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Brain Imaging

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and Transcranial Magnetic Stimulation (TMS).

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Related Experiment Video

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

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Published on: January 7, 2019

Deep learning-driven macroscopic AI segmentation model for brain tumor detection via digital pathology: Foundations

Myeong Suk Yim¹, Yun Heung Kim², Hyeon Sang Bark³

¹Gimhae Biomedical Center, Gimhae Biomedical Industry Promotion Agency (GBIA), Gimhae, 05969, Republic of Korea.

|December 5, 2024

Summary

We developed an AI model using deep learning to automatically identify cancer in digital pathology images. This tool aids neuropathologists by providing guiding images for faster, more accurate cancer delineation.

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Published on: November 30, 2022

Area of Science:

Digital Pathology
Artificial Intelligence
Oncology

Background:

Accurate cancer delineation in digital pathology images is crucial for diagnosis and treatment planning.
Manual annotation of cancerous regions is time-consuming and requires specialized expertise.
Developing automated methods can improve efficiency and consistency in cancer diagnosis.

Purpose of the Study:

To develop and validate a deep learning-based AI model for autonomous delineation of cancerous regions in H&E-stained digital pathology images.
To create a robust and scalable AI training pipeline using DEEP:PHI and efficient data handling techniques.
To facilitate the development of AI-driven cancer diagnosis technologies.

Main Methods:

Utilized deep learning algorithms on a dataset of 187 H&E-stained images from a transgenic brain tumor model.
Employed the DEEP:PHI platform to simplify AI model training and execution.
Implemented Image Crop with Mask and patch generation techniques for data balancing and resource optimization.

Main Results:

Successfully developed an AI model capable of autonomously segmenting cancerous areas in digital pathology images.
The AI model provides guiding images, reducing the need for extensive neuropathologist assistance.
A high-quality, large dataset was curated, supporting further AI development in cancer diagnosis.

Conclusions:

Deep learning offers a powerful approach for automated cancer region delineation in digital pathology.
The developed AI model enhances diagnostic efficiency and accuracy, supporting neuropathologists.
The curated dataset and methodology contribute to advancing AI-based cancer diagnosis, including terahertz imaging applications.