Stromal tissue segmentation in Ki67 histology images based on cytokeratin-19 stain translation

Montse Pardàs; David Anglada-Rotger; Maria Espina; Ferran Marqués; Philippe Salembier

doi:10.1117/1.JMI.10.3.037502

23 June 2023 Stromal tissue segmentation in Ki67 histology images based on cytokeratin-19 stain translation

Montse Pardàs, David Anglada-Rotger, Maria Espina, Ferran Marqués, Philippe Salembier

Author Affiliations +

Journal of Medical Imaging, Vol. 10, Issue 3, 037502 (June 2023). https://doi.org/10.1117/1.JMI.10.3.037502

Abstract

Purpose

The diagnosis and prognosis of breast cancer relies on histopathology image analysis. In this context, proliferation markers, especially Ki67, are increasingly important. The diagnosis using these markers is based on the quantification of proliferation, which implies the counting of Ki67 positive and negative tumoral cells in epithelial regions, thus excluding stromal cells. However, stromal cells are often very difficult to distinguish from negative tumoral cells in Ki67 images and often lead to errors when automatic analysis is used.

Approach

We study the use of automatic semantic segmentation based on convolutional neural networks (CNNs) to separate stromal and epithelial areas on Ki67 stained images. CNNs need to be accurately trained with extensive databases with associated ground truth. As such databases are not publicly available, we propose a method to produce them with minimal manual labelling effort. Inspired by the procedure used by pathologists, we have produced the database relying on knowledge transfer from cytokeratin-19 images to Ki67 using an image-to-image (I2I) translation network.

Results

The automatically produced stroma masks are manually corrected and used to train a CNN that predicts very accurate stroma masks for unseen Ki67 images. An F-score value of 0.87 is achieved. Examples of effect on the KI67 score show the importance of the stroma segmentation.

Conclusions

An I2I translation method has proved very useful for building ground-truth labeling in a task where manual labeling is unfeasible. With reduced correction effort, a dataset can be built to train neural networks for the difficult problem of separating epithelial regions from stroma in stained images where separation is very hard without additional information.

Citation Download Citation

Montse Pardàs, David Anglada-Rotger, Maria Espina, Ferran Marqués, and Philippe Salembier "Stromal tissue segmentation in Ki67 histology images based on cytokeratin-19 stain translation," Journal of Medical Imaging 10(3), 037502 (23 June 2023). https://doi.org/10.1117/1.JMI.10.3.037502

Received: 26 November 2022; Accepted: 5 June 2023; Published: 23 June 2023

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KEYWORDS

Image segmentation

Education and training

Tumors

Semantics

Tissues

Databases

Gallium nitride

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