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---
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: 0.77
name: Dice Coefficient
- type: iou
value: 0.62
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](https://www.kaggle.com/datasets/mateuszbuda/lgg-mri-segmentation)
- **Version:** v1.0.0
- **Task:** Image Segmentation
- **License:** Apache 2.0
## Usage
To use this model for inference, you can load it using the `tensorflow` library.
```bash
# 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)
```
## Training Details
### Compute
- The model was trained on a GeForce 4070Ti GPU with 16GB VRAM.
- Training completed in approximately 4.9 minutes over 24 epochs.
### Dataset
- The model was trained on the [LGG Segmentation Dataset](https://www.kaggle.com/datasets/mateuszbuda/lgg-mri-segmentation), which includes Brain MRI images labeled for FLAIR abnormality segmentation.
- Only images with positive FLAIR abnormalities were selected for training.
### Performance on test set
- **Dice Coefficient:** 0.77
- **Intersection over Union (IoU):** 0.62
## Citation
If you use this model in your research, please cite the repository:
```bash
@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].
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