Competition link: https://www.kaggle.com/c/ultrasound-nerve-segmentation/

The objective of this competition was to segment ultrasound images to find the pixels in each image containing a nerve. I implemented a U-net style architecture in Keras (https://arxiv.org/abs/1505.04597)

Preprocessing:

• Each image was z-scored by subtracting the mean and diving by the std of the training set
• Each mask was normalized in the range 0-1 by dividing by 255

Without augmentation, this model gave accuracy of ~0.5

I then added the following data augmentations:

• 30 degree rotations
• vertical and horizontal shifts
• vertical and horiztonal flips
• zooms

Later adding Spatial Dropout between each U-net skip connection was able to boost accuracy from 0.61 to 0.67