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...

Analyzing Components of a Transformer under Different Dataset Scales in 3D Prostate CT Segmentation

Literature on medical imaging segmentation claims that hybrid UNet models containing both Transformer and convolutional blocks perform better than purely convolutional UNet models. This recently touted success of hybrid Transformers warrants an investigation into which of its components contribute to its performance. Also, previous work has a...

Automated segmentation of lacunes of presumed vascular origin in brain MRI scans

Lacunes of presumed vascular origin (lacunes) are small lesions in the brain and are an important indicator of cerebral small vessel disease (cSVD). To gain more insight in this disease, obtaining more information about the shape, size and location of lacunes is essential. However, manual segmentation (the voxel-wise labeling of lacunes in a...