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--- a/README.md +++ b/README.md @@ -1,59 +1,59 @@ -# Brain tumor segmentation in MRI images using U-Net - -Here, I have implemented a U-Net from the paper ["U-Net: Convolutional Networks for Biomedical -Image Segmentation"](https://arxiv.org/pdf/1505.04597.pdf) to segment tumor in MRI images of brain. - -There are 3 types of brain tumor: -1. meningioma -2. glioma -3. pituitary tumor - -## Examples of predicted tumor segment by the current U-Net implementation. -meningioma | glioma | pituitary tumor -:-------------------------:|:-------------------------:|:------------------------: - |  |  - |  |  - |  |  - -## Getting Started -Here I will explain how to get the data and convert it into the usable form. You can run the train and run model using [notebook](https://github.com/adityajn105/brain-tumor-segmentation-unet/blob/master/brain-tumor-segmentation.ipynb). - -### Prerequisites -You will need Python 3.X.X with some packages which you can install direclty using requirements.txt. -> pip install -r requirements.txt - -### Get the Dataset -I have used brain-tumor segment dataset which is available on the internet. You can run [download_data.sh](https://github.com/adityajn105/brain-tumor-segmentation-unet/blob/master/download_data.sh) shell script to download all data. It contains 3064 MRI images and 3064 masks. -> bash tumor-segmentation-unet/download_data.sh - -After that run the following command to convert data in useable form. -> python tumor-segmentation-unet/mat_to_numpy.py brain_tumor_dataset/ - -## Model Architecture -I have used combination of multiple losses which includes binary crossentropy, dice loss with equal weightage. Also I have used Conv2D transpose layers for upsampling. - -I have used the metric called IOU (Intersection over Union) metric to track progress of training and trained Unet with Adam optimizer for 40-60 epochs with decaying learning rate between 1e-3 to 1e-4. I have also performed only one Image augmentation i.e. horizontal flip. Train and test split was stratified using type of tumor. - - - - -Detailed architecure is given below. - - -## Possible Improvements -1. Can use transfer learning to utilize state-of-the-art model like VGG, Inception, Resnet. -2. We can use more types of image augmentation like vertical flip, brightness, zoom etc. -3. Include lovasz loss with higher weightage. -4. Learn and use Hypercolumns - -## Author: -* Aditya Jain : [Portfolio](https://adityajain.me) - -## To Read: -1. [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://arxiv.org/pdf/1505.04597.pdf) -2. [Image Segmentation, ConvNet, FCN, Unet](https://towardsdatascience.com/understanding-semantic-segmentation-with-unet-6be4f42d4b47) -3. [Up-sampling with Transposed Convolution](https://towardsdatascience.com/up-sampling-with-transposed-convolution-9ae4f2df52d0) -4. [Lovasz Loss](https://arxiv.org/abs/1705.08790) -5. [Jaccard Index - Intesection over Union](https://www.jeremyjordan.me/evaluating-image-segmentation-models/) -6. [Understanding Dice Loss](https://forums.fast.ai/t/understanding-the-dice-coefficient/5838) +# Brain tumor segmentation in MRI images using U-Net + +Here, I have implemented a U-Net from the paper ["U-Net: Convolutional Networks for Biomedical +Image Segmentation"](https://arxiv.org/pdf/1505.04597.pdf) to segment tumor in MRI images of brain. + +There are 3 types of brain tumor: +1. meningioma +2. glioma +3. pituitary tumor + +## Examples of predicted tumor segment by the current U-Net implementation. +meningioma | glioma | pituitary tumor +:-------------------------:|:-------------------------:|:------------------------: + |  |  + |  |  + |  |  + +## Getting Started +Here I will explain how to get the data and convert it into the usable form. You can run the train and run model using [notebook](https://github.com/adityajn105/brain-tumor-segmentation-unet/blob/master/brain-tumor-segmentation.ipynb). + +### Prerequisites +You will need Python 3.X.X with some packages which you can install direclty using requirements.txt. +pip install -r requirements.txt + +### Get the Dataset +I have used brain-tumor segment dataset which is available on the internet. You can run [download_data.sh](https://github.com/adityajn105/brain-tumor-segmentation-unet/blob/master/download_data.sh) shell script to download all data. It contains 3064 MRI images and 3064 masks. +bash tumor-segmentation-unet/download_data.sh + +After that run the following command to convert data in useable form. +python tumor-segmentation-unet/mat_to_numpy.py brain_tumor_dataset/ + +## Model Architecture +I have used combination of multiple losses which includes binary crossentropy, dice loss with equal weightage. Also I have used Conv2D transpose layers for upsampling. + +I have used the metric called IOU (Intersection over Union) metric to track progress of training and trained Unet with Adam optimizer for 40-60 epochs with decaying learning rate between 1e-3 to 1e-4. I have also performed only one Image augmentation i.e. horizontal flip. Train and test split was stratified using type of tumor. + + + + +Detailed architecure is given below. + + +## Possible Improvements +1. Can use transfer learning to utilize state-of-the-art model like VGG, Inception, Resnet. +2. We can use more types of image augmentation like vertical flip, brightness, zoom etc. +3. Include lovasz loss with higher weightage. +4. Learn and use Hypercolumns + +## Author: +* Aditya Jain : [Portfolio](https://adityajain.me) + +## To Read: +1. [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://arxiv.org/pdf/1505.04597.pdf) +2. [Image Segmentation, ConvNet, FCN, Unet](https://towardsdatascience.com/understanding-semantic-segmentation-with-unet-6be4f42d4b47) +3. [Up-sampling with Transposed Convolution](https://towardsdatascience.com/up-sampling-with-transposed-convolution-9ae4f2df52d0) +4. [Lovasz Loss](https://arxiv.org/abs/1705.08790) +5. [Jaccard Index - Intesection over Union](https://www.jeremyjordan.me/evaluating-image-segmentation-models/) +6. [Understanding Dice Loss](https://forums.fast.ai/t/understanding-the-dice-coefficient/5838) 7. [Another Image Segmentation Problem](https://github.com/adityajn105/TGS-Salt-Identification-Image-Segmentation-) \ No newline at end of file