Pediatric Bone Age Assessment Using Deep Convolutional Neural Networks

Abstract

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.

Full text

Pediatric Bone Age Assessment Using Deep
Convolutional Neural Networks
Vladimir Iglovikov
1
, Alexander Rakhlin
2
, Alexandr Kalinin
3
, and Alexey Shvets
4
1Lyft Inc., San Francisco, CA 94107, USA
iglovikov@gmail.com
2
National Research University of Electronic Technology, Zelenograd, Moscow, Russia
rakhlin@gmx.net
3University of Michigan, Ann Arbor, MI 48109, USA
akalinin@umich.edu
4Massachusetts Institute of Technology, Cambridge, MA 02142, USA
shvets@mit.edu
Abstract.
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 the 2017 Pediatric Bone
Age Challenge organized by the Radiological Society of North America.
The dataset for this competition is consisted of 12.6k radiological images.
Each radiograph in this dataset is an image of a left hand labeled by the
bone age and the sex of a patient. Our approach utilizes several deep
neural network architectures 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 the 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.
Keywords:
Medical Imaging, Computer-aided diagnosis (CAD), Computer
Vision, Image Recognition, Deep Learning
1 Introduction
During organism development the bones of the skeleton change in size and shape,
and thus a difference between a child’s assigned bone and chronological ages
might indicate a growth problem. Clinicians use bone age assessment in order to
estimate the maturity of a child’s skeletal system. Bone age assessment methods
usually start with taking a single X-ray image of the left hand from the wrist to
fingertips, see Fig. . It is a safe and painless procedure that uses a small amount
of radiation. The bones on the X-ray image are compared with radiographs in
a standardized atlas of bone development. Such bone age atlases are based on
large numbers of radiographs collected from children of the same sex and age.

2 Iglovikov et al.
Over the past decades, the bone age assessment procedure has been performed
manually using either the Greulich and Pyle (GP) [] or Tanner-Whitehouse
(TW) [] methods. The GP procedure determines the bone age by comparing
the patient’s radiograph with an atlas of representative ages. The TWtechnique
is based on a scoring system that examines  specific bones. In both cases,
bone assessment procedure requires a considerable time. Only recently software
solutions, such as BoneXpert [], have been developed and approved for the
clinical use in Europe. BoneXpert uses the Active Appearance Model (AAM) [],
a computer vision algorithm, which reconstructs the contours of  bones of a
hand. Then the system determines the overall bone age according to their shape,
texture, and intensity based on the GP or TW techniques. The accuracy of this
system is around . years (.months). However, it is sensitive to the image
quality and does not utilize the carpal bones, despite their importance for skeletal
maturity assessment in infants and toddlers.
Recent advances in deep learning and its applications to computer vision
allowed many researchers to drastically improve results obtained with image
processing systems particularly related to medical image analyses []. Deep
learning based approaches are gaining more attention because in a number of cases
they were shown to achieve and even overcome human level performance, making
end-to-end image processing automated and sufficiently fast. In the domain of
medical imaging, convolutional neural networks (CNN) have been successfully
used for diabetic retinopathy screening [], heart disease diagnostics [], lung
cancer detection [], and other applications []. In the case of bone age assessment,
a manually performed procedure requires around  minutes of doctor’s time per
a patient. When the same procedure is done using software based on classical
Fig. 1.
Bones of a human hand and wrist (adapted from the Human Anatomy library [8]).

Automatic Bone Age Assessment with Deep Learning 3
Fig. 2. Bone age distribution for females and males in the training dataset.
computer vision methods, it takes -minutes, but still requires substantial
doctoral supervision and expertise. Deep learning based methods allow to avoid
feature engineering by automatically learning the hierarchy of discriminative
features directly from a set of labeled examples. Using a deep learning approach
processing of one image typically takes less than sec, while accuracy of these
methods in many cases exceeds that of conventional methods. Deep neural network
based solutions for bone age assessment from hand radiographs were suggested
before [,,]. However, these studies did not perform a numerical evaluation
of the performance of their models using different hand bones. In addition, we
find that the performance of deep learning models for bone age assessment can be
further improved with better preprocessing and training networks on radiographs
from scratch instead of fine-tuning from natural image domain.
In this paper, we present a novel deep learning based method for bone age
assessment. We validate the performance of this method using the data from
the  Pediatric Bone Age Challenge organized by the Radiological Society of
North America (RSNA) []. This data set is now freely available and can be
accessed at []. In our approach, we first preprocess radiographs by segmenting
the hand, normalizing contrast, detecting key points and using them to register
segmented hand images. Then, we train several deep network architectures
using different parts of images to evaluate how various bones contribute to the
models’ performance across four major skeletal development stages. Finally, we
compare predictions across different models and evaluate the overall performance
of our approach. We demonstrate that the suggested method is more robust and
demonstrates superior performance compared to other commonly used solutions.
2 Preprocessing
The goal of the first step in the preprocessing pipeline is to extract a region
of interest (a hand mask) from the image and remove all extraneous objects.
Images were collected at different hospitals and simple background removal

4 Iglovikov et al.
methods did not produce satisfactory results. Thus, there is a compelling need
for a reliable hand segmentation technique. However, this type of algorithms
typically requires large manually labeled training set. To alleviate labeling costs,
we employ a technique called positive mining. In general, positive mining is an
iterative procedure where manual labeling is combined with automatic processing.
It allows us quickly obtain accurate masks for all images in the training set.
Overall our preprocessing method includes binary image segmentation as a
first step and then the analysis of connected components for the post-processing
of segmentation results. For the image segmentation, we use U-Net deep network
architecture originally proposed in []. U-Net is capable of learning from a
relatively small training set that makes it a good architecture to combine with
positive mining. In general, the U-Net architecture consists of a contracting path
to capture context and a symmetric expanding path that enables precise local-
ization. The contracting path follows the typical architecture of a convolutional
network with convolution and pooling operations and progressively downsampled
feature maps. Every step in the expansive path consists of an upsampling of the
feature map followed by a convolution. Hence, the expansive branch increases the
resolution of the output. In order to localize, upsampled features in the expansive
path are combined with the high resolution features from the contracting path
via skip-connections []. This architecture proved itself very useful for segmenta-
tion problems with limited amounts of data, e.g. see []. We also employ batch
normalization technique to improve convergence during training [].
In our algorithms, we use the generalized loss function
𝐿=𝐻−log 𝐽,()
where 𝐻is a binary cross entropy that defined as
𝐻=−
1
𝑛
𝑛

𝑖=1
(𝑦𝑖log ^𝑦𝑖+(1−𝑦𝑖) log(1 −^𝑦𝑖)) ,()
where
𝑦𝑖
is a binary value of the corresponding pixel
𝑖
(
^𝑦𝑖
is a predicted probability
for the pixel). In the second term of Eq. (),
𝐽
is a differentiable generalization
of the Jaccard Index
𝐽=1
𝑛
𝑛

𝑖=1 𝑦𝑖^𝑦𝑖
𝑦𝑖+ ^𝑦𝑖−𝑦𝑖^𝑦𝑖.()
For more details see [].
In this work, we first manually label  masks using the online annotation
service Supervisely [] that takes approximately min to process each image.
These masks are used to train the U-Net model that then is used to perform hand
segmentation on the rest of the training set. Since each image is supposed to
contain only one hand, for each prediction we remove all connected components
except for the largest one that is kept and further subjected to the standard hole
filling protocol. This procedure allows us to predict hand masks in the unlabeled
train set, however, visual inspection reveals that the quality of mask predictions
is inconsistent and requires additional improvements. Thus, we visually inspect

Automatic Bone Age Assessment with Deep Learning 5
Fig. 3.
Iterative procedure of positive mining utilizing U-Net architecture for image
segmentation: (A) raw input data; (B) mask manually labeled with the online annotation
tool Supervisely [22]; (C) new data; (D) raw prediction; (E) post processed prediction;
(F) raw image with mask plotted together for visual inspection.
all predicted masks and keep only those of acceptable quality, while discarding
the rest. This manual process allows us to curate approximately -images per
second. Expanding the initial training set with the additional good quality masks
increases the size of the labeled images for segmentation procedure and improves
segmentation results. To achieve an acceptable quality on the whole training set,
we repeat this procedure times. Finally, we manually label approximately 
of the corner cases that U-Net is not able to capture well. The whole iterative
procedure is schematically shown in Fig. 
Original GP and TWmethods focus on specific hand bones, including
phalanges, metacarpal and carpal bones, see Fig. . So, we choose to train
separate models on several specific regions in high resolution and subsequently
evaluate their performance. To correctly locate these regions it is necessary to
transform all the images to the same size and position, i.e. to register them in one
coordinate space. Hence, our model comprises two sub-models: image registration
and bone age assessment of a specific region.
3 Key points detection
One of our goals is to evaluate the importance of specific regions of a hand for the
automated bone age assessment. This opens remarkable opportunity of running a
model on smaller image crops with higher resolution that might result in reduced
processing time and higher accuracy. To crop a specific region, we have to register
hand radiographs, or in other words, align them into a common coordinate space.
To this end, we first detect coordinates of several specific key points of a hand.

6 Iglovikov et al.
Then, we calculate affine transformation parameters (zoom, rotation, translation,
and mirror) to fit the image into the desired position (Fig. ).
Three characteristic points on the image are chosen: the tip of the distal
phalanx of the third finger, tip of the distal phalanx of the thumb, and center of
the capitate. All images are re-scaled to the same resolution:
2080 ×1600
pixels,
and padded with zeros when necessary. To create training set for key points
model, we manually label  radiographs. Pixel coordinates of key points serve
as training targets for our regression model. Registration procedure is shown in
Fig. .
Key points model is implemented as a deep convolutional neural network,
inspired by a popular VGG family of models [], with a regression output. The
network architecture is schematically shown in Fig. . The VGG module consists
of two convolutional layers with the Exponential Linear Unit (ELU) activation
function [], batch normalization [], and max pooling. The input image is
passed through a stack of three VGG blocks followed by three Fully Connected
layers. VGG blocks consist of ,, convolution layers respectively. For
better generalization, dropout units are applied in-between. The model is trained
with Mean Squared Error loss function (MSE) using Adam optimizer []:
𝑀𝑆𝐸 =1
𝑛
𝑛

𝑖=1
(^𝑦𝑖−𝑦𝑖)2.()
To reduce computational costs, we downscale input images to x pixels.
At the same time, target coordinates for key points are re-scaled from
[0,2079] ×
[0,1599]
to the uniform square
[−1,1] ×[−1,1]
. At the inference stage after the
model detects key points, we project their coordinates back to the original image
size, i.e
2080 ×1600
pixels. To improve generalization of our model, we apply
Fig. 4.
Image registration. (Left) Key points: the tip of the middle finger (the yellow dot),
the center of the capitate (the red dot), the tip of the thumb (the blue dot). Registration
positions: for the tip of the middle finger and for the center of the capitate (white dots).
(Right) Registered image after key points are found and the affine transformation and
scaling are applied.

Automatic Bone Age Assessment with Deep Learning 7
Fig. 5.
VGG-style neural network architectures for regression (top) and classification
(bottom) tasks.
input augmentations: rotation, translation and zoom. The model output consists
of coordinates, for every key point.
At the next step, we calculate affine transformations (zoom, rotation, transla-
tion) for all radiographs. Our goal is to preserve proportions of an image and to
fit it into uniform position such that for every image: ) the tip of the middle
finger is aligned horizontally and positioned approximately  pixels below the
top edge of the image; ) the capitate (see Fig. ) is aligned horizontally and
positioned approximately  pixels above the bottom edge of the image. By
convention, bone age assessment uses radiographs of the left hand, but sometimes
the images in the dataset get mirrored. To detect these images and adjust them
appropriately the key point for the thumb is used. The results of the segmentation,
normalization and registration are shown in the fourth row of Fig. .
4 Bone age assessment model
Although CNNs are more commonly used in classification tasks, bone age as-
sessment is a regression task by nature. In order to access performance in both
settings, we compare two types of CNNs: regression and classification. Both
models share similar architectures and training protocols, and only differ in two
final layers.

8 Iglovikov et al.
Fig. 6.
Preprocessing pipeline: (first row) original images; (second row) binary hand
masks that are applied to the original images to remove background; (third row) masked
and normalized images; (bottom row) registered images.
4.1 Regression model
Our first model is a VGG-style CNN [] with a regression output. This network
represents a stack of six convolutional blocks with ,,,,,
filters followed by two fully connected layers of  neurons each and a single
output (see Fig. ). The input size varies depending on the considered region
of an image, Fig. . For better generalization, we apply dropout layers before
the fully connected layers. For regression targets, we scale bone age in the range
[−1,1]. The network is trained by minimizing Mean Absolute Error (MAE):
𝑀𝐴𝐸 =1
𝑛
𝑛

𝑖=1
|^𝑦𝑖−𝑦𝑖|()
with Adam optimizer. We begin training with the learning rate
10−3
and then
progressively lower it to
10−5
. Due to a limited data set size, we use train time
augmentation with zoom, rotation and shift to avoid overfitting.
4.2 Classification model
The classification model (Fig. ) is similar to the regression one, except for the
two final layers. First, we assign each bone age a class. Bone ages expressed in
months, hence, we assume  classes overall. The second to the last layer is a
softmax layer with  outputs. This layer outputs vector of probabilities of 
classes. The probability of a class takes a real value in the range
[0,1]
. In the final
layer, the softmax layer is multiplied by a vector of distinct bone ages uniformly
distributed over  integer values
[0,1, ..., 238,239]
. Thereby, the model outputs
single value that corresponds to the expectation of the bone age. We train this
model using the same protocol as the regression model.

Automatic Bone Age Assessment with Deep Learning 9
4.3 Region-specific modelling
In accordance with the targeted features of skeletal development stages described
in [,,], we crop three specific regions from registered radiographs (
2080×1600
pixel), as shown in Fig. :
. whole hand (2000 ×1500 pixel)
. carpal bones (750 ×750 pixel)
. metacarpals and proximal phalanges (600 ×1400 pixel)
4.4 Experiment setup
We split labeled radiographs into two sets preserving sex ratio. The training
set contains , images, and validation set contains , images. We create
several models with a breakdown by:
. type (regression, classification)
. sex (males, females, mixed)
. region (A, B, C)
Given these conditions, we produce  basic models (
2×3×3
). Furthermore,
we construct several meta-models as a linear average of regional models and,
finally, an average of different models.
5 Results
The performance of all models is evaluated on validation data set, as presented
in Fig. . The leftmost column represents the performance of a regression model
for both sexes. For carpal bones (region B) this model has the lowest accuracy
with MAE . months. The region of metacarpals and proximal phalanges
(region C) has higher accuracy with MAE equal to . months. MAE of the
whole image (region A) is . months. The linear ensemble of the three regional
models outperforms all of the above models with MAE . months (bottom
row). This regional pattern MAE(B)
>
MAE(C)
>
MAE(A)
>
MAE (ensemble)
is further observed for other model types and patient cohorts with few exceptions.
Separate regression models for male and female cohorts (second and third columns)
demonstrated higher accuracy when compared to those trained on a mixed
population. The ensemble of regional models has MAE equal to . months for
males and . months for females (bottom row). For males, the top performing
region is the whole hand (A) that has MAE equal to . months. In contrast,
for the female cohort region of metacarpals and proximal phalanges (C) has
MAE equal to . months and this result is the most accurate across the three

10 Iglovikov et al.
Fig. 7.
A registered radiograph with three specific regions: (A) a whole hand; (B) carpal
bones; (C) metacarpals and proximal phalanges.
regions. Classification models (fourth and fifth columns) perform slightly better
than regression networks. The ensemble of the regional models has MAE equal to
. months for males and . months for females (bottom row). For the male
cohort the whole hand region (A) has the highest accuracy with MAE equal to
. months. For the female cohort the result produced using the metacarpals
and proximal phalanges region (C) is on par with that obtained using the whole
hand and has MAE equal to . months for both of them.
In the last column, we analyze the ensemble of classification and regression
models. As shown, the MAEs of regional models follow overall pattern MAE(B)
>
MAE(C)
>
MAE(A). The ensemble of the regional models (bottom row) has
the best accuracy with MAE equal to . month. This result clearly outperforms
state-of-the-art results with MAE equal to .months for the BoneXpert software
[] and .-. months for the recent application of deep neural networks [].
Next we evaluate our method in the context of the age distribution. Following
[,], we consider four major skeletal development stages: pre-puberty, early-
and-mid puberty, late puberty, and post-puberty. Infant and toddler categories
were excluded due to scarcity of the data: the development data set contained
only  radiographs for bone age less than  months and only of them were
presented in our validation subset. The model accuracies for the four skeletal
development stages, for different regions and sexes are depicted in the Fig. .
Note that the skeletal development stages are different for males and females.
Based on this data, we report two important findings.
First, unlike Lee et al. [], we do not observe better results when training on
carpal bones compared to other areas. With the two exceptions, the metacarpals
and proximal phalanges provide better accuracy than the carpal bones do. These

Automatic Bone Age Assessment with Deep Learning 11
Fig. 8.
Mean absolute errors on the validation data set for regression and classification
models for different bones and sexes. Colors correspond to different regions. Table:
regions are shown in rows, models in columns. There is a total of 15 individual models
and 9 ensembles.
exceptions are pre-puberty for the male cohort and post-puberty for the female
cohort, where the accuracy of the carpal bones is higher. However, the validation
dataset sizes for these two skeletal development stages ( for males in pre-
puberty and  for females in post-puberty) are too small to draw statistically
significant conclusion. At the same time, we notice that Gilsanz and Ratib []
proposed carpal bones as the best predictor of skeletal maturity only in infants
and toddlers. Thereafter, we find no sound evidence to support the suggestion
that the carpal bones can be considered as the best predictor in the pre-puberty,
see [].
The second interesting finding is the influence of the dataset on the accuracy
of a model. For the both sexes the accuracy peaks at the late-puberty, the most
frequent age in the data set. This dependency is particularly evident in the male
cohort, where the accuracy essentially mirrors data distribution (see Fig. ). This
finding is very important as it suggests a straightforward way for the future
improvement of the method.
6 Conclusion
In this study, we investigate the application of deep convolutional neural networks
to the problem of the automatic bone age assessment. The automatic bone age

12 Iglovikov et al.
Fig. 9.
Mean absolute error in months as a function of skeletal development stages
for different sexes. Different colors on the plot correspond to different regions of a
radiograph. For males and females the development stages are labelled at the bottom
of each plot.
assessment system based on our approach can estimate skeletal maturity with
accuracy similar to that of an expert radiologist and surpasses existing automated
models, e.g. see []. In addition, we numerically evaluate different zones of a
hand in bone age assessment. We find that bone age assessment could be done
just for carpal bones or for metacarpals and proximal phalanges with around
-
%
increase in error compared to the whole hand assessment. Therefore, we
can establish bone age using just part of the radiogram with high enough quality,
lowering computational overheads.
Despite the challenging quality of the radiographs, our approach succeeds in
image preprocessing, cleaning and standardization. These transformations, in
turn, greatly help in improving the robustness and performance of deep learning
models. Moreover, the accuracy of our approach can be improved even further.
First, our solution could be easily combined with other, more complex network
architectures, such as Resnet []. Another way to improve it is to substantially
extend the training data set by additional examples. Furthermore, bone ages that
serve as labels for training may also be refined based on work of independent
experts. Thereby, implementing these simple steps could potentially lead to a
development of a state-of-the-art bone assessment software system. It would have
a potential for the deployment in the clinical environment in order to help doctors
in making a final age bone assessment decision accurately and in real time, with
just one click. Moreover, a cloud-based system could be deployed for this problem
and process radoigraphs independently of their origin. This potentially could help
thousands of doctors to get a qualified evaluation even in hard-to-reach areas.
To conclude, in the final stage of the RSNA Pediatric Bone Age As-
sessement challenge our solution has been evaluated by organizers using the test

Automatic Bone Age Assessment with Deep Learning 13
set. This data set consisted of  radiographs equally divided between sexes.
All labels have been hidden from participants. Based on organizers’ report our
method achieved MAE equal to . months, which is higher compared to the
performance on the data withheld from the training set. The explanation of such
improvement may be hidden in more accurate labelling of the test set or better
quality of the radiographs. Each of radiographs in the test data set was evaluated
and crosschecked by three experts independently, compared to a one expert that
labelled training radiographs. This again demonstrate the importance of the
input from domain experts.
Acknowledgments
The authors would like to thank Open Data Science com-
munity [] for many valuable discussions and educational help in the growing
field of machine/deep learning.
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