Population-specific segmentation

Reference keys: cell population, instance segmentation

Independent segmentation

The purpose of Celldetective is to achieve single-cell resolution through instance segmentation. In co-cultures, different cell types can be in spatial co-presence on a 2D projection, making single-cell quantifications challenging.

One way to fix this problem, particularly in effector/target systems, is to segment independently each cell population, in order to have masks as complete as possible for each population separately. Measurements can thus be performed as cleanly as possible, within the constraints of 2D images.

Another way to put this is that if you have several cell populations on your images, you should repeat the segmentation task for each population of interest, with a segmentation method as appropriate as possible for the population you want. A consequence of this is that what we call a “cell population” is a group of cells that were segmented using the same method (a population does not have to strictly relate to cell type; it can be based on different cell states in a mono culture, for example).

Strategies

Overview of segmentation options in Celldetective. Celldetective provides several entry points (black arrows) to perform segmentation, with the intent of segmenting specifically a cell population (left: effectors, right: targets). The masks output from each segmentation technique can be visualized and manually corrected in napari. Exporting these corrections into a paired image and masks dataset can be used either to fit a generalist model (transfer learning) or train one from scratch. Once the segmentation is satisfactory enough, the user can decide to proceed with the tracking and measurement modules.