  Artificial Intelligence (AI) can potentially support histopathologists in the
diagnosis of a broad spectrum of cancer types. In colorectal cancer (CRC), 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
automated assessment of CRC histopathology whole-slide images. First, we
present an AI-based method to segment multiple 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. Second, we use the best performing AI model as the basis
for a computer-aided diagnosis system (CAD) 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 1,000
patients. The results show the potential of such an AI-based system to assist
pathologists in diagnosis of CRC in the context of population screening. We
have made the segmentation model available for research use on
https://grand-challenge.org/algorithms/colon-tissue-segmentation/.
