Deep learning for multi-class semantic segmentation enables colorectal cancer detection and classification in digital pathology images

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Bokhorst, John-MelleRadboud Univ Nijmegen, Netherlands (author)

Deep learning for multi-class semantic segmentation enables colorectal cancer detection and classification in digital pathology images

Article/chapterEnglish2023

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NATURE PORTFOLIO,2023
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LIBRIS-ID:oai:DiVA.org:liu-196889
https://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-196889URI
https://doi.org/10.1038/s41598-023-35491-zDOI

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Language:English
Summary in:English

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Funding Agencies|Dutch Cancer Society [10602/2016-2]; Alpe dHuZes / Dutch Cancer Society Fund [KUN 2014-7032]; European Unions Horizon 2020 research and innovation programme [825292]
In colorectal cancer (CRC), artificial intelligence (AI) can alleviate the laborious task of characterization and reporting on resected biopsies, including polyps, the numbers of which are increasing as a result of CRC population screening programs ongoing in many countries all around the globe. Here, we present an approach to address two major challenges in the automated assessment of CRC histopathology whole-slide images. We present an AI-based method to segment multiple (n=14 ) tissue compartments in the H &E-stained whole-slide image, which provides a different, more perceptible picture of tissue morphology and composition. We test and compare a panel of state-of-the-art loss functions available for segmentation models, and provide indications about their use in histopathology image segmentation, based on the analysis of (a) a multi-centric cohort of CRC cases from five medical centers in the Netherlands and Germany, and (b) two publicly available datasets on segmentation in CRC. We used the best performing AI model as the basis for a computer-aided diagnosis system that classifies colon biopsies into four main categories that are relevant pathologically. We report the performance of this system on an independent cohort of more than 1000 patients. The results show that with a good segmentation network as a base, a tool can be developed which can support pathologists in the risk stratification of colorectal cancer patients, among other possible uses. We have made the segmentation model available for research use on .

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Nagtegaal, Iris D.Radboud Univ Nijmegen, Netherlands (author)
Fraggetta, FilippoGravina Hosp, Italy (author)
Vatrano, SimonaGravina Hosp, Italy (author)
Mesker, WilmaLeids Univ, Netherlands (author)
Vieth, MichaelFriedrich Alexander Univ Erlangen Nuremberg, Germany (author)
van der Laak, JeroenLinköpings universitet,Avdelningen för diagnostik och specialistmedicin,Medicinska fakulteten,Centrum för medicinsk bildvetenskap och visualisering, CMIV,Region Östergötland, Klinisk patologi,Radboud Univ Nijmegen, Netherlands(Swepub:liu)jerva26 (author)
Ciompi, FrancescoRadboud Univ Nijmegen, Netherlands (author)
Radboud Univ Nijmegen, NetherlandsGravina Hosp, Italy (creator_code:org_t)

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In:Scientific Reports: NATURE PORTFOLIO13:12045-2322

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