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Classification of Bones

Classification of Bones

The bones of the human skeletal system are of varied shapes, sizes, and functions. They can be classified based on their shape and function into four major classes: long bones, short bones, flat bones, and irregular bones. Some classifications include a fifth type, the sesamoid bones, as a separate class, whereas others categorize them under short bones.
Long and Short Bones
The appendicular skeleton, particularly the upper and lower limbs, is primarily made of long and short bones. The long...

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Updated: Jul 15, 2026

Automated Quantification of Hematopoietic Cell – Stromal Cell Interactions in Histological Images of Undecalcified Bone

Automated Quantification of Hematopoietic Cell – Stromal Cell Interactions in Histological Images of Undecalcified Bone

Published on: April 8, 2015

一种完全自动化的基于U-net的ROI定位和骨年龄评估方法.

Yuzhong Zhao¹, Yihao Wang¹, Haolei Yuan²

¹Institute of Natural Sciences, School of Mathematical Sciences, MOE-LSC & Shanghai National Center for Applied Mathematics (SJTU Center), Shanghai Jiao Tong University, Shanghai 200030, China.

Mathematical biosciences and engineering : MBE

|February 14, 2025

概括

本研究引入了一种用于骨年龄评估 (BAA) 的自动深度学习方法,比传统技术提高了准确性和效率. 这种新的方法精确地定位骨区域,使青少年可靠的生物发育评估成为可能.

关键词:

在ROI本地化方面.在U-net中,U-net是指U-net网络.骨年龄评估骨年龄评估可以解释的解释性.

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In Vivo Quantification of Hip Arthrokinematics during Dynamic Weight-bearing Activities using Dual Fluoroscopy

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Last Updated: Jul 15, 2026

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In Vivo Quantification of Hip Arthrokinematics during Dynamic Weight-bearing Activities using Dual Fluoroscopy

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科学领域:

医疗成像医学成像
人工智能的人工智能
儿科内分泌学儿科内分泌学

背景情况:

骨年龄评估 (BAA) 对于评估青少年生物发育至关重要.
像Tanner Whitehouse (TW) 这样的传统方法需要手动提取感兴趣区域 (ROI),这是耗时和主观的.
现有的自动化方法可能缺乏解释性或难以捕捉本地和全球骨特征.

研究的目的:

开发一个完全自动化的深度学习系统,用于精确的ROI定位和准确的骨年龄预测.
将基于ROI和基于全球特征的BAA方法的优势整合到一个单一的可解释模型中.
根据已建立的数据集和临床标准验证拟议方法的性能.

主要方法:

基于U-net的深度学习架构用于语义细分,以实现骨架ROI的自动和精确本地化.
使用InceptionResNetV2网络从本地化ROI和整个手部X射线图中进行了强大的特征提取.
BAA模型以协同作用的方式将本地化ROI特征与全球图像特征相结合,以提高预测准确度.

主要成果:

在公开的RSNA数据集上,自动化ROI本地化实现了99.1%的高精度.
BAA模型显示了低平均绝对误差 (MAE):在RSNA数据集上,0.38年 (男性) 和0.45年 (女性).
在内部数据集上观察到可比的MAE值 (男性为0.41年,女性为0.44年),证实了该方法的概括性和准确性.

结论:

拟议的深度学习方法为骨年龄评估提供了高度准确和自动化的解决方案.
这种方法通过消除手动ROI提取来提高效率,并提供可解释的结果.
经过验证的性能表明,在儿科内分泌学和发育评估中临床整合的巨大潜力.