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Updated: Sep 17, 2025

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Published on: January 7, 2019

Cross-domain subcortical brain structure segmentation algorithm based on low-rank adaptation fine-tuning SAM.

Yuan Sui¹, Qian Hu², Yujie Zhang³

¹School of Computer Science and Engineering, Northeastern University, Shenyang, Liaoning, 110169, China.

BMC Medical Imaging

|July 2, 2025

Summary

This summary is machine-generated.

This study introduces a new method to improve brain MRI segmentation using Low-Rank Adaptation (LoRA) to fine-tune the Segment Anything Model (SAM). This approach enhances accuracy for deep brain structures, reducing annotation costs for clinicians.

Keywords:

Cross-domain adaption Fine-tuning SAM Low-rank adaptation Subcortical structure segmentation

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Last Updated: Sep 17, 2025

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Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

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Published on: April 13, 2013

Area of Science:

Medical Imaging
Artificial Intelligence
Neuroscience

Background:

Accurate brain MRI segmentation is vital for diagnosing and treating neurological conditions.
Deep learning models struggle with subcortical brain structure segmentation due to domain differences.

Purpose of the Study:

To develop an efficient and accurate subcortical brain structure segmentation algorithm for MRI.
To adapt large-scale foundation models for specialized medical imaging tasks.

Main Methods:

Fine-tuning the Segment Anything Model (SAM) using Low-Rank Adaptation (LoRA).
Freezing the SAM image encoder and applying LoRA to its weights.
Fine-tuning SAM's prompt encoder and mask decoder with adaptive prompt learning.

Main Results:

The fine-tuned model uses only 6.39% of the original SAM's parameters.
Adaptive prompt learning enhances segmentation accuracy for arbitrary brain MRI scans.
The method demonstrates generalization across diverse MRI datasets and segmentation scenarios.

Conclusions:

This interactive approach offers intelligent segmentation for deep brain structures, overcoming data limitations.
The algorithm effectively reduces manual annotation costs in medical image segmentation.
The proposed method shows superior generalization and effectiveness compared to existing algorithms.