Region of Interest Localization for Bone Age Estimation Using Whole-Body Bone Scintigraphy

• 발행기관: 한국스마트미디어학회
• 저자명: Thanh-Cong Do 양형정(Hyung Jeong Yang) 김수형(Soo Hyung Kim) 이귀상(Guee Sang Lee) 강세령(Sae Ryung Kang) 민정준(Jung Joon Min)
• 간행물 정보: 『스마트미디어저널』Vol10, No.2, 22~29쪽, 전체 8쪽
• 주제분류: 공학 > 컴퓨터학
• 파일형태: PDF
• 발행일자: 2021.06.30

영문 초록

In the past decade, deep learning has been applied to various medical image analysis tasks. Skeletal bone age estimation is clinically important as it can help prevent age-related illness and pave the way for new anti-aging therapies. Recent research has applied deep learning techniques to the task of bone age assessment and achieved positive results. In this paper, we propose a bone age prediction method using a deep convolutional neural network. Specifically, we first train a classification model that automatically localizes the most discriminative region of an image and crops it from the original image. The regions of interest are then used as input for a regression model to estimate the age of the patient. The experiments are conducted on a whole-body scintigraphy dataset that was collected by Chonnam National University Hwasun Hospital. The experimental results illustrate the potential of our proposed method, which has a mean absolute error of 3.35 years. Our proposed framework can be used as a robust supporting tool for clinicians to prevent age-related diseases.

목차

I. INTRODUCTION

II. RELATED WORK

III. PROPOSED METHOD

IV. EXPERIMENT AND RESULTS

V. CONCLUSION

키워드

Bone Age Estimation Deep Learning Convolutional Neural Network