  Rheumatoid Arthritis (RA) is a chronic, autoimmune disease which primarily
affects the joint's synovial tissue. It is a highly heterogeneous disease, with
wide cellular and molecular variability observed in synovial tissues. Over the
last two decades, the methods available for their study have advanced
considerably. In particular, Immunohistochemistry stains are well suited to
highlighting the functional organisation of samples. Yet, analysis of
IHC-stained synovial tissue samples is still overwhelmingly done manually and
semi-quantitatively by expert pathologists. This is because in addition to the
fragmented nature of IHC stained synovial tissue, there exist wide variations
in intensity and colour, strong clinical centre batch effect, as well as the
presence of many undesirable artefacts present in gigapixel Whole Slide Images
(WSIs), such as water droplets, pen annotation, folded tissue, blurriness, etc.
There is therefore a strong need for a robust, repeatable automated tissue
segmentation algorithm which can cope with this variability and provide support
to imaging pipelines. We train a UNET on a hand-curated, heterogeneous
real-world multi-centre clinical dataset R4RA, which contains multiple types of
IHC staining. The model obtains a DICE score of 0.865 and successfully segments
different types of IHC staining, as well as dealing with variance in colours,
intensity and common WSIs artefacts from the different clinical centres. It can
be used as the first step in an automated image analysis pipeline for synovial
tissue samples stained with IHC, increasing speed, reproducibility and
robustness.
