metadata
license: apache-2.0
tags:
- brain-mri
- segmentation
- medical-imaging
- deep-learning
- unet
base_model: tf-keras/imagenet-mobilenetv2
model-index:
- name: Brain MRI Segmentation - FLAIR Abnormality Segmentation
results:
- task:
type: image-segmentation
name: Image Segmentation
dataset:
name: LGG Segmentation Dataset
type: medical-imaging
link: https://www.kaggle.com/datasets/mateuszbuda/lgg-mri-segmentation
metrics:
- type: dice
value: 0.843
name: Dice Coefficient
- type: iou
value: 0.609
name: Intersection over Union (IoU)
Brain MRI Segmentation - FLAIR Abnormality Segmentation v1.0.0
This repository hosts the trained model for FLAIR Abnormality Segmentation in Brain MRI scans. The model is a U-Net architecture with a MobileNetV2 encoder pretrained on ImageNet, designed to segment FLAIR abnormalities from MRI images effectively.
Model Details
- Architecture: U-Net with MobileNetV2 encoder and custom decoder layers.
- Dataset: LGG Segmentation Dataset
- Version: v1.0.0
- Task: Image Segmentation
- License: Apache 2.0
Performance
The model achieved the following metrics on the test dataset:
- Dice Coefficient: 0.843
- Intersection over Union (IoU): 0.609
Usage
To use this model for inference, you can load it using the tensorflow library.
# Clones the repository and install dependencies
!git clone https://huggingface.co/preethamganesh/bms-flair-abnormality-segmentation-v1.0.0
!pip install tensorflow
# Imports TensorFlow
import tensorflow as tf
# Loads the pre-trained model from the cloned directory
model_path = "bms-flair-abnormality-segmentation-v1.0.0"
exported_model = tf.saved_model.load(model_path)
# Retrieves the default serving function from the loaded model
model = exported_model.signatures["serving_default"]
# Prepares a dummy input tensor for inference (batch size: 1, height: 256, width: 256, channels: 3)
input_data = tf.ones((1, 256, 256, 3), dtype=tf.float32)
# Performs inference using the model. The output will be a dictionary, with the segmentation map in the key 'output_0'
output = model(input_data)["output_0"]
# Prints the shape of the output tensor for verification
print("Output shape:", output.shape)
Dataset
The model was trained on the LGG Segmentation Dataset, which contains Brain MRI images with FLAIR abnormalities labeled for segmentation tasks. The dataset is available on Kaggle.
Citation
If you use this model in your research, please cite the repository:
@misc{preethamganesh2024brainmri,
title={Brain MRI Segmentation - FLAIR Abnormality Segmentation},
author={Preetham Ganesh},
year={2025},
url={https://huggingface.co/preethamganesh/brain-mri-flair-abnormality-segmentation-v1-0-0},
note={Apache-2.0 License}
}
Contact
For any questions or support, please contact [email protected].