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Related Concept Videos

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

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Published on: March 14, 2018

Data-Efficient Bone Segmentation Using Feature Pyramid- Based SegFormer.

Naohiro Masuda¹, Keiko Ono², Daisuke Tawara³

¹Master's Program in Information and Computer Science, Doshisha University, Kyoto 610-0394, Japan.

Sensors (Basel, Switzerland)

|January 11, 2025

Summary

This study enhances SegFormer for medical image segmentation, improving bone structure analysis with limited data. The modified model achieves superior accuracy in precise object contour detection for diagnostic applications.

Keywords:

Mask2Former SegFormer feature pyramid network semantic segmentation transformer block

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Area of Science:

Medical Imaging
Computer Vision
Artificial Intelligence

Background:

Semantic segmentation of bone structures is crucial for accurate medical diagnosis and modeling.
Convolutional Neural Networks (CNNs) face limitations in segmenting complex bone shapes due to texture focus and poor positional awareness.
Vision Transformer models like SegFormer offer improved spatial awareness but require extensive training data, a challenge in medical imaging.

Purpose of the Study:

To enhance SegFormer for accurate semantic segmentation of bone structures using limited medical imaging datasets.
To address the data-efficiency limitations of SegFormer in medical image analysis.
To improve the precision of bone model generation for diagnostic purposes.

Main Methods:

Proposed two modified SegFormer architectures: a data-efficient model with deeper hierarchical encoders and increased feature map resolution, and an FPN-based model with an enhanced decoder using attention mechanisms.
Combined the proposed data-efficient and FPN-based enhancements.
Evaluated the models on spine, hand, and wrist datasets from The Cancer Imaging Archive and a custom dataset.

Main Results:

The proposed enhanced SegFormer models outperformed the original SegFormer, U-Net, and Mask2Former in semantic segmentation accuracy.
Ablation studies confirmed the effectiveness of the individual and combined modifications.
Demonstrated improved image feature extraction and more precise object contour detection.

Conclusions:

The developed SegFormer enhancements significantly improve semantic segmentation of bone structures, especially with limited training data.
The modifications enable more reliable bone model generation for orthopedic surgery and diagnosis.
This approach offers a valuable solution for data-scarce medical imaging segmentation tasks.