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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...

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Updated: May 6, 2026

Outer-Boundary Assisted Segmentation and Quantification of Trabecular Bones by an Imagej Plugin

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Published on: March 14, 2018

Unsupervised Segmentation of Knee Bone Marrow Edema-like Lesions Using Conditional Generative Models.

Andrew Seohwan Yu^1,2,3, Mingrui Yang^1,2, Richard Lartey^1,2

¹Program of Advanced Musculoskeletal Imaging (PAMI), Cleveland Clinic, Cleveland, OH 44195, USA.

Bioengineering (Basel, Switzerland)

|June 27, 2024

Summary

This study introduces an unsupervised method for automatically segmenting bone marrow edema-like lesions (BMEL) in knee osteoarthritis (OA) using advanced AI. This approach overcomes limitations of manual segmentation, improving efficiency and reliability in OA research.

Keywords:

MRI anomaly detection deep learning diffusion models bone marrow edema-like lesions generative adversarial networks inter-rater reliability unsupervised segmentation

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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Published on: November 30, 2022

Area of Science:

Medical Imaging
Artificial Intelligence
Osteoarthritis Research

Background:

Bone marrow edema-like lesions (BMEL) are key indicators in knee osteoarthritis (OA) progression.
Current manual and semi-automatic segmentation methods for BMELs are time-consuming, prone to bias, and have low reliability.
The variability in BMEL size, shape, and signal intensity complicates accurate quantification.

Purpose of the Study:

To develop a novel, fully automated, unsupervised method for segmenting BMELs in knee MRIs.
To leverage conditional diffusion models and anomaly detection for accurate BMEL segmentation without manual annotations.
To establish improved benchmarks for BMEL segmentation by analyzing expert inter-rater variability.

Main Methods:

Utilized conditional diffusion models for image segmentation.
Incorporated anomaly detection techniques to identify BMELs.
Employed multiple MRI sequences with varying BMEL contrast.
Analyzed expert annotations to quantify intra- and inter-rater variability.

Main Results:

Developed a fully automated, unsupervised method for BMEL segmentation.
Demonstrated a novel approach using conditional diffusion models and anomaly detection.
Quantified expert variability to set new performance benchmarks for BMEL segmentation.

Conclusions:

The proposed unsupervised method offers a significant advancement for automated BMEL segmentation in knee OA.
This AI-driven approach addresses the limitations of manual segmentation, reducing time, cost, and bias.
The study provides a more reliable tool for quantifying BMELs, aiding OA research and clinical applications.