Leveraging Weakly Labeled Datasets with Target Adaptive Loss for Cell Segmentation in Immunofluorescence Images

The instance segmentation of whole cells and of the respective sub-cellular compartments - nuclei, cytosol, and membrane is key to enable the quantification of biomarker signal(s) (e.g. HER2, PDL1, PD1) at a single cell level in digital histopathology images. Instance segmentation of the whole-cell objects is typically obtained using deep learning models trained on large-scale datasets of manual and pixel-precise annotations. Aiming for a segmentation model in the immunofluorescence (IF) domain and starting with an available manually labeled dataset in the immunohistochemistry (IHC) stain domain, we translate this dataset of whole cell instances to the target domain using known CycleGan-based stain translation methods. To further increase the size of the training data while limiting the associated annotation burden, we propose to additionally leverage - through the introduction of two target adaptative losses, two additional datasets that are weakly labeled for nucleus centers and nucleus masks respectively. The introduced losses map the five class-probability maps output of the model (nucleus center, cell center, nucleus body, cytosol, membrane) to the binary class configuration expected by the nucleus center and nucleus mask datasets. We show quantitatively on a test set of manually labeled IF FOVs that the approach yields an increased accuracy of the detected and segmented cell instances compared to a baseline model trained solely on the translated dataset of whole cell instances. The results as well indicate the ability of the approach to fill the residual domain gap between the source and target domains.