System for quantitative evaluation of DAB&H-stained breast cancer biopsy digital images (CHISEL)

Abstract

This study presents CHISEL (Computer-assisted Histopathological Image Segmentation and EvaLuation), an end-to-end system capable of quantitative evaluation of benign and malignant (breast cancer) digitized tissue samples with immunohistochemical nuclear staining of various intensity and diverse compactness. It stands out with the proposed seamless segmentation based on regions of interest cropping as well as the explicit step of nuclei cluster splitting followed by a boundary refinement. The system utilizes machine learning and recursive local processing to eliminate distorted (inaccurate) outlines. The method was validated using two labeled datasets which proved the relevance of the achieved results. The evaluation was based on the IISPV dataset of tissue from biopsy of breast cancer patients, with markers of T cells, along with Warwick Beta Cell Dataset of DAB&H-stained tissue from postmortem diabetes patients. Based on the comparison of the ground truth with the results of the detected and classified objects, we conclude that the proposed method can achieve better or similar results as the state-of-the-art methods. This system deals with the complex problem of nuclei quantification in digitalized images of immunohistochemically stained tissue sections, achieving best results for DAB&H-stained breast cancer tissue samples. Our method has been prepared with user-friendly graphical interface and was optimized to fully utilize the available computing power, while being accessible to users with fewer resources than needed by deep learning techniques.

A novel pre-processing approach based on colour space assessment for digestive neuroendocrine tumour grading in immunohistochemical tissue images

“…A pre-processing step was applied to select the relevant colour space representation to eliminate the staining difference. Moreover, only one colour component was chosen instead of 3 to reduce the quantityofdatatobeprocessedandlimitthecomputationalcomplexity [4].Thus,conversiontosever-…”

“…Efficientcomputer-aideddiagnosis(CAD)systems couldremovethelimitationsmentionedaboveoftraditional methods for IHC assessment. Pathologists havesuccessfullyappliedthosesystemsinthehistological analysis [3][4][5]. Most of those CAD systems require3principalstepstodeterminethepercentage ofpositivecells.Thefirstconcernsimagequalityenhancementtoreducethevariationinstaininginformation.…”

“…The C channel was chosen for further processing because it produced the best result. Anovelsegmentationandquantificationapproachto theIHCstainedslideswasproposedbyRoszkowiak et al [4].ItisbasedonaconversionfromtheRGBto theHSVcolourspace.Thatmethodallowstheseparationofdatacontainingcolourinformationfromthe datacontainingluminescenceintensityinformation. Colourspaceconversionwasalsodonebyqualitative assessment [5].…”

High Contrast Breast Cancer Biomarker Semi-Quantification and Immunohistochemistry Imaging Using Upconverting Nanoparticles

“…The challenges associated with chromogenic staining, such as DAB, open up a new avenue for improved quantifiable labling schemes and intelligent image analysis techniques to enable extraction of previously hidden information. In the last decade vast efforts have been undertaken to utilize machine learning (ML) and artificial intelligence (AI) algorithms based on conventional histopathological staining to obtain more objective diagnostic results, including prediction of diagnosis, prognosis and treatment response [16][17][18][19][20][21][22]. At the same time, immunofluorescence (IF) using fluorophore-labeled antibodies has become an alternative approach.…”