El Simard, Mika E;
Shen, Zhuoyan;
Br, Konstantin;
Abu-Eid, Rasha;
Hawkins, Maria A;
Fekete, Charles-Antoine Collins;
(2025)
Immunocto: a massive immune cell database auto-generated for histopathology.
Medical Image Analysis
, Article 103905. 10.1016/j.media.2025.103905.
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Abstract
With the advent of novel cancer treatment options such as immunotherapy, studying the tumour immune micro-environment (TIME) is crucial to inform on prognosis and understand potential response to therapeutic agents. A key approach to characterising the TIME involves combining digitised images of haematoxylin and eosin (H&E) stained tissue sections obtained in routine histopathology examination with automated immune cell detection and classification methods. In this work, we introduce a workflow to automatically generate robust single cell contours and labels from dually stained tissue sections with H&E and multiplexed immunofluorescence (IF) markers. The approach harnesses the Segment Anything Model and requires minimal human intervention compared to existing single cell databases. With this methodology, we create Immunocto, a massive, multi-million automatically generated database of 6,848,454 human cells and objects, including 2,282,818 immune cells distributed across 4 subtypes: CD4+ T cell lymphocytes, CD8+ T cell lymphocytes, CD20+ B cell lymphocytes, and CD68+/CD163+ macrophages. For each cell, we provide a 64 × 64 pixels2 H&E image at 40× magnification, along with a binary mask of the nucleus and a label. The database, which is made publicly available, can be used to train models to study the TIME on routine H&E slides. We show that deep learning models trained on Immunocto result in state-of-the-art performance for lymphocyte detection. The approach demonstrates the benefits of using matched H&E and IF data to generate robust databases for computational pathology applications.
| Type: | Article |
|---|---|
| Title: | Immunocto: a massive immune cell database auto-generated for histopathology |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.media.2025.103905 |
| Publisher version: | https://doi.org/10.1016/j.media.2025.103905 |
| Language: | English |
| Additional information: | Under a Creative Commons license https://creativecommons.org/licenses/by-nc-nd/4.0/ |
| Keywords: | computational pathology, immunotherapy, lymphocytes, macrophages, segment anything, database |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10218982 |
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