Method for accurate segmentation of unstained living adherent cells in differential interference contrast images

Abstract

In cell instance segmentation of an original cell image obtained by microscopy, the original cell image is rotated by a rotation angle to yield a rotated cell image. A first machine-learning model for cell instance segmentation processes the original and rotated cell images such that effectively, horizontal and oblique boxes are used in bounding cells of the original cell image for enhancing cell-segmentation accuracy. A set of predictions is generated for each cell instance identified from the original and rotated cell images, yielding a plurality of sets of predictions for all cell instances. The plurality of sets of predictions is processed to remove any set having predicted space not simply connected, any unwanted set classified by a second machine-learning model as a poor indicator according to a certain performance criterion, and any redundant set. The plurality of sets of predictions then produces a segmentation map for the original cell image.