A fast and robust hepatocyte quantification algorithm including vein processing

Abstract

BackgroundQuantification of different types of cells is often needed for analysis of histological images. In our project, we compute the relative number of proliferating hepatocytes for the evaluation of the regeneration process after partial hepatectomy in normal rat livers.ResultsOur presented automatic approach for hepatocyte (HC) quantification is suitable for the analysis of an entire digitized histological section given in form of a series of images. It is the main part of an automatic hepatocyte quantification tool that allows for the computation of the ratio between the number of proliferating HC-nuclei and the total number of all HC-nuclei for a series of images in one processing run. The processing pipeline allows us to obtain desired and valuable results for a wide range of images with different properties without additional parameter adjustment. Comparing the obtained segmentation results with a manually retrieved segmentation mask which is considered to be the ground truth, we achieve results with sensitivity above 90% and false positive fraction below 15%.ConclusionsThe proposed automatic procedure gives results with high sensitivity and low false positive fraction and can be applied to process entire stained sections.

Segmentation and 3D Reconstruction of Animal Tissues in Histological Images

“…A Gaussian filter uses a 2D convolution operator in order to blur the input images and remove details and noise from them. The kernel used in the convolution represents the shape of a Gaussian hump (Ivanovska et al 2010). A 2D Gaussian filter has the following form:…”

“…where ( , ) are the spatial coordinates and is the standard deviation of the distribution (Ivanovska et al 2010).…”

Image-based quantification of histological features as a function of spatial location using the Tissue Positioning System

“…In the liver, applications have focused on the segmentation of cell nuclei and the prediction of ploidy states. 7 , 8 An unresolved problem is how to determine the position of any given modality (i.e. cell, clone, gene expression value) on an image with respect to important zonal landmarks.…”

Image-based quantification of histological features as a function of spatial location using the Tissue Positioning System

“…Despite recent advances in computer vision and deep learning, automated analysis of histology images is still immature and has not yet met the challenge of defining zonation. In the liver, applications have focused on the segmentation of cell nuclei and the prediction of ploidy states 7,8 . An unresolved problem is how to determine the position of any given modality (i.e.…”