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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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Leveraging Textual Anatomical Knowledge for Class-Imbalanced Semi-Supervised Multi-Organ Segmentation.

Yuliang Gu, Weilun Tsao, Yepeng Liu

IEEE Transactions on Medical Imaging

|January 12, 2026

Summary

This summary is machine-generated.

This study introduces a novel method using Multimodal Large Language Models (MLLM) to extract anatomical priors for semi-supervised multi-organ segmentation, significantly improving accuracy by leveraging textual insights for better organ localization and shape plausibility.

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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Area of Science:

Medical Image Analysis
Artificial Intelligence
Computational Anatomy

Background:

Semi-supervised multi-organ segmentation faces challenges due to imbalanced class distributions.
Integrating anatomical knowledge is a key strategy to address these segmentation difficulties.

Purpose of the Study:

To explore the use of Multimodal Large Language Models (MLLM) for extracting anatomical priors.
To improve semi-supervised multi-organ segmentation by incorporating textual anatomical insights.

Main Methods:

Utilized GPT-4o to generate textual descriptions of organ positional relationships and shapes as anatomical priors.
Integrated these textual priors into a segmentation model's head.
Employed contrastive learning to align textual priors with visual features.

Main Results:

The proposed method demonstrated significant performance improvements over state-of-the-art approaches.
Inter-organ positional priors aided in localizing smaller organs relative to larger ones.
Organ shape priors enhanced the anatomical plausibility of learned morphological structures.

Conclusions:

MLLM-derived anatomical priors are effective for enhancing semi-supervised multi-organ segmentation.
The integration of textual and visual features through contrastive learning improves segmentation accuracy and anatomical correctness.