Automated Human Bone Age Assessment using Image Processing Methods - Survey

Rajitha, B.; Agarwal, Suneeta

doi:10.5120/18265-9259

Cited by 8 publications

(6 citation statements)

References 48 publications

(32 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thus, our review of related research primarily focuses on deep-learning-based approaches. Comprehensive surveys on computerized methods for BAA are provided in [14] and [15].…”

Section: Related Workmentioning

confidence: 99%

“…Consequently, failure to acquire the precise location information of a growth region of any single ROI can degrade assessment accuracy. Although segmentation algorithms based on conventional image processing techniques have been proposed to detect the contours of bones, they are not adequately robust to handle bones of varying sizes, shapes, morphologies, and densities [14], [15]. Second, the lack of large-scale labeled training datasets is another critical obstacle to the development of TW3-based fully automated BAA systems.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

TW3-Based Fully Automated Bone Age Assessment System Using Deep Neural Networks

et al. 2019

View full text Add to dashboard Cite

Deep learning technology has rapidly evolved in recent years. Bone age assessment (BAA) is a typical object detection and classification problem that would benefit from deep learning. Convolutional neural networks (CNNs) and their variants are hence increasingly used for automating BAA, and they have shown promising results. In this paper, we propose a complete end-to-end BAA system to automate the entire process of the Tanner-Whitehouse 3 method, starting from localization of the epiphysis-metaphysis growth regions within 13 different bones and ending with estimation of the corresponding BA. Specific modifications to the CNNs and other stages are proposed to improve results. In addition, an annotated database of 3300 X-ray images is built to train and evaluate the system. The experimental results show that the average top-1 and top-2 prediction accuracies for skeletal bone maturity levels for 13 regions of interest are 79.6% and 97.2%, respectively. The mean absolute error and root mean squared error in age prediction are 0.46 years and 0.62 years, respectively, and accuracy within one year of the ground truth of 97.6% is achieved. The proposed system is shown to outperform a commercially available Greulich-Pyle-based system, demonstrating the potential for practical clinical use. INDEX TERMS Bone age assessment, deep learning, GP, TW3.

show abstract

“…Thus, our review of related research primarily focuses on deep-learning-based approaches. Comprehensive surveys on computerized methods for BAA are provided in [14] and [15].…”

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

TW3-Based Fully Automated Bone Age Assessment System Using Deep Neural Networks

et al. 2019

View full text Add to dashboard Cite

show abstract

“…As a result the highest demands for new technology are around solutions that drastically improve workflow while maintaining or improving quality of care, building in effciencies for the clinician, health system, and ultimately for the patient. Consequently, technologies that can aid in automating the medical imaging workflow are in high demand and have inspired advances in machine learning methods which show promise in assisting radiologists to analyze complex imaging and text data [2,3,4,5,6]. Yet, despite the rapid exploration around new machine learning tools for use in medical imaging diagnostic tasks, a significant barrier remains for this technology to be applied at scale: the diagnostic information for the imaging studies are contained within unstructured clinician-created free text reports.…”

Section: Introductionmentioning

confidence: 99%

“…Recent advances in computing power and machine learning techniques have prompted the rise of a new generation of machine learning technique called convolutional neural networks (CNNs) that have successfully been applied to image recognition tasks, including facial recognition, object detection, and image classification [6,19]. CNNs and other similar deep learning architectures have significant implications for diagnostic imaging [5,20,4,21,22], but rather than being limited to image recognition tasks, it is possible that CNNs can also be applied to text data.…”

Section: Introductionmentioning

confidence: 99%

Comparative effectiveness of convolutional neural network (CNN) and recurrent neural network (RNN) architectures for radiology text report classification

Banerjee

Yuan

Chen

et al. 2019

Artificial Intelligence in Medicine

189

View full text Add to dashboard Cite

This paper explores cutting-edge deep learning methods for information extraction from medical imaging free text reports at a multi-institutional scale and compares them to the state-of-the-art domain-specific rule-based system -PEFinder and traditional machine learning methods -SVM and Adaboost. We proposed two distinct deep learning models -(i) CNN Word -Glove, and (ii) Domain phrase attention-based hierarchical recurrent neural network (DPA-HNN), for synthesizing information on pulmonary emboli (PE) from over 7,370 clin-ical thoracic computed tomography (CT) free-text radiology reports collected from four major healthcare centers. Our proposed DPA-HNN model encodes domain-dependent phrases into an attention mechanism and represents a radiology report through a hierarchical RNN structure composed of word-level, sentence-level and document-level representations. Experimental results suggest that the performance of the deep learning models that are trained on a single institutional dataset, are better than rule-based PEFinder on our multi-institutional test sets. The best F1 score for the presence of PE in an adult patient population was 0.99 (DPA-HNN) and for a pediatrics population was 0.99 (HNN) which shows that the deep learning models being trained on adult data, demonstrated generalizability to pediatrics population with comparable accuracy. Our work suggests feasibility of broader usage of neural network models in automated classification of multi-institutional imaging text reports for a variety of applications including evaluation of imaging utilization, g

show abstract

“…2 Its potential use in pediatric radiology for detecting endocrine dysfunction has also been explored. 3 Currently, left hand radiographs are widely utilized for BAA, with a focus on the morphological characteristics of bones such as phalanges and wrists. Traditionally, the Greulich and Pyle (GP) 4 or Tanner-Whitehouse 3 (TW3) approach has been employed for manual BAA.…”

Section: Introductionmentioning

confidence: 99%

SVTNet: Automatic bone age assessment network based on TW3 method and vision transformer

Wu,

Mi,

Zhang

et al. 2023

Int J Imaging Syst Tech

View full text Add to dashboard Cite

This study aims to develop a proficient and clinically applicable algorithm that can accurately assess bone age. This algorithm is based on the principles of the Tanner‐Whitehouse 3 (TW3) integral approach, and aims to achieve efficiency, scalability, and interpretability. We developed a model for bone age prediction in children. The model was tested on a pediatric dataset from a tertiary care hospital consisting of left‐hand radiographs of children between the age of 0 and 18. Our model consists of removing the arm portion using a pre‐trained YOLO network, localizing 37 key points in the hand bone portion using a spatial configuration network, and segmenting the original image through 20 of these points to obtain 20 fixed‐size patches. Finally, each of the 20 bone images is classified by training a visual transformer (ViT) model. In this study, a hybrid network, SVTNet, was developed that incorporates visual transformers to obtain estimates of bone age in the carpal (C series) and metacarpal (RUS series) bones. The sum of the clinical TW3 scoring region scores and bone maturity scores were utilized to determine the bone age for each corresponding region. The performance of the algorithm was evaluated in terms of both training and testing by evaluating 3871 left hand X‐ray micrographs obtained from a tertiary hospital in China. The results showed that the average absolute error of bone age estimation was 0.50 years for the RUS series of bones and 0.47 years for the C series of bones. The main contribution of this study is to propose, for the first time, a ViT‐based bone age assessment method that automates the entire process of the TW3 algorithm and is clinically interpretable, with predictive accuracy comparable to that of an experienced orthopedic surgeon.

show abstract

Automated Human Bone Age Assessment using Image Processing Methods - Survey

Cited by 8 publications

References 48 publications

TW3-Based Fully Automated Bone Age Assessment System Using Deep Neural Networks

TW3-Based Fully Automated Bone Age Assessment System Using Deep Neural Networks

Comparative effectiveness of convolutional neural network (CNN) and recurrent neural network (RNN) architectures for radiology text report classification

SVTNet: Automatic bone age assessment network based on TW3 method and vision transformer

Contact Info

Product

Resources

About