Pediatric Bone Age Assessment Using Deep Convolutional Neural Networks

Skeletal bone age assessment is a common clinical practice to diagnose endocrine and metabolic disorders in child development. In this paper, we describe a fully automated deep learning approach to the problem of bone age assessment using data from Pediatric Bone Age Challenge organized by RSNA 2017. The dataset for this competition is consisted of 12.6k radiological images of left hand labeled by the bone age and sex of patients. Our approach utilizes several deep learning architectures: U-Net, ResNet-50, and custom VGG-style neural networks trained end-to-end. We use images of whole hands as well as specific parts of a hand for both training and inference. This approach allows us to measure importance of specific hand bones for the automated bone age analysis. We further evaluate performance of the method in the context of skeletal development stages. Our approach outperforms other common methods for bone age assessment.Comment: 14 pages, 9 figure

Predictive Modelling of Bone Age through Classification and Regression of Bone Shapes

Bone age assessment is a task performed daily in hospitals worldwide. This involves a clinician estimating the age of a patient from a radiograph of the non-dominant hand. Our approach to automated bone age assessment is to modularise the algorithm into the following three stages: segment and verify hand outline; segment and verify bones; use the bone outlines to construct models of age. In this paper we address the final question: given outlines of bones, can we learn how to predict the bone age of the patient? We examine two alternative approaches. Firstly, we attempt to train classifiers on individual bones to predict the bone stage categories commonly used in bone ageing. Secondly, we construct regression models to directly predict patient age. We demonstrate that models built on summary features of the bone outline perform better than those built using the one dimensional representation of the outline, and also do at least as well as other automated systems. We show that models constructed on just three bones are as accurate at predicting age as expert human assessors using the standard technique. We also demonstrate the utility of the model by quantifying the importance of ethnicity and sex on age development. Our conclusion is that the feature based system of separating the image processing from the age modelling is the best approach for automated bone ageing, since it offers flexibility and transparency and produces accurate estimate

Doctor Imitator: Hand-Radiography-based Bone Age Assessment by Imitating Scoring Methods

Bone age assessment is challenging in clinical practice due to the complicated bone age assessment process. Current automatic bone age assessment methods were designed with rare consideration of the diagnostic logistics and thus may yield certain uninterpretable hidden states and outputs. Consequently, doctors can find it hard to cooperate with such models harmoniously because it is difficult to check the correctness of the model predictions. In this work, we propose a new graph-based deep learning framework for bone age assessment with hand radiographs, called Doctor Imitator (DI). The architecture of DI is designed to learn the diagnostic logistics of doctors using the scoring methods (e.g., the Tanner-Whitehouse method) for bone age assessment. Specifically, the convolutions of DI capture the local features of the anatomical regions of interest (ROIs) on hand radiographs and predict the ROI scores by our proposed Anatomy-based Group Convolution, summing up for bone age prediction. Besides, we develop a novel Dual Graph-based Attention module to compute patient-specific attention for ROI features and context attention for ROI scores. As far as we know, DI is the first automatic bone age assessment framework following the scoring methods without fully supervised hand radiographs. Experiments on hand radiographs with only bone age supervision verify that DI can achieve excellent performance with sparse parameters and provide more interpretability.Comment: Original Title: "Doctor Imitator: A Graph-based Bone Age Assessment Framework Using Hand Radiographs" @inproceedings{chen2020doctor, title={Doctor imitator: A graph-based bone age assessment framework using hand radiographs}, author={Chen, Jintai and Yu, Bohan and Lei, Biwen and Feng, Ruiwei and Chen, Danny Z and Wu, Jian}, booktitle={MICCAI}, year={2020}

Predictive Modelling of Bone Ageing

Bone age assessment (BAA) is a task performed daily by paediatricians in hospitalsworldwide. The main reasons for BAA to be performed are: fi�rstly, diagnosis of growth disorders through monitoring skeletal development; secondly, prediction of final adult height; and fi�nally, verifi�cation of age claims. Manually predicting bone age from radiographs is a di�fficult and time consuming task. This thesis investigates bone age assessment and why automating the process will help. A review of previous automated bone age assessment systems is undertaken and we investigate why none of these systems have gained widespread acceptance. We propose a new automated method for bone age assessment, ASMA (Automated Skeletal Maturity Assessment). The basic premise of the approach is to automatically extract descriptive shape features that capture the human expertise in forming bone age estimates. The algorithm consists of the following six modularised stages: hand segmentation; hand segmentation classifi�cation; bone segmentation; feature extraction; bone segmentation classifi�cation; bone age prediction. We demonstrate that ASMA performs at least as well as other automated systems and that models constructed on just three bones are as accurate at predicting age as expert human assessors using the standard technique. We also investigate the importance of ethnicity and gender in skeletal development. Our conclusion is that the feature based system of separating the image processing from the age modelling is the best approach, since it off�ers flexibility and transparency, and produces accurate estimates

Carpal-bone feature extraction analysis in skeletal age assessment based on deformable model

Skeletal age assessment is one of the important applications of hand radiography in the area of pediatric radiology. Features analysis of the carpal bones can reveal the important information for skeletal age assessment. The present work in this paper faces the problem of the detection of carpal-bone features from its radio-image. A novel and effective segmentation technique is presented in this work with carpal bone image for skeletal age estimation. Carpal bone segmentation is a critical operation of the automatic skeletal age assessment system. This method consists of three procedures. First, the original carpal bone image is preprocessed via anisotropic diffusion. Then, the carpal bone image is segmented by GVF-Snake model. Third, experiments are carried out on images of carpal bone. The results are very promising. In particular the method is able to extract overlapping carpal bones.Facultad de Informátic

Carpal-bone feature extraction analysis in skeletal age assessment based on deformable model

Skeletal age assessment is one of the important applications of hand radiography in the area of pediatric radiology. Features analysis of the carpal bones can reveal the important information for skeletal age assessment. The present work in this paper faces the problem of the detection of carpal-bone features from its radio-image. A novel and effective segmentation technique is presented in this work with carpal bone image for skeletal age estimation. Carpal bone segmentation is a critical operation of the automatic skeletal age assessment system. This method consists of three procedures. First, the original carpal bone image is preprocessed via anisotropic diffusion. Then, the carpal bone image is segmented by GVF-Snake model. Third, experiments are carried out on images of carpal bone. The results are very promising. In particular the method is able to extract overlapping carpal bones.Facultad de Informátic

The relationship between adiposity, bone density and microarchitecture is maintained in young women irrespective of diabetes status

Background: The relationship between bone health and adiposity and how it may be affected in people with chronic metabolic conditions is complex. Methods: 17 women with Type 1 diabetes mellitus (T1DM) and 9 age-matched healthy women with a median age of 22.6 yrs (range, 17.4, 23.8) were studied by 3T-MRI and MR spectroscopy to assess abdominal adiposity, tibial bone microarchitecture and vertebral bone marrow adiposity. Additional measures included DXA-based assessments of total body (TB), femoral neck (FN) and lumbar spine (LS) bone mineral density (BMD) and fat mass (FM). Results: Although women with T1DM had similar BMI and bone marrow adiposity to the controls, they had higher visceral and subcutaneous adiposity on MRI (p<0.05) and total body FM by DXA (p=0.03). Overall, in the whole cohort, a clear inverse association was evident between bone marrow adiposity and BMD at all sites (p<0.05). These associations remained significant after adjusting for age, BMI, FM, and abdominal adiposity. In addition, visceral adiposity, but not subcutaneous adiposity, showed a positive association with bone marrow adiposity (r,0.4, p=0.03), and a negative association with total body BMD (r,0.5, p=0.02). Apparent trabecular separation as assessed by MRI showed an inverse association to total body BMD by DXA (r,–0.4, p=0.04). Conclusion: Irrespective of the presence of an underlying metabolic condition, young women display a negative relationship between MRI-measured bone marrow adiposity and DXA-based assessment of bone mineral density. Furthermore, an association between bone marrow adiposity and visceral adiposity supports the notion of a common origin of these two fat depots

Bone Age practices in infants and older children among practicing radiologists in Pakistan: Developing world perspective

Objective To investigate which bone age assessment techniques are utilized by radiologists in Pakistan to determine skeletal age in three defined age groups: less than one year, one to three years and three to 18 years. We also assessed the perceived confidence in skeletal age assessments made by respondents using their chosen bone age assessment technique, within each defined age group. Materials and methods A cross-sectional survey was conducted among 147 practicing radiologists in Pakistan. A pre-validated survey form was adopted from a similar study conducted amongst members of the Society for Pediatric Radiology. The survey collected demographic information, choice of bone age assessment technique in each age group and confidence of bone age assessments in each age group. Results The hand-wrist method of Greulich and Pyle was used by 87.5% of respondents when assessing bone age in infants (less than one year), followed by Gilsanz-Ratib hand bone age method (7.3%). In children aged one to three years, Greulich and Pyle method was chosen by 85.7% of respondents, followed by Gilsanz-Ratib hand bone age method (6.1%) and the Hoerr, Pyle, Francis\u27 Radiographic Atlas of Skeletal Development of the Foot and Ankle (3.1%). In children, older than three years, the Greulich and Pyle technique was used by 83.7% of respondents. This was followed by Gilsanz-Ratib hand bone age method (5.8%) and the Hoerr, Pyle, Francis\u27 Radiographic Atlas of Skeletal Development of the Foot and Ankle (3.8%). 26.4% were very confident in bone age assessments conducted among infants. In children aged one to three years, 38.1% were very confident . In children, greater than three years, 48.6% were very confident in their chosen technique. Conclusion Greulich and Pyle is the dominant method for bone age assessments in all age groups, however, confidence in its application among infants and young children is low. It is recommended that clear recommendations be developed for bone age assessments in this age group alongside incorporation of indigenous standards of bone age assessments based on a representative sample of healthy native children