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Related Experiment Video

Updated: Aug 8, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Published on: November 30, 2022

Selective Deeply Supervised Multi-Scale Attention Network for Brain Tumor Segmentation.

Azka Rehman¹, Muhammad Usman², Abdullah Shahid¹

¹Center for Artificial Intelligence in Medicine and Imaging, HealthHub Co., Ltd., Seoul 06524, Republic of Korea.

Sensors (Basel, Switzerland)

|February 28, 2023

Summary

This summary is machine-generated.

A new automated method, the selective deeply supervised multi-scale attention network (SDS-MSA-Net), accurately segments brain tumors. This approach improves segmentation of core and enhancing tumor regions, aiding faster diagnosis and treatment.

Keywords:

3D segmentation brain tumor segmentation selective deep supervision

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Last Updated: Aug 8, 2025

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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

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Published on: July 5, 2024

Area of Science:

Medical imaging analysis
Artificial intelligence in oncology
Neurosurgery and oncology

Background:

Brain tumors are aggressive cancers requiring timely diagnosis.
Manual segmentation of brain tumors is labor-intensive and error-prone.
Automated segmentation methods face challenges due to tumor heterogeneity.

Purpose of the Study:

To develop a fully automated brain tumor segmentation method.
To improve the accuracy of segmenting whole, core, and enhancing tumor regions.
To address the limitations of manual segmentation in clinical workflows.

Main Methods:

Proposed a selective deeply supervised multi-scale attention network (SDS-MSA-Net).
Utilized 3D and 2D inputs for sequential information and feature extraction.
Incorporated multi-scale architecture with attention units and selective deep supervision.

Main Results:

Achieved improved performance in brain tumor region segmentation on the BraTS2020 dataset.
Demonstrated particular effectiveness in segmenting core and enhancing tumor subregions.
The proposed SDS-MSA-Net shows significant potential for clinical application.

Conclusions:

The SDS-MSA-Net offers an effective and automated solution for brain tumor segmentation.
The method's ability to handle tumor heterogeneity enhances diagnostic accuracy.
Public availability of the code facilitates further research and development.