ArticleJournal of pathology informatics2022
Novel Pixelwise Co-Registered Hematoxylin-Eosin and Multiphoton Microscopy Image Dataset for Human Colon Lesion Diagnosis.
Article in Journal of pathology informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Towards next-generation diagnostic pathology: AI-empowered label-free multiphoton microscopy.Light, science & applications · 2024Review
- Combined flat-field and frequency filter approach to correcting artifacts of multichannel two-photon microscopy.Journal of biomedical optics · 2024Article
- Context-aware augmentation for liver lesion segmentation: shape uniformity, expansion limit and fusion strategy.Quantitative imaging in medicine and surgery · 2023Article
- Toward next-generation endoscopes integrating biomimetic video systems, nonlinear optical microscopy, and deep learning.Biophysics reviews · 2023Review
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Authors and funding
12 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Colorectal cancer presents one of the most elevated incidences of cancer worldwide. Colonoscopy relies on histopathology analysis of hematoxylin-eosin (H&E) images of the removed tissue. Novel techniques such as multi-photon microscopy (MPM) show promising results for performing real-time optical biopsies. However, clinicians are not used to this imaging modality and correlation between MPM and H&E information is not clear. The objective of this paper is to describe and make publicly available an extensive dataset of fully co-registered H&E and MPM images that allows the research community to analyze the relationship between MPM and H&E histopathological images and the effect of the semantic gap that prevents clinicians from correctly diagnosing MPM images. The dataset provides a fully scanned tissue images at 10x optical resolution (0.5 µm/px) from 50 samples of lesions obtained by colonoscopies and colectomies. Diagnostics capabilities of TPF and H&E images were compared. Additionally, TPF tiles were virtually stained into H&E images by means of a deep-learning model. A panel of 5 expert pathologists evaluated the different modalities into three classes (healthy, adenoma/hyperplastic, and adenocarcinoma). Results showed that the performance of the pathologists over MPM images was 65% of the H&E performance while the virtual staining method achieved 90%. MPM imaging can provide appropriate information for diagnosing colorectal cancer without the need for H&E staining. However, the existing semantic gap among modalities needs to be corrected.
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