FLUORESCENCE MICROSCOPY IMAGES SEGMENTATION AND ANALYSIS USING MACHINE LEARNING

Shuo Han
2020
Microscopy image analysis can provide substantial information for clinical study and understanding of the biological structure. Two-photon microscopy is a type of fluorescence microscopy that can visualize deep into tissue with near-infrared excitation light. Large 3D image volumes of complex subcellular are often produced, which calls for automatic image analysis techniques. Automatic methods that can obtain nuclei quantity in microscopy image volumes are needed for biomedical research and
more » ... al research and clinical diagnosis. In general, several challenges exist for counting nuclei in 3D image volumes. These include "crowding" and touching of nuclei, overlapping of two or more nuclei, and shape and size variances of the nuclei. In this thesis, a 3D nuclei counter using two different generative adversarial networks (GAN) is proposed and evaluated. Synthetic data that resembles real microscopy image is generated with a GAN. The synthetic data is used to train another 3D GAN network that counts the number of nuclei. Our approach is evaluated with respect to the number of ground truth nuclei and compared with common ways of counting used in the biological research.Fluorescence microscopy 3D image volumes of rat kidneys are used to test our 3D nuclei counter. The evaluation of both networks shows that the proposed technique is successful for counting nuclei in 3D. Then, a 3D segmentation and classification method to segment and identify individual nuclei in fluorescence microscopy volumes without having ground truth volumes is introduced. Three dimensional synthetic data is generated using the Recycle-GAN with the Hausdorff distance loss introduced into preserve the shape of individual nuclei. Realistic microscopy image volumes with nuclei segmentation mask and nucleus boundary ground truth volumes are generated.A subsequent 3D CNN with a regularization term that discourage detection out of nucleus boundary is used to detect and segment nuclei. Nuclei boundary refinement is then performed to enhance nuclei segmentation. Experimental [...]
doi:10.25394/pgs.12743642 fatcat:pd77isrdznbktatx2o6g4tmhvi