We are interested in the outer part of the brain, the cortex.

More precisely, we are interested in segmenting the 6 different layers of the cortex in 3D.

Creating an expertly labelled training dataset with each 2D section (shown in figure 1) is unfeasible. Instead of giving as input a 2D image of one section of the brain, we give as input 1D vectors with information from across the cortex, extracted from smaller portions of manually labelled cortex

.. figure:: images/big_brain_section.png

:align: center

through the cortex. As the images were stained for cell bodies, the intensity of each pixel of these rays represents the cell densities

and sizes contained in the cortical layer to which the pixel belongs. Since the 6 cortical layers

have different properties (cell density and size), the intensity profile can be used to

Each ray has 2 input channels, one representing the smoothed intensity and the other,

the raw version, as shown in Figure 3. The next figure, Figure 4, shows the

ground truth segmentation map, where each different color represent

a different label. The purple color indicate that these pixels are

outside the cortex, while the 6 other colors represent the 6 cortical layers.

For example, the first layer of the cortex is between pixels ~ 35-55. The cortex

.. figure:: images/raw_smooth.png

Each pixel is labelled between 2 classes : polype or background.

The size of the images vary. We use data augmentation for training, as specified

the data augmentation is necessary for training with batch size greater than 1

in order to have same image size with a random cropping. If no random cropping,

the batch size for the training set must be set to 1, like for validation and test

The data is from ISBI challenge and can be found `here <http://brainiac2.mit.edu/isbi_challenge/home>`_.

We use data augmentation for training, as specified

Model

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