US 12,437,396 B2
Machine-learned cell counting or cell confluence for a plurality of cell types
Manuel Amthor, Jena (DE); Daniel Haase, Zöllnitz (DE); and Michael Gögler, Wolfratshausen (DE)
Assigned to Carl Zeiss Microscopy GmbH, Jena (DE)
Filed by Carl Zeiss Microscopy GmbH, Jena (DE)
Filed on Sep. 30, 2022, as Appl. No. 17/957,042.
Claims priority of application No. 102021125538.4 (DE), filed on Oct. 1, 2021.
Prior Publication US 2023/0108453 A1, Apr. 6, 2023
Int. Cl. G06T 7/00 (2017.01)
CPC G06T 7/0012 (2013.01) [G06T 2207/10061 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/30024 (2013.01)] 3 Claims
OG exemplary drawing
 
1. A computer-implemented method, comprising:
acquiring a light-microscope image, which images a multiplicity of cells of a plurality of cell types,
determining a plurality of density maps for the light-microscope image using a plurality of machine-learned processing paths of at least one machine-learned algorithm, wherein the plurality of processing paths are assigned to the plurality of cell types, wherein the plurality of density maps each encodes a probability for the presence or absence of cells of a corresponding cell type, and
on the basis of the plurality of density maps and for each of the plurality of cell types: determining at least one of an estimation of a number or of a degree of confluence of the respective cells.