THREE DIMENSIONAL SEGMENTATION AND DETECTION OF FLUORESCENCE MICROSCOPY IMAGES

Fluorescence microscopy is an essential tool for imaging subcellular structures in tissue. Two-photon microscopy enables imaging deeper into tissue using near-infrared light. The use of image analysis and computer vision tools to detect and extract information from the images is still challenging due to the degraded microscopy volumes by blurring and noise during the image acquisition and the complexity of subcellular structures presented in the volumes. In this thesis we describe methods for segmentation and detection of fluorescence microscopy images in 3D. We segment tubule boundaries by distinguishing them from other structures using three dimensional steerable filters. These filters can capture strong directional tendencies of the voxels on a tubule boundary. We also describe multiple three dimensional convolutional neural networks (CNNs) to segment nuclei. Training the CNNs usually require a large set of labeled images which is extremely difficult to obtain in biomedical images. We describe methods to generate synthetic microscopy volumes and to train our 3D CNNs using these synthetic volumes without using any real ground truth volumes. The locations and sizes of the nuclei are detected using of our CNNs, known as the Sphere Estimation Network. Our methods are evaluated using real ground truth volumes and are shown to outperform other techniques.