Multiple sclerosis lesion segmentation from Longitudinal brain MRI

Supervision: Prof. Dr. Nassir Navab, Dr. Seong Tae Kim, Ashkan Khakzar

Abstract

Longitudinal medical data is defined that imaging data are obtained at more than one time-point where subjects are scanned repeatedly over time. Longitudinal medical image analysis is a very important topic because it can solve some difficulties which are limited when only spatial data is utilized. Temporal information could provide very useful cues for accurately and reliably analyzing medical images. To effectively analyze temporal changes, it is required to segment region-of-interest accurately in a short time. In the series of images acquired over multiple times of imaging, available cues for segmentation become richer with the intermediate predictions. In this project, we will investigate a way to fully exploit this rich source of information.

Requirements:

• Good understanding of statistics and machine learning methods.
• Very good programming skills in Python & TensorFlow^? / PyTorch^?

Location:

• Garching

Resultant Paper

S. Denner, A. Khakzar, M. Sajid, M. Saleh, Z. Spiclin, S.T. Kim, N. Navab Spatio-temporal learning from longitudinal data for multiple sclerosis lesion segmentation The first two authors contributed equally. BrainLes? at International Conference on Medical Image Computing and Computer-Assisted Intervention. (bib)