AxonDeepSeg: Automatic Myelin and Axon Segmentation Using Deep Learning

Mathieu Boudreau Presenter
Montreal Heart Institute
Montreal, Quebec 
Software Demonstrations 
Quantitative MRI techniques that can probe tissue microstructure, such as diffusion MRI (NODDI, AxCaliber) (Duval 2016), magnetization transfer (Sled 2018), and myelin water fraction imaging (Alonso-Ortiz 2015) are under constant scrutiny when it comes to biological specificity (e.g. axon diameter, myelin density, g-ratio). One way to properly validate those techniques is histology, whereby a piece of tissue is imaged at the nanometer scale in order to derive statistics about the morphometrics of the cells within an equivalent MRI-voxel (e.g., axon diameter distribution). However, manually segmenting histology images is a time consuming endeavour that is prone to inconsistent labelling by the person doing the job or between multiple labellers. Several axon/myelin segmentation tools have been proposed using conventional image processing techniques (Liu 2011), however they are not flexible to multiple modalities, are not fully automatic, and don't harness the entire information potential of the images. We propose a deep learning approach in order to overcome these limitations, and to offer the neuroscience community a free and open-source alternative for segmenting myelin and axons in their histology slides. We also offer a graphical user interface (GUI) allowing for additional manual corrections if needed.