Journal article
Mapping cell-to-tissue graphs across human placenta histology whole slide images using deep learning with HAPPY
- Abstract:
- Accurate placenta pathology assessment is essential for managing maternal and newborn health, but the placenta’s heterogeneity and temporal variability pose challenges for histology analysis. To address this issue, we developed the ‘Histology Analysis Pipeline.PY’ (HAPPY), a deep learning hierarchical method for quantifying the variability of cells and micro-anatomical tissue structures across placenta histology whole slide images. HAPPY differs from patch-based features or segmentation approaches by following an interpretable biological hierarchy, representing cells and cellular communities within tissues at a single-cell resolution across whole slide images. We present a set of quantitative metrics from healthy term placentas as a baseline for future assessments of placenta health and we show how these metrics deviate in placentas with clinically significant placental infarction. HAPPY’s cell and tissue predictions closely replicate those from independent clinical experts and placental biology literature.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Files:
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(Preview, Version of record, pdf, 11.0MB, Terms of use)
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- Publisher copy:
- 10.1038/s41467-024-46986-2
Authors
+ Engineering and Physical Sciences Research Council
More from this funder
- Grant:
- EP/S02428X/1
- 2279808
- Publisher:
- Springer Nature
- Journal:
- Nature Communications More from this journal
- Volume:
- 15
- Issue:
- 1
- Article number:
- 2710
- Publication date:
- 2024-03-28
- Acceptance date:
- 2024-03-15
- DOI:
- EISSN:
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2041-1723
- Pmid:
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38548713
- Language:
-
English
- Keywords:
- Pubs id:
-
1931964
- Local pid:
-
pubs:1931964
- Deposit date:
-
2024-05-28
Terms of use
- Copyright holder:
- Vanea et al.
- Copyright date:
- 2024
- Rights statement:
- Copyright © 2024, The Author(s). This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
- Licence:
- CC Attribution (CC BY)
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