Bone Age Assessment with less human intervention

Biomedical imaging allows doctors to examine the condition of a patient’s organs or tissues without a surgical procedure. Various modalities of imaging techniques have been developed, such as X-radiation (X-ray), Magnetic Resonance Imaging (MRI), and Computed Tomography (CT). For example, the Bone Age Assessment (BAA) evaluates the maturity in infants, children, and adolescents using their hand radiographs. It plays an essential role in diagnosing a patient with growth disorders or endocrine disorders, such that needed treatments could be provided. Computer-aided diagnosis (CAD) systems have been introduced to extract features from regions of interest in this field automatically. Recently, several deep learning methods are proposed to perform automated bone age assessment by learning visual features. This study proposes a BAA model, including image preprocessing procedures and transfer learning with a limited number of annotated samples. The goal is to examine the efficiency of data augmentations by using a publicly available X-ray data set. The model achieves a comparable MAE of 5.8 months, RMSE of 7.3 months, and accuracy (within 1 year) of more than 90% on the data set. We also study whether generating samples by a Generative Adversarial Network could be a valuable technique for training the model and prevent it from overfitting when the samples are insufficient

A GOOD INITIAL GUESS FOR APPROXIMATING NONLINEAR OSCILLATORS BY THE HOMOTOPY PERTURBATION METHOD

A good initial guess and an appropriate homotopy equation are two main factors in applications of the homotopy perturbation method. For a nonlinear oscillator, a cosine function is used in an initial guess. This article recommends a general approach to construction of the initial guess and the homotopy equation. Duffing oscillator is adopted as an example to elucidate the effectiveness of the method

Bone age assessment from articular surface and epiphysis using deep neural networks

Bone age assessment is of great significance to genetic diagnosis and endocrine diseases. Traditional bone age diagnosis mainly relies on experienced radiologists to examine the regions of interest in hand radiography, but it is time-consuming and may even lead to a vast error between the diagnosis result and the reference. The existing computer-aided methods predict bone age based on general regions of interest but do not explore specific regions of interest in hand radiography. This paper aims to solve such problems by performing bone age prediction on the articular surface and epiphysis from hand radiography using deep convolutional neural networks. The articular surface and epiphysis datasets are established from the Radiological Society of North America (RSNA) pediatric bone age challenge, where the specific feature regions of the articular surface and epiphysis are manually segmented from hand radiography. Five convolutional neural networks, i.e., ResNet50, SENet, DenseNet-121, EfficientNet-b4, and CSPNet, are employed to improve the accuracy and efficiency of bone age diagnosis in clinical applications. Experiments show that the best-performing model can yield a mean absolute error (MAE) of 7.34 months on the proposed articular surface and epiphysis datasets, which is more accurate and fast than the radiologists. The project is available at https://github.com/YameiDeng/BAANet/, and the annotated dataset is also published at https://doi.org/10.5281/zenodo.7947923

Carpal Bone Analysis using Geometric and Deep Learning Models

The recent trend for analyzing 3D shapes in medical application has arisen new challenges for a vast amount of research activities. Quantitative shape comparison is a fundamental problem in computer vision, geometry processing and medical imaging. This thesis is motivated by the availability of carpal bone shape dataset to develop efficient techniques for diagnosis of a variety of wrist diseases and examine human skeletal. This study is conducted in two sections. First, we propose a spectral graph wavelet approach for shape analysis of carpal bones of the human wrist. More precisely, we employ spectral graph wavelets to represent the cortical surface of a carpal bone via the spectral geometric analysis of the Laplace-Beltrami operator in the discrete domain. We then propose global spectral graph wavelet (GSGW) descriptor that is isometric invariant, efficient to compute and combines the advantages of both low-pass and band-pass filters. Subsequently, we perform experiments on shapes of the carpal bones of ten women and ten men from a publicly-available database of wrist bones. Using one-way multivariate analysis of variance (MANOVA) and permutation testing, we show through extensive experiments that the proposed GSGW framework gives a much better performance compared to the global point signature (GPS) embedding approach for comparing shapes of the carpal bones across populations. In the second section, we evaluate bone age to assess children’s biological maturity and to diagnose any growth disorders in children. Manual bone age assessment (BAA) methods are timeconsuming and prone to observer variability by even expert radiologists. These drawbacks motivate us for proposing an accurate computerized BAA method based on human wrist bones X-ray images. We also investigate automated BAA methods using state-of-the-art deep learning models that estimate the bone age more accurate than the manual methods by eliminating human observation variations. The presented approaches provide faster assessment process and cost reduction in the hospitals/clinics. The accuracy of our experiments is evaluated using mean absolute error (MAE), and the results demonstrate that exploiting InceptionResNet-V2 model in our architecture achieves higher performance compared to the other used pre-trained models

Towards fully automated third molar development staging in panoramic radiographs

Staging third molar development is commonly used for age assessment in sub-adults. Current staging techniques are, at most, semi-automated and rely on manual interactions prone to operator variability. The aim of this study was to fully automate the staging process by employing the full potential of deep learning, using convolutional neural networks (CNNs) in every step of the procedure. The dataset used to train the CNNs consisted of 400 panoramic radiographs (OPGs), with 20 OPGs per developmental stage per sex, staged in consensus between three observers. The concepts of transfer learning, using pre-trained CNNs, and data augmentation were used to mitigate the issues when dealing with a limited dataset. In this work, a three-step procedure was proposed and the results were validated using fivefold cross-validation. First, a CNN localized the geometrical center of the lower left third molar, around which a square region of interest (ROI) was extracted. Second, another CNN segmented the third molar within the ROI. Third, a final CNN used both the ROI and the segmentation to classify the third molar into its developmental stage. The geometrical center of the third molar was found with an average Euclidean distance of 63 pixels. Third molars were segmented with an average Dice score of 93%. Finally, the developmental stages were classified with an accuracy of 54%, a mean absolute error of 0.69 stages, and a linear weighted Cohen’s kappa coefficient of 0.79. The entire automated workflow on average took 2.72 s to compute, which is substantially faster than manual staging starting from the OPG. Taking into account the limited dataset size, this pilot study shows that the proposed fully automated approach shows promising results compared with manual staging.Internal Funds KU Leuvenhttp://link.springer.com/journal/4142021-04-01hj2020Anatom

Ultrasound volume projection image quality selection by ranking from convolutional RankNet.

Periodic inspection and assessment are important for scoliosis patients. 3D ultrasound imaging has become an important means of scoliosis assessment as it is a real-time, cost-effective and radiation-free imaging technique. With the generation of a 3D ultrasound volume projection spine image using our Scolioscan system, a series of 2D coronal ultrasound images are produced at different depths with different qualities. Selecting a high quality image from these 2D images is the crucial task for further scoliosis measurement. However, adjacent images are similar and difficult to distinguish. To learn the nuances between these images, we propose selecting the best image automatically, based on their quality rankings. Here, the ranking algorithm we use is a pairwise learning-to-ranking network, RankNet. Then, to extract more efficient features of input images and to improve the discriminative ability of the model, we adopt the convolutional neural network as the backbone due to its high power of image exploration. Finally, by inputting the images in pairs into the proposed convolutional RankNet, we can select the best images from each case based on the output ranking orders. The experimental result shows that convolutional RankNet achieves better than 95.5% top-3 accuracy, and we prove that this performance is beyond the experience of a human expert

Tecnicas de Deep Learning para a determinação da idade óssea

Dissertação de mestrado de integrado em Engenharia InformáticaA avaliação da idade óssea (a maturação esquelética) é uma prática clínica comum para investigar doenças endocrinológicas, genéticas e de crescimento em crianças. Geralmente é realizada por exame radiológico da mão esquerda usando o método Greulich e Pyle (G & P) ou o Tanner Whitehouse (TW). No entanto, ambos procedimentos clínicos demonstraram várias limitações, desde o esforço do exame que tem que ser feito pelos radiologistas até a significativa variabilidade intra e inter-operador. Para resolver este problema, várias abordagens com recurso a sistemas de apoio ao diagnóstico médico (especialmente tomando como base o método TW) foram propostas. Nenhum deles demonstrou capacidades de generalização para diferentes raças, faixas etárias e géneros. A avaliação de exames radiológicos requer a análise de um profissional com a máxima atenção. No caso do método de Greulich e Pyle a radiografia da mão do paciente é comparada com um atlas padrão sendo possível observar deficiências no crescimento dos pacientes. Este é um trabalho exaustivo e sujeito a erros devido ao nível de atenção que é necessário durante o diagnóstico. Os métodos de deep learning têm sido aplicados a diversas tarefas de análise de imagem médica como, por exemplo, classificação de lesões e segmentação de tecidos. O principal objectivo deste trabalho e desenvolver um modelo capaz de automaticamente determinar a idade óssea. Neste trabalho foram primeiramente testadas várias arquitecturas de redes neuronais convolucionais na determinação da idade óssea que mostraram bons resultados em tarefas comuns de visão por computador. Baseado nos resultados obtidos foi desenvolvido/optimizado um novo modelo que é apresentado neste documento. Foi usado transfer learning e o treino de raiz nas redes neuronais seleccionadas obtendo uma taxa de erro de 7.89 meses na determinação da idade óssea em pacientes do sexo feminino e uma taxa de erro de 8,28 meses ao executar esta tarefa em homens.Bone age assessment is a common clinical practice to detect endocrinological, genetic and growth diseases. Usually it’s performed using a x-ray image of the non dominant hand applying the Greulich and Pyle or the Tanner Whitehouse (TW) method. However both procedures showed to possess several limitations since the effort deman ded to radiologists to the significant intra- and inter-operator variability. To address these problems, several automated approaches (especially relying on the TW method) have been proposed. None of them showed to be able to generalize to different races, age ranges and genders. The evaluation of x-ray images requires the analysis of a professional with maximum levels of attention. The Greulich and Pyle method consists in comparing the image with an atlas being able to observe disabilities in the growing of pacients. This is an intensive job and error-prone due to the level of attention that is needed during the diagnosis. Deep Learning methods have been applied to different medical imaging analysis tasks like, for e.g., lesion classification and tissue segmentation. The main objective of this dis sertation is to develop a model capable of automatically assess bone age. In this work, we first have tested several state-of-the-art Convolution Neural Networks models for assessing bone age that previously has shown great results in general computer vision tasks. Ba sed on these results, we have developed/optimized a new model, which is presented here. For this purpose, we used transfer learning methods and trained the selected networks from scratch achieving a 7.89-month error rate when assessing bone age in females and 8.28-month error rate when performing this task on men

FRACTAL SPACE BASED DIMENSIONLESS ANALYSIS OF THE SURFACE SETTLEMENT INDUCED BY THE SHIELD TUNNELING

The surface settlement during the tunneling process is becoming increasingly difficult to forecast as its surroundings become more and more erratic, and the maximal surface settlement raises risks posed suddenly by various uncertain factors. This paper proposes a novel approach to prediction of the surface settlement and analyzes the stability of tunnel construction. The dimensionless analysis and Buckingham’s π-theorem are adopted for this purpose, and some useful dimensionless quantities are found, which can be used to determine the surface settlement’s main properties. In this manner, the paper offers new ways of predicting surface settlement in various cases, and it sheds a new light on the tunnel’s design and safety monitoring

Deep Learning for Predicting Congestive Heart Failure

Congestive heart failure (CHF) is one of the most debilitating cardiac disorders. It is a costly disease in terms of both lives and financial outlays, given the high rate of hospital re-admissions and mortality. Heart failure (HF) is notoriously difficult to identify on time, and is frequently accompanied by additional comorbidities that further complicate diagnosis. Many decision support systems (DSS) have been developed to facilitate diagnosis and to raise the standard of screening and monitoring operations, even for non-expert staff. This is confirmed in the literature by records of highly performing diagnosis-aid systems, which are unfortunately not very relevant to expert cardiologists. In order to assist cardiologists in predicting the trajectory of HF, we propose a deep learning-based system which predicts severity of disease progression by employing medical patient history. We tested the accuracy of four models on a labeled dataset, composed of 1037 records, to predict CHF severity and progression, achieving results comparable to studies based on much larger datasets, none of which used longitudinal multi-class prediction. The main contribution of this work is that it demonstrates that a fairly complicated approach can achieve good results on a medium size dataset, providing a reasonably accurate means of determining the evolution of CHF well in advance. This potentially constitutes a significant aid for healthcare managers and expert cardiologists in designing different therapies for medication, healthy lifestyle changes and quality of life (QoL) management, while also promoting allocation of resources with an evidence-based approach. © 2022 by the authors