
# Pytorch implementation of 3D UNet

This implementation is based on the orginial 3D UNet paper and adapted to be used for MRI or CT image segmentation task   
Link to the paper: [https://arxiv.org/pdf/1606.06650v1.pdf](https://arxiv.org/pdf/1606.06650v1.pdf)

## Model Architecture

The model architecture follows an encoder-decoder design which requires the input to be divisible by 16 due to its downsampling rate in the analysis path.

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`tensorboard --logdir=runs/`

	a/README.md		b/README.md
1	# Pytorch implementation of 3D UNet	1	# Pytorch implementation of 3D UNet
2		2
3	This implementation is based on the orginial 3D UNet paper and adapted to be used for MRI or CT image segmentation task	3	This implementation is based on the orginial 3D UNet paper and adapted to be used for MRI or CT image segmentation task
4	> Link to the paper: [https://arxiv.org/pdf/1606.06650v1.pdf](https://arxiv.org/pdf/1606.06650v1.pdf)	4	Link to the paper: [https://arxiv.org/pdf/1606.06650v1.pdf](https://arxiv.org/pdf/1606.06650v1.pdf)
5		5
6	## Model Architecture	6	## Model Architecture
7		7
8	The model architecture follows an encoder-decoder design which requires the input to be divisible by 16 due to its downsampling rate in the analysis path.	8	The model architecture follows an encoder-decoder design which requires the input to be divisible by 16 due to its downsampling rate in the analysis path.
9		9
	...		...
63		63
64	`tensorboard --logdir=runs/`	64	`tensorboard --logdir=runs/`