Intrinsically explainable deep learning architecture for semantic segmentation of histological structures in heart tissue

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11120%2F24%3A43927055" target="_blank" >RIV/00216208:11120/24:43927055 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216208:11130/24:10480529

Výsledek na webu

<a href="https://doi.org/10.1016/j.compbiomed.2024.108624" target="_blank" >https://doi.org/10.1016/j.compbiomed.2024.108624</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.compbiomed.2024.108624" target="_blank" >10.1016/j.compbiomed.2024.108624</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Intrinsically explainable deep learning architecture for semantic segmentation of histological structures in heart tissue

Popis výsledku v původním jazyce

BACKGROUND: Analysis of structures contained in tissue samples and the relevant contextual information is of utmost importance to histopathologists during diagnosis. Cardiac biopsies require in-depth analysis of the relationships between biological structures. Statistical measures are insufficient for determining a model&apos;s viability and applicability in the diagnostic process. A deeper understanding of predictions is necessary in order to support histopathologists. METHODS: We propose a method for providing supporting information in the form of segmentation of histological structures to histopathologists based on these principles. The proposed method utilizes nuclei type and density information in addition to standard image input provided at two different zoom levels for the semantic segmentation of blood vessels, inflammation, and endocardium in heart tissue. RESULTS: The proposed method was able to reach state-of-the-art segmentation results. The overall quality and viability of the predictions was qualitatively evaluated by two pathologists and a histotechnologist. CONCLUSIONS: The decision process of the proposed deep learning model utilizes the provided information sources correctly and simulates the decision process of histopathologists via the usage of a custom-designed attention gate that provides a combination of spatial and encoder attention mechanisms. The implementation is available at https://github.com/mathali/IEDL-segmentation-of-heart-tissue.

Název v anglickém jazyce

Intrinsically explainable deep learning architecture for semantic segmentation of histological structures in heart tissue

Popis výsledku anglicky

BACKGROUND: Analysis of structures contained in tissue samples and the relevant contextual information is of utmost importance to histopathologists during diagnosis. Cardiac biopsies require in-depth analysis of the relationships between biological structures. Statistical measures are insufficient for determining a model&apos;s viability and applicability in the diagnostic process. A deeper understanding of predictions is necessary in order to support histopathologists. METHODS: We propose a method for providing supporting information in the form of segmentation of histological structures to histopathologists based on these principles. The proposed method utilizes nuclei type and density information in addition to standard image input provided at two different zoom levels for the semantic segmentation of blood vessels, inflammation, and endocardium in heart tissue. RESULTS: The proposed method was able to reach state-of-the-art segmentation results. The overall quality and viability of the predictions was qualitatively evaluated by two pathologists and a histotechnologist. CONCLUSIONS: The decision process of the proposed deep learning model utilizes the provided information sources correctly and simulates the decision process of histopathologists via the usage of a custom-designed attention gate that provides a combination of spatial and encoder attention mechanisms. The implementation is available at https://github.com/mathali/IEDL-segmentation-of-heart-tissue.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

30109 - Pathology

Projekt

—

Návaznosti

N - Vyzkumna aktivita podporovana z neverejnych zdroju

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Computers in Biology and Medicine

ISSN

0010-4825

e-ISSN

1879-0534

Svazek periodika

177

Číslo periodika v rámci svazku

July

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

11

Strana od-do

108624

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-85193784556