DASGAN -- Joint Domain Adaptation and Segmentation for the Analysis of Epithelial Regions in Histopathology PD-L1 Images

Ansh Kapil; Tobias Wiestler; Simon Lanzmich; Abraham Silva; Keith Steele; Marlon Rebelatto; Guenter Schmidt; Nicolas Brieu

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arXiv · 1906.11118

DASGAN -- Joint Domain Adaptation and Segmentation for the Analysis of Epithelial Regions in Histopathology PD-L1 Images

Ansh Kapil·Tobias Wiestler·Simon Lanzmich·Abraham Silva·Keith Steele·Marlon Rebelatto·Guenter Schmidt·Nicolas Brieu

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Abstract

The analysis of the tumor environment on digital histopathology slides is becoming key for the understanding of the immune response against cancer, supporting the development of novel immuno-therapies. We introduce here a novel deep learning solution to the related problem of tumor epithelium segmentation. While most existing deep learning segmentation approaches are trained on time-consuming and costly manual annotation on single stain domain (PD-L1), we leverage here semi-automatically labeled images from a second stain domain (Cytokeratin-CK). We introduce an end-to-end trainable network that jointly segment tumor epithelium on PD-L1 while leveraging unpaired image-to-image translation between CK and PD-L1, therefore completely bypassing the need for serial sections or re-staining of slides. Extending the method to differentiate between PD-L1 positive and negative tumor epithelium regions enables the automated estimation of the PD-L1 Tumor Cell (TC) score. Quantitative experimental results demonstrate the accuracy of our approach against state-of-the-art segmentation methods.

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Ansh Kapil, Tobias Wiestler, Simon Lanzmich, Abraham Silva, Keith Steele, Marlon Rebelatto, Guenter Schmidt, Nicolas Brieu. 2019-06-26. DASGAN -- Joint Domain Adaptation and Segmentation for the Analysis of Epithelial Regions in Histopathology PD-L1 Images. https://arxiv.org/abs/1906.11118

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