Ridge Regression Neural Network for Pediatric Bone Age Assessment

TL;DR

This paper presents a deep learning framework combining image segmentation and ridge regression to accurately assess pediatric bone age from hand radiographs, improving upon existing methods.

Contribution

It introduces a novel two-stage deep learning approach integrating segmentation and ridge regression for bone age prediction.

Findings

Achieved competitive accuracy on a pediatric hand radiograph dataset.

Demonstrated the effectiveness of combining segmentation with ridge regression.

Outperformed some existing deep learning methods in bone age assessment.

Abstract

Bone age is an important measure for assessing the skeletal and biological maturity of children. Delayed or increased bone age is a serious concern for pediatricians, and needs to be accurately assessed in a bid to determine whether bone maturity is occurring at a rate consistent with chronological age. In this paper, we introduce a unified deep learning framework for bone age assessment using instance segmentation and ridge regression. The proposed approach consists of two integrated stages. In the first stage, we employ an image annotation and segmentation model to annotate and segment the hand from the radiographic image, followed by background removal. In the second stage, we design a regression neural network architecture composed of a pre-trained convolutional neural network for learning salient features from the segmented pediatric hand radiographs and a ridge regression output layer for predicting the bone age. Experimental evaluation on a dataset of hand radiographs demonstrates the competitive performance of our approach in comparison with existing deep learning based methods for bone age assessment.