Microtubule Detection and Instance Segmentation using Deep Convolutional Neural Networks

Microtubules are long cylindrical polymers, assembled from tubulin proteins. Microtubule ends can be visualized using fluorescence and confocal microscopy. This allows for the study of microtubule dynamics. However, the manual annotation of microtubules is laborious, which is why automated tracking methods are used. In this project we have...

Automatic Segmentation of Low-Field MRI Brain Scans by Integrating Analytic and Deep Learning Techniques

Hydrocephalus is a disease where an excess of cerebrospinal fluid (CSF) is built up in the brain. It affects approximately 180.000 infants per year in sub-Saharan Africa. Magnetic resonance imaging (MRI) is an advantageous imaging method to diagnose hydrocephalus and examine the amount of fluid in the brain for treatment. Unfortunately, in sub...

Longitudinal diffusion MRI analysis using Segis-Net: A single-step deep-learning framework for simultaneous segmentation and registration

This work presents a single-step deep-learning framework for longitudinal image analysis, coined Segis-Net. To optimally exploit information available in longitudinal data, this method concurrently learns a multi-class segmentation and nonlinear registration. Segmentation and registration are modeled using a convolutional neural network and...