Boundary segmentation for fluorescence microscopy using steerable filters

Abstract

Fluorescence microscopy is used to image multiple subcellular structures in living cells which are not readily observed using conventional optical microscopy. Moreover, two-photon microscopy is widely used to image structures deeper in tissue. Recent advancement in fluorescence microscopy has enabled the generation of large data sets of images at different depths, times, and spectral channels. Thus, automatic object segmentation is necessary since manual segmentation would be inefficient and biased. However, automatic segmentation is still a challenging problem as regions of interest may not have well defined boundaries as well as non-uniform pixel intensities. This paper describes a method for segmenting tubular structures in fluorescence microscopy images of rat kidney and liver samples using adaptive histogram equalization, foreground/background segmentation, steerable filters to capture directional tendencies, and connected-component analysis. The results from several data sets demonstrate that our method can segment tubular boundaries successfully. Moreover, our method has better performance when compared to other popular image segmentation methods when using ground truth data obtained via manual segmentation.

Tubule Segmentation of Fluorescence Microscopy Images Based on Convolutional Neural Networks With Inhomogeneity Correction

“…The second row shows segmentation results of various 3D methods such as 3D region-based active contours [10] (3Dac), 3D active contours with inhomogeneity correction [11] (3DacIC), and 3D Squassh presented in [12] (3Dsquassh). Similarly, the third row portrays various segmentation methods particularly designed for tubular structure segmentation such as ellipse fitting method presented in [15] (Ellipse Fitting), the Jelly filling method in [20] (Jelly Filling), and tubule segmentation using steerable filter [21] (Steerable Filter). Finally, the last row shows segmentation results of our proposed CNN architecture without inhomogeneity correction [27] (2DCNN) and with inhomogeneity correction (2DCNNIC).…”

“…More recently, one method used to segment tubular structures was delineating tubule boundaries followed by ellipse fitting to close the boundaries while considering intensity inhomogeneity [15]. Another method known as Jelly filling [20] utilized adaptive thresholding, component analysis, and 3D consistency to achieve segmentation, whereas a method for tubule boundary segmentation used steerable filters to generate potential seeds from which to grow tubule boundaries followed by tubule/lumen separation and 3D propagation to generate segmented tubules in 3D [21]. Previous methods, however, focused on segmenting bound-aries of tubule membrane.…”