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 investigated the automation of both microtubule detection and segmentation using deep convolutional neural networks. To this end, we have adapted the ISOODL network introduced by Böhm et al. This network not only detects, but also segments microtubules. Microtubule locations are represented as reference points at their center of mass. Separation between microtubules is introduced by predicting both xz- and yz-sheared masks. At every predicted reference point, post-processing operations revert the shearing operation. This results in microtubule detections and their corresponding segmentations. We have concluded that the ISOODL network is able to detect and segment microtubules. In an attempt to further improve the segmentation of overlapping microtubules, we developed a new network to be used exclusively on overlapping microtubules. This network
predicts the segmentation mask of every microtubule in a different channel of the output. However, the developed ’overlap network’ was unable to produce satisfactory segmentation results. Consequently, this data structure does not facilitate separation between overlapping microtubules.