Abinash Pant hails from Kathmandu, Nepal, although he has lived and worked extensively in India over the last decade. He has completed his Bachelors of Technology from NIT Allahbad in India on receiving a scholarship from the Government of India. He followed it up with a Masters in Computer Vision from the Universite de Bourgogne in France. Apart from many other internship projects and experiences, his master’s thesis was in the field of Hippocampus Segmentation as an early bio marker for Alzheimer's disease at CSIRO, Australia wherein he focused on researching the state of the algorithms proposed in the literature for hippocampus segmentation and developing an automated pipeline for hippocampus segmentation using a database of manually segmented hippocampus for training. Currently he is a PhD research scholar at the Besa GmbH and University of Muenster on the topic “Developing automatic segmentation algorithms for MRIs of children”. Through this doctoral research, he hopes to work towards development of advance and robust segmentation algorithm which will eventually help us gain a better understanding of the neurocognitive disorders in children. His areas of interest include Machine Learning, Medical Imaging and Image Processing. In his free time, he enjoys reading, travelling and solving puzzles.

Electroencephalography (EEG) is a non-invasive and widely used technique to measure electric potential differences that result from current flow in active brain regions. Localization of the relevant brain regions causing the effects on the scalp surface cannot be performed unequivocally due to the so-called “inverse problem”, i.e. different underlying brain activity patterns can cause the same EEG representation. One important factor for identifying the truly active brain regions is having very good knowledge about the so-called “forward model”. As electric current is diffracted differently and reflected to varying degrees by different tissues, it is important to know the anatomy of the head, as well as the conductivity properties of the head tissues as precisely as possible.
Head models representing the conductivity distribution inside the subject's or patient's head play an important role for the solution of the forward, and thus, also the inverse problem. Realistic models taking into account the individual anatomy of the subject can greatly improve the accuracy of source analysis.
Generation of the head model can be accurately estimated with the help of structural imaging techniques. These structural images can be used to segment the different brain regions.
An accurate head model could be obtained by using these images to estimate the underlying structure by segmenting the different brain regions. Pipeline to generate head models using segmentation is particularly challenging for children; smaller anatomical structures, motion artifacts, and an anatomy which varies greatly for children at different ages require the development of new algorithms for an accurate and robust segmentation. Development will focus on Bayesian segmentation approaches representing the typical anatomy using Markov Random Field models. Furthermore, age-specific a-priori knowledge on the anatomy of the children's heads will be taken into account.