Automatic Image Analysis for Bone Age Determination
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Date
2022-12
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Addis Ababa University
Abstract
Bone age evaluation is
commonly performed through radiological assessment of the skeletal
development of the left hand, and then is compared with the chronological age. However,
despite the time taking process, such image based automated processing remains incredibly
challenging to implement. An accurate method is important to study the development of
different wrist and pelvis structures which can predict age effectively. The proposed age
determination scheme is composed of two major steps a watershed based segmentation scheme
used to generate segmented images which are used as inputs to a deep learning algorithm that is
able to determine the age of a given subject. The deep learning scheme utilized the InceptionV3
architecture for its implementation. The model was trained, validated, and tested on radiological
hand images acquired from the RSNA database. The model resulted in a mean square error of
4.4 months when compared against the available ground truth information. Overall, the results
showed that the proposed bone age determination scheme comes with great promises.
Description
Keywords
(TW) Tanner and Whitehouse Method, (EDPh) Epiphysis Distal Phalanx, (EMPhx) Epiphysis Middle Phalanx, (EPRPL) Epiphysis Proximal Phalanx, (EMCRM) Epiphysis Metacarpals, (ROI) Region of Interest