<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The automated segmentation of brain tissues and lesions represents a widely investigated research topic. The Brain Tumor Segmentation Challenges (BraTS) organized yearly since 2012 provided standard training and testing data and a unified evaluation framework to the research community, which provoked an intensification in this research field. This paper proposes a solution to the brain tumor segmentation problem, which is built upon the U-net architecture that is very popular in medical imaging. The proposed procedure involves two identical, cascaded U-net networks with 3D convolution. The first stage produces an initial segmentation of a brain volume, while the second stage applies a post-processing based on the labels provided by the first stage. In the first U-net based classification, each pixel is characterized by the four observed features (T1, T2, T1c, and FLAIR), while the second identical U-net works with four features extracted from the volumetric neighborhood of the pixels, representing the ratio of pixels with positive initial labeling within the neighborhood. Statistical accuracy indexes are employed to evaluate the initial and final segmentation of each MRI record. Tests based on BraTS 2019 training data set led to average Dice scores over 87%. The postprocessing step can increase the average Dice scores by 0.5%, it improves more those volumes whose initial segmentation was less successful.</p>
</abstract>

The automated segmentation of brain tissues and lesions represents a widely investigated research topic. The Brain Tumor Segmentation Challenges (BraTS) organized yearly since 2012 provided standard training and testing data and a unified evaluation framework to the research community, which provoked an intensification in this research field. This paper proposes a solution to the brain tumor segmentation problem, which is built upon the U-net architecture that is very popular in medical imaging. The proposed procedure involves two identical, cascaded U-net networks with 3D convolution. The first stage produces an initial segmentation of a brain volume, while the second stage applies a post-processing based on the labels provided by the first stage. In the first U-net based classification, each pixel is characterized by the four observed features (T1, T2, T1c, and FLAIR), while the second identical U-net works with four features extracted from the volumetric neighborhood of the pixels, representing the ratio of pixels with positive initial labeling within the neighborhood. Statistical accuracy indexes are employed to evaluate the initial and final segmentation of each MRI record. Tests based on BraTS 2019 training data set led to average Dice scores over 87%. The postprocessing step can increase the average Dice scores by 0.5%, it improves more those volumes whose initial segmentation was less successful.

A two-stage U-net approach to brain tumor segmentation from multi-spectral MRI records

Sapientia Hungarian University of Transylvania

University Research, Innovation and Service Center

Acta Universitatis Sapientiae, Informatica

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

The automated segmentation of brain tissues and lesions represents a widely investigated research topic. The Brain Tumor Segmentation Challenges (BraTS)...

A two-stage U-net approach to brain tumor segmentation from multi-spectral MRI records

Data publikacji: 04 lut 2023

Zakres stron: 223 - 247

Otrzymano: 18 lis 2022

Przyjęty: 11 gru 2022

DOI: https://doi.org/10.2478/ausi-2022-0014

Słowa kluczowe
machine learning, convolutional neural networks, U-net architecture, image segmentation

© 2022 Ágnes Győrfi et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

A two-stage U-net approach to brain tumor segmentation from multi-spectral MRI records

Data publikacji: 04 lut 2023

Zakres stron: 223 - 247

Otrzymano: 18 lis 2022

Przyjęty: 11 gru 2022

DOI: https://doi.org/10.2478/ausi-2022-0014

Słowa kluczowemachine learning, convolutional neural networks, U-net architecture, image segmentation

© 2022 Ágnes Győrfi et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Słowa kluczowe
machine learning, convolutional neural networks, U-net architecture, image segmentation