properties for studying cartilage function and changes during OA. Hence, research and clinical needs exist for non-destructive measures of cartilage properties. Various arthroscopic (e.g., ultrasound probes) and imaging (e.g., MRI or CT) techniques are available for assessing cartilage less destructively. However, arthroscopic methods are limited by patient anesthesia/infection risks and cost, and MRI is hindered by high cost, long image acquisition times and low resolution. Contrast-enhanced CT (CECT) is a promising diagnostic tool for early-stage OA, yet most of its development work utilizes simplified and ideal cartilage models, and rarely intact, pre-clinical animal or human models. To advance CECT imaging for articular cartilage, this dissertation describes further development of a new cationic contrast agent (CA4+) for minimally-invasive assessment of cartilage biochemical and mechanical properties, including glycosaminoglycan content, compressive modulus, and coefficient of friction. Specifically, CA4+ enhanced CT is compared to these three cartilage properties initially using an ideal bovine osteochondral plug model, then the technique is expanded to examine human finger joints and both euthanized and live mouse knees. Furthermore, CECT attenuations with CA4+ map bovine meniscal GAG content and distribution, signifying CECT can evaluate multiple tissues involved in OA. CECT's sensitivity to critical cartilage and meniscal properties demonstrates its applicability as both a non-destructive research tool as well as a method for diagnosing and monitoring early-stage OA. Additionally, CECT enables evaluation of efficacy for a new biolubricant (2M TEG) for early-stage OA treatment. In particular, CECT can detect the reduced wear on cartilage surfaces for samples tested in 2M TEG compared to samples tested in saline (negative control). With its sensitivity to cartilage GAG content, surface roughness, and mechanical properties, CA4+ enhanced CT will serve as a valuable tool for subsequent in vivo animal and clinical use

Deep Learning Algorithms with Demographic Information Help to Detect Tuberculosis in Chest Radiographs in Annual Workers' Health Examination Data

We aimed to use deep learning to detect tuberculosis in chest radiographs in annual workers' health examination data and compare the performances of convolutional neural networks (CNNs) based on images only (I-CNN) and CNNs including demographic variables (D-CNN). The I-CNN and D-CNN models were trained on 1000 chest X-ray images, both positive and negative, for tuberculosis. Feature extraction was conducted using VGG19, InceptionV3, ResNet50, DenseNet121, and InceptionResNetV2. Age, weight, height, and gender were recorded as demographic variables. The area under the receiver operating characteristic (ROC) curve (AUC) was calculated for model comparison. The AUC values of the D-CNN models were greater than that of I-CNN. The AUC values for VGG19 increased by 0.0144 (0.957 to 0.9714) in the training set, and by 0.0138 (0.9075 to 0.9213) in the test set (both p < 0.05). The D-CNN models show greater sensitivity than I-CNN models (0.815 vs. 0.775, respectively) at the same cut-off point for the same specificity of 0.962. The sensitivity of D-CNN does not attenuate as much as that of I-CNN, even when specificity is increased by cut-off points. Conclusion: Our results indicate that machine learning can facilitate the detection of tuberculosis in chest X-rays, and demographic factors can improve this process.ope

Decomposing 3D Neuroimaging into 2+1D Processing for Schizophrenia Recognition

Deep learning has been successfully applied to recognizing both natural images and medical images. However, there remains a gap in recognizing 3D neuroimaging data, especially for psychiatric diseases such as schizophrenia and depression that have no visible alteration in specific slices. In this study, we propose to process the 3D data by a 2+1D framework so that we can exploit the powerful deep 2D Convolutional Neural Network (CNN) networks pre-trained on the huge ImageNet dataset for 3D neuroimaging recognition. Specifically, 3D volumes of Magnetic Resonance Imaging (MRI) metrics (grey matter, white matter, and cerebrospinal fluid) are decomposed to 2D slices according to neighboring voxel positions and inputted to 2D CNN models pre-trained on the ImageNet to extract feature maps from three views (axial, coronal, and sagittal). Global pooling is applied to remove redundant information as the activation patterns are sparsely distributed over feature maps. Channel-wise and slice-wise convolutions are proposed to aggregate the contextual information in the third view dimension unprocessed by the 2D CNN model. Multi-metric and multi-view information are fused for final prediction. Our approach outperforms handcrafted feature-based machine learning, deep feature approach with a support vector machine (SVM) classifier and 3D CNN models trained from scratch with better cross-validation results on publicly available Northwestern University Schizophrenia Dataset and the results are replicated on another independent dataset

Acoustic Emission Measurement System in Diagnostic of Cartilage Injuries of the Knee

Abstract The measurement system BONEDIAS (Bone Diagnostic System) was developed as a non-invasive diagnostic method, based on the analysis on the acoustic emission from the knee joint. Knee squats of a patient will release acoustic emission in high temporal resolution and well correlated to the angle of knee flexion. The physician will get the relevant information concerning arthritic lesions in the knee joint (well characterized acoustic emission, singular events without a follow up of further emission), acoustic emission due to elevated intra-articular friction caused by e.g. cartilage lesions, inappropriate surface roughness, a lack of synovial fluid or crack initiation in the femur. Over 100 patients were analyzed with the measurement system BONEDIAS, afterwards the results were compared with the intra-operative views (arthroscopy and arthroplasty of the knee). Other parameters were studied, concerning the relation between the age and the sex of the subjects, the length of the femur, thigh thickness, the body mass index, the anatomical axis of the knee and the appearance and severity of the cartilage lesions. The study was made with the purpose to see if there was a correspondence between the cartilage disorders, the intraoperative views (arthroscopy and the arthroplasty of the knee) and the acoustic emission measurements, performed one day before the surgery. Because there arent at this moment cheap and standards methods who can determine the early cartilage injuries, this study is supposed (concording with the results) to open new ideas and new advantages in the diagnostic of this often disease, using the acoustic emission measurement system. The results obtained, 50% correspondence for the gr. 0, I and II Outerbridge lesions are more important, more significant that the other results, with over 60% correspondence for the advanced osteoarthrosis. The obtained acoustic emission signals, corresponding to the intra-arthroscopic findings showed the importance of this method to identify the early cartilage injuries. The method is not perfect and the results (50%) are not really statistically significant, so that we can introduce this method on a large scale, but offers important information that should be used in the future. Also, there isn’t a perfect method to compare the acoustic emission signals with the intra-arthroscopic findings. Every patient was analysed separately and with his corresponding measurement compared, that means a lot of time (20 – 30 minutes for the measurement and the other questions and clinical tests and another 15 minutes to analyse the signals and compare them with the intra-operative findings). For a study this can be accepted, but for clinical every day use maybe not. A standard interpretation and analyse method, maybe after clinical large trials, if such a method can be developed, could bring big advantages for the early determination of the cartilage injuries. In conclusion, the study had offered important informations about the importance of accoustic emission measurements, that can be used for the future studies and with some improvements, this method , cheap and non-invasive, but at this moment a little beat time-consuming, can be helpful in the diagnose of the early cartilage injuries