Author, as appears in the article.: Roszkowiak, Lukasz; Korzynska, Anna; Siemion, Krzysztof; Zak, Jakub; Pijanowska, Dorota; Bosch, Ramon; Lejeune, Marylene; Lopez, Carlos;

Department: Ciències Mèdiques Bàsiques

URV's Author/s: Lejeune, Marylène Marie

Keywords: Regulatory t-cells Nuclei Mitosis detection Histopathology Classification

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.

Thematic Areas: Zootecnia / recursos pesqueiros Saúde coletiva Química Psicología Odontología Nutrição Multidisciplinary sciences Multidisciplinary Medicina veterinaria Medicina iii Medicina ii Medicina i Materiais Matemática / probabilidade e estatística Letras / linguística Interdisciplinar Geografía Geociências Farmacia Engenharias iv Engenharias iii Engenharias ii Enfermagem Educação física Educação Economia Ciências biológicas iii Ciências biológicas ii Ciências biológicas i Ciências ambientais Ciências agrárias i Ciência de alimentos Ciência da computação Biotecnología Biodiversidade Astronomia / física

licence for use: https://creativecommons.org/licenses/by/3.0/es/

Author's mail: marylenemarie.lejeune@urv.cat

Author identifier: 0000-0001-8441-9404

Record's date: 2023-02-19

Papper version: info:eu-repo/semantics/publishedVersion

Link to the original source: https://www.nature.com/articles/s41598-021-88611-y

Licence document URL: http://repositori.urv.cat/ca/proteccio-de-dades/

Papper original source: Scientific Reports. 11 (1):

APA: Roszkowiak, Lukasz; Korzynska, Anna; Siemion, Krzysztof; Zak, Jakub; Pijanowska, Dorota; Bosch, Ramon; Lejeune, Marylene; Lopez, Carlos; (2021). System for quantitative evaluation of DAB&H-stained breast cancer biopsy digital images (CHISEL). Scientific Reports, 11(1), -. DOI: 10.1038/s41598-021-88611-y

Article's DOI: 10.1038/s41598-021-88611-y

Entity: Universitat Rovira i Virgili

Journal publication year: 2021

Publication Type: Journal Publications