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Updated: Jul 18, 2025

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

Feature-aware unsupervised lesion segmentation for brain tumor images using fast data density functional transform.

Shin-Jhe Huang¹, Chien-Chang Chen¹, Yamin Kao¹

¹Geometric Data Vision Laboratory, Department of Biomedical Sciences and Engineering, National Central University, Taoyuan City, 32001, Taiwan.

Scientific Reports

|August 21, 2023

Summary

We introduce a novel method using fast data density functional transform (fDDFT) and geometric deep learning for unsupervised medical image segmentation. This approach accurately identifies lesion morphology and boundaries, improving efficiency and accuracy in clinical investigations.

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Area of Science:

Medical Imaging Analysis
Computational Physics
Artificial Intelligence

Background:

Accurate segmentation of medical images is crucial for diagnosis and treatment planning.
Current deep learning methods often require large labeled datasets and significant computational resources.
Unsupervised methods offer a promising alternative for lesion recognition and segmentation.

Purpose of the Study:

To develop an unsupervised method for lesion morphology recognition and segmentation using medical image matrices.
To leverage the framework of fast data density functional transform (fDDFT) and geometric deep learning.
To improve the efficiency and accuracy of medical image analysis.

Main Methods:

Isomorphic mapping of gray-level medical image matrices onto fDDFT energy spaces.
Integration of geometric deep learning and graph neural network metrics.
Utilizing gridized density functionals with global convolutional kernels for feature extraction and boundary identification.
Employing an AutoEncoder-assisted module to reduce computational complexity.
Validation on diverse open-access datasets.

Main Results:

Achieved unsupervised recognition of lesion morphology and accurate segmentation.
Demonstrated efficient global convolutional operations with reduced complexity.
Inference time averaged 1.76 seconds per object in large 3D datasets.
Median Dice score exceeded 0.75, meeting standard deep learning requirements.
Synergy of fDDFT and neural networks improved training (58%) and inference (51%) times, raising Dice score to 0.9415.

Conclusions:

The proposed fDDFT-based approach enables effective unsupervised lesion segmentation in medical images.
The method offers significant improvements in computational efficiency and accuracy compared to conventional deep learning models.
This technique facilitates fast computational modeling for interdisciplinary applications and clinical investigation.