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Related Experiment Video

Updated: Aug 3, 2025

Patient-Specific Polyvinyl Alcohol Phantom Fabrication with Ultrasound and X-Ray Contrast for Brain Tumor Surgery Planning

Patient-Specific Polyvinyl Alcohol Phantom Fabrication with Ultrasound and X-Ray Contrast for Brain Tumor Surgery Planning

Published on: July 14, 2020

A Foreground Prototype-Based One-Shot Segmentation of Brain Tumors.

Ananthakrishnan Balasundaram¹, Muthu Subash Kavitha², Yogarajah Pratheepan³

¹School of Computer Science and Engineering, Vellore Institute of Technology, Chennai 600127, Tamil Nadu, India.

Diagnostics (Basel, Switzerland)

|April 13, 2023

Summary

This study introduces a novel one-shot learning model for brain tumor segmentation in MRI scans. The model achieves high accuracy with minimal data, outperforming traditional methods in efficiency and performance.

Keywords:

few-shot learning foreground prototypes magnetic resonance imaging

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Area of Science:

Medical Imaging
Artificial Intelligence
Computational Biology

Background:

Deep learning networks (DNNs) show promise for brain tumor segmentation but require extensive training data.
A significant challenge for DNNs is their performance on unseen classes, limiting their clinical applicability.
Few-shot learning offers a potential solution to data scarcity in medical image analysis.

Purpose of the Study:

To develop and evaluate a one-shot learning model for accurate brain tumor segmentation using minimal data.
To address the limitations of conventional DNNs in handling limited and unseen data for brain tumor segmentation.
To improve the efficiency and effectiveness of brain tumor segmentation in MRI by leveraging few-shot learning techniques.

Main Methods:

Proposed a one-shot learning model utilizing a single prototype similarity score for brain tumor segmentation.
Employed few-shot learning techniques with support and query image sets, focusing on foreground slices.
Utilized a metric learning-based approach with non-parametric thresholds for differentiating query images from class prototypes.
Trained the model iteratively using random foreground slices from the multimodal Brain Tumor Image Segmentation (BraTS) 2021 dataset.

Main Results:

Achieved a mean dice score of 83.42%, outperforming existing literature benchmarks.
Demonstrated superior performance compared to conventional methods in terms of computational time and memory usage.
Successfully segmented brain tumors using a single prototype similarity score with minimal training data.

Conclusions:

The proposed one-shot learning model offers a highly effective and efficient solution for brain tumor segmentation in MRI.
This approach significantly reduces the need for large annotated datasets, making it valuable for clinical applications.
The model's ability to perform well with limited data and its computational efficiency represent a significant advancement in medical image analysis.