sarah-antillia/TensorFlow-FlexUNet-Image-Segmentation-BraTS-Africa-Glioma-T2W
Domaine:
healthcareType de record:
software
Créateur:
sar
Hôte:
TensorFlowFlexUNet Image Segmentation for BraTS Africa Glioma T2W.
TensorFlow-FlexUNet-Image-Segmentation-BraTS-Africa-Glioma-T2W (2026/04/06)
Sarah T. Arai
Software Laboratory antillia.com
This is the first experiment of Image Segmentation for BraTS-Africa-Glioma-T2W based on
our TensorFlowFlexUNet
( TensorFlow Flexible UNet Image Segmentation Model for Multiclass ), and a 512x512 pixels upscaled PNG
BraTS-Africa-Glioma-T2W-ImageMask-Dataset.zip
(
CC BY 4.0
), which was derived by us from
BraTS-Africa | Expanding the Brain Tumor Segmentation (BraTS) data to include African Populations
on CANCER IMAGING ARCHIVE
Actual Image Segmentation for BraTS-Africa-Glioma-T2W Images of 512x512 pixels
As shown below, the inferred masks predicted by our segmentation model trained by the dataset appear similar to the
ground truth masks.
class_color_map = {NCR (Necrotic Tumor Core):blue, ED (Edema):green, ET (Enhancing Tumor):red}
Input: image
Mask (ground_truth)
Prediction: inferred_mask
1. Dataset Citation
The dataset used here was taken from
BraTS-Africa | Expanding the Brain Tumor Segmentation (BraTS) data to include African Populations
on CANCER IMAGING ARCHIVE.
The following explanation was taken from the above web site.
The dataset is a collection of retrospective pre-operative brain magnetic resonance imaging (MRI) scans,
clinically acquired from six diagnostic centers in Nigeria.
The scans are from 146 patients who have brain MRIs indicating central nervous system neoplasms, diffuse glioma,
low-grade glioma, or glioblastoma/high-grade glioma.
The brain scans were multiparametric MR images (mpMRI),
specifically T1, T1 CE, T2, and T2 FLAIR, acquired on 1.5T MRI between January 2010 and December 2022.
Scans were obtained from different scanners using each center’s acquisition protocol.
Each scan was de-identified and de-faced to remove personal identifiers and presented in
their original state with respect to resolution and orientation.
To ensure uniformity across scans and modalities, a standardized pre-p …
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