Improved Automatic Detection and Segmentation of Cell Nuclei in Histopathology Images

Abstract

Automatic segmentation of cell nuclei is an essential step in image cytometry and histometry. Despite substantial progress, there is a need to improve accuracy, speed, level of automation, and adaptability to new applications. This paper presents a robust and accurate novel method for segmenting cell nuclei using a combination of ideas. The image foreground is extracted automatically using a graph-cuts-based binarization. Next, nuclear seed points are detected by a novel method combining multiscale Laplacian-of-Gaussian filtering constrained by distance-map-based adaptive scale selection. These points are used to perform an initial segmentation that is refined using a second graph-cuts-based algorithm incorporating the method of alpha expansions and graph coloring to reduce computational complexity. Nuclear segmentation results were manually validated over 25 representative images (15 in vitro images and 10 in vivo images, containing more than 7400 nuclei) drawn from diverse cancer histopathology studies, and four types of segmentation errors were investigated. The overall accuracy of the proposed segmentation algorithm exceeded 86%. The accuracy was found to exceed 94% when only over- and undersegmentation errors were considered. The confounding image characteristics that led to most detection/segmentation errors were high cell density, high degree of clustering, poor image contrast and noisy background, damaged/irregular nuclei, and poor edge information. We present an efficient semiautomated approach to editing automated segmentation results that requires two mouse clicks per operation.

Three‐dimensional segmentation of nuclei and mitotic chromosomes for the study of cell divisions in live Drosophila embryos

“…To further evaluate our segmentation method, we compared it with a variant of Otsu's adaptive thresholding (13) and the automated nuclear segmentation module of the FAR-SIGHT toolkit (20,40). Our intention was to assess how existing open-source tools developed for different 3D images of fluorescently labeled nuclei performs on our data.…”

“…A gradient flow tracking algorithm was used to identify the nuclei in 3D image stacks of C. elegans and zebrafish embryos (19). A combination of graph cuts and multiscale Laplacian of Gaussian (LOG) was used to detect cell nuclei in histopathology images (20). A hybrid blob-slice model approach first detects nuclear slices in 2D images and then fits these slices to a 3D model (21).…”

Robust Cell Detection and Segmentation in Histopathological Images Using Sparse Reconstruction and Stacked Denoising Autoencoders

“…We evaluate the proposed detection method through both qualitative and quantitative comparison with four state of the arts, including Laplacian-of-Gaussian (LoG) [1], iterative radial voting (IRV) [7], and image-based tool for counting nuclei (ITCN) [3], and singlepass voting (SPV) [8]. The qualitative comparison of two patches is shown in Figure 3.…”

Comparison of the spatial organization in colorectal tumors using second-order statistics and functional ANOVA

“…Segmenting the accurate edges of these objects is a very challenging issue [9]. However, in our context, we are interested in the object locations organization.…”