Round Corner
Department of Computer and Information Science


Smart Image Segmentation


Brain injury in newborns accounts for more than 9 million years lived with disability worldwide. The functional consequences of early brain injury in newborns are commonly seen months or even years after birth, delaying therapeutic interventions and leaving families with uncertainty about their child’s health status for months and years.

Classification of movement characteristics and movement patterns in spontaneous movements in young infants has in recent years been presented as the most accurate method for early identification of infants who will later develop serious motor problems and reduced motor function (like cerebral palsy). Today, characteristic of such normal and abnormal infant spontaneous movement could be objectively captured and processed by computer software from video recordings but the procedures are so far too imprecise, cumbersome and time consuming for clinical use.

Based on a database of 900 standardized video recordings at St. Olavs University Hospital of infants at risk of neurological dysfunctions from Norway, USA and India, this project aims at solving the following tasks:

1) To assess the movement (x and y coordinates) of infant body segments (legs, arms, trunk and head) from the database of video recordings

2) Make this procedure time effective, feasible and available for researchers within the medical field without any technical or computer vision expertise.


To achieve the above goals, this project is divided into the following sub-projects:

I. Investigate the possibility of applying existing state-of-the-art deeplearning methods to solve the above tasks. As part of this, the candidate is expected to perform a state-of-the-art literature review, and implement the most relevant method(s) that can solve the problem.

II. Using results from I., an important task is to extend the developed method to make it as effective and feasible as possible. This includes evaluating the method with respect to applicability.

Hovedveileder: Heri Ramampiaro, IDI.
Biveiledere: Lars Adde, LBK, Fakultet for medisin og helsevitenskap
og Espen Ihlen, INM, Fakultet for medisin og helsevitenskap



Heri Ramampiaro Heri Ramampiaro
Associate Professor
205 IT-bygget
735 91459 
NTNU logo