Quantification of histopathological findings using a novel image analysis platform

Abstract

Digital pathology, including image analysis and automatic diagnosis of pathological tissue, has been developed remarkably. HALO is an image analysis platform specialized for the study of pathological tissues, which enables tissue segmentation by using artificial intelligence. In this study, we used HALO to quantify various histopathological changes and findings that were difficult to analyze using conventional image processing software. Using the tissue classifier module, the morphological features of degeneration/necrosis of the hepatocytes and muscle fibers, bile duct in the liver, basophilic tubules and hyaline casts in the kidney, cortex in the thymus, and red pulp, white pulp, and marginal zone in the spleen were learned and separated, and areas of interest were quantified. Furthermore, using the cytonuclear module and vacuole module in combination with the tissue classifier module, the number of erythroblasts in the red pulp of the spleen and each area of acinar cells in the parotid gland were quantified. The results of quantitative analysis were correlated with the histopathological grades evaluated by pathologists. By using artificial intelligence and other functions of HALO, we recognized morphological features, analyzed histopathological changes, and quantified the histopathological grades of various findings. The analysis of histopathological changes using HALO is expected to support pathology evaluations.

Current status and prospects for quantitative analysis of digital image of pathological specimen using image processing software including artificial intelligence

“…HALO has an image analysis function based on parameter settings, including color and form, of pathological tissues. By adding a functional module specialized for analysis of various morphologies of pathological tissues, these quantitative analyses are possible [8,[13][14][15][16][17]. Furthermore, by learning and recognizing the morphological structures and features of pathological tissues, these regions (tissue class) and structures can be easily separated and quantified.…”

“…Although vacuoles derived from lipids or phospholipids can be analyzed by IPP [7,12], the size of vacuoles is easily quantified using HALO ( Fig. 1A-C) [8]. The area of fibrosis (stained with azan stain, Sirius red stain, or Masson's trichrome stain) was quantifiable using both IPP and HALO.…”

“…Therefore, automated image analysis should be accompanied by visual examination. Hepatocyte hypertrophy was analyzed using Tissue Studio or HALO [7,8], which includes the use of an algorithm that simulates the cytoplasm by recognizing hepatocyte nuclei. However, the nucleus does not appear in extremely enlarged hepatocytes, which cannot be simulated and evaluated.…”

“…Recently, with the development of information technology, digital pathology has also developed remarkably. An increasing number of researchers have been attempting to analyze pathological tissue and diagnose the pathology using machine learning or artificial intelligence (AI) [2][3][4][5][6][7][8]. Notably, in the clinical field, some cancer tissues can be recognized by automatic diagnosis software with high accuracy [9][10][11].…”

“…Therefore, it is not easy for everyone to analyze tissue image, and this practice tends to be of low versatility. Recently, we have analyzed and recognized tissue morphological changes using HALO which is an image analysis platform specialized for the study of pathological tissues [8]. By learning the morphological features of pathological tissues using AI, including random forest or neural network, it is possible to recognize complex features of pathological tissues that could not be recognized and analyzed by conventional software.…”