Towards Explainable Automated Neuroanatomy
Kui Qian Litao Qiao Beth Friedman Edward O'Donnell David Kleinfeld Yoav Freund
We present a novel method for quantifying the microscopic structure of brain
tissue. It is based on the automated recognition of interpretable features
obtained by analyzing the shapes of cells. This contrasts with prevailing
methods of brain anatomical analysis in two ways. First, contemporary methods
use gray-scale values derived from smoothed version of the anatomical images,
which dissipated valuable information from the texture of the images. Second,
contemporary analysis uses the output of black-box Convolutional Neural
Networks, while our system makes decisions based on interpretable features
obtained by analyzing the shapes of individual cells. An important benefit of
this open-box approach is that the anatomist can understand and correct the
decisions made by the computer. Our proposed system can accurately localize and
identify existing brain structures. This can be used to align and coregistar
brains and will facilitate connectomic studies for reverse engineering of brain
circuitry.