Automated seeding-based nuclei segmentation in nonlinear optical microscopy

Nonlinear optical (NLO) microscopy based, e. g., on coherent anti-Stokes Raman scattering (CARS) or two photon excited fluorescence (TPEF) is a fast label-free imaging technique, with a great potential for biomedical applications. However, NLO microscopy as a diagnostic tool is still in its infancy, there is a lack of robust and durable nuclei segmentation methods capable for an accurate image processing in cases of variable image contrast, nuclear density and type of investigated tissue. Nonetheless, such algorithms specically adapted to NLO microscopy present one prerequisite for the technology to be routinely used, e. g., in pathology or intra-operatively for surgical guidance. In this work, we compare the applicability of different seeding and boundary detection methods to NLO microscopic images in order to develop an optimal seeding-based approach, capable of accurate segmentation of both TPEF and CARS images. Among different methods, the Laplacian of Gaussian (LoG) lter showed the best accuracy for the seeding of the image, while a modified seeded watershed segmentation was the most accurate in the task of boundary detection. The resulting combination of these methods, followed by the verifcation of the detected nuclei performs high average sensitivity and specificity being applied to various types of NLO microscopy images.