  In this work, we explore the various Brain Neuron tracking techniques, which
is one of the most significant applications of Diffusion Tensor Imaging.
Tractography provides us with a non-invasive method to analyze underlying
tissue micro-structure. Understanding the structure and organization of the
tissues facilitates us with a diagnosis method to identify any aberrations and
provide acute information on the occurrences of brain ischemia or stroke, the
mutation of neurological diseases such as Alzheimer, multiple sclerosis and so
on. Time if of essence and accurate localization of the aberrations can help
save or change a diseased life. Following up with the limitations introduced by
the current Tractography techniques such as computational complexity,
reconstruction errors during tensor estimation and standardization, we aim to
elucidate these limitations through our research findings. We introduce an end
to end Deep Learning framework which can accurately estimate the most probable
likelihood orientation at each voxel along a neuronal pathway. We use
Probabilistic Tractography as our baseline model to obtain the training data
and which also serve as a Tractography Gold Standard for our evaluations.
Through experiments we show that our Deep Network can do a significant
improvement over current Tractography implementations by reducing the run-time
complexity to a significant new level. Our architecture also allows for
variable sized input DWI signals eliminating the need to worry about memory
issues as seen with the traditional techniques. The advantage of this
architecture is that it is perfectly desirable to be processed on a cloud setup
and utilize the existing multi GPU frameworks to perform whole brain
Tractography in minutes rather than hours. We evaluate our network with Gold
Standard and benchmark its performance across several parameters.
