1 research outputs found
Deep-learning based segmentation of challenging myelin sheaths
The segmentation of axons and myelin in electron
microscopy images allows neurologists to highlight the density of
axons and the thickness of the myelin surrounding them. These
properties are of great interest for preventing and anticipating
white matter diseases. This task is generally performed manually,
which is a long and tedious process.
We present an update of the methods used to compute that
segmentation via machine learning. Our model is based on
the architecture of the U-Net network. Our main contribution
consists in using transfer learning in the encoder part of the UNet network, as well as test time augmentation when segmenting.
We use the SE-Resnet50 backbone weights which was pre-trained
on the ImageNet 2012 dataset.
We used a data set of 23 images with the corresponding
segmented masks, which also was challenging due to its extremely
small size. The results show very encouraging performances
compared to the state-of-the-art with an average precision of
92% on the test images. It is also important to note that the
available samples were taken from elderly mices in the corpus
callosum. This represented an additional difficulty, compared to
related works that had samples taken from the spinal cord or
the optic nerve of healthy individuals, with better contours and
less debri