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--- a/README.md +++ b/README.md @@ -1,58 +1,58 @@ -# Brain MRI Segmentation - -The method we use comes from this paper: -[From neonatal to adult brain -mr image segmentation in a few seconds using 3d-like fully convolutional network and transfer learning](https://www.lrde.epita.fr/wiki/Publications/xu.17.icip) - -Soft tissue segmentation. - -This project was part of the Smart India Hackathon 2019 in which our team was the runner ups. -The problem statement was **Brain MRI Segmentation using Machine Learning** given by **Department of Atomic Energy, Government of India** - -This project could be used by medical professionals for medical purposes. - - - -## Preprocessing - - - - - - - - -## Why our model? - -• Since we are using transfer learning, a novel approach in this field, so we do not need to train our model from scratch which makes it very fast in training in comparison to other models. - -• Stacking 3 successive 2D slices allows us to make a RGB image, another novel idea.This representation enables us to incorporate some 3D information, while avoiding the expensive computational and memory requirements of fully 3D FCN. - -• Using Transfer Learning we do not need many training images, so we could train our model very well only on a few training images. - -• We are also using traditional data augmentation methods like rotating, cropping and flipping the images in training set for improving our model. - -## GUI and giving input - - - - -## Output - - - -## Tumour prediction - - - -## Other regions - - - -## Contributors -[Vaibhav Shukla](https://github.com/vaibhavshukla182/) -[Abhijeet Singh](https://github.com/abhi40308) -[Omkar Ajnadkar](https://github.com/blackbird71SR) -[Govind Singh Rajpurohit](https://github.com/govind51) -[Ratna Priya](https://github.com/Ratna04priya) -[Sanath Singavarapu](https://github.com/Killer2499) +# Brain MRI Segmentation + +The method we use comes from this paper: +[From neonatal to adult brain +mr image segmentation in a few seconds using 3d-like fully convolutional network and transfer learning](https://www.lrde.epita.fr/wiki/Publications/xu.17.icip) + +Soft tissue segmentation. + +This project was part of the Smart India Hackathon 2019 in which our team was the runner ups. +The problem statement was **Brain MRI Segmentation using Machine Learning** given by **Department of Atomic Energy, Government of India** + +This project could be used by medical professionals for medical purposes. + + + +## Preprocessing + + + + + + + + +## Why our model? + +• Since we are using transfer learning, a novel approach in this field, so we do not need to train our model from scratch which makes it very fast in training in comparison to other models. + +• Stacking 3 successive 2D slices allows us to make a RGB image, another novel idea.This representation enables us to incorporate some 3D information, while avoiding the expensive computational and memory requirements of fully 3D FCN. + +• Using Transfer Learning we do not need many training images, so we could train our model very well only on a few training images. + +• We are also using traditional data augmentation methods like rotating, cropping and flipping the images in training set for improving our model. + +## GUI and giving input + + + + +## Output + + + +## Tumour prediction + + + +## Other regions + + + +## Contributors +[Vaibhav Shukla](https://github.com/vaibhavshukla182/) +[Abhijeet Singh](https://github.com/abhi40308) +[Omkar Ajnadkar](https://github.com/blackbird71SR) +[Govind Singh Rajpurohit](https://github.com/govind51) +[Ratna Priya](https://github.com/Ratna04priya) +[Sanath Singavarapu](https://github.com/Killer2499)