Method for Accurate Segmentation of Unstained Living Adherent Cells in Differential Interference Contrast Images

A computer-implemented method of cell instance segmentation of an original cell image obtained by microscopy, the method comprising:

• rotating the original cell image by a rotation angle to yield a rotated cell image;
• using a first machine-learning model configured for cell instance segmentation to process the original and rotated cell images such that effectively, horizontal boxes and oblique boxes are used in bounding cells of the original cell image for enhancing cell-segmentation accuracy in segmenting the original cell image, wherein the first machine-learning model generates a set of predictions for an individual cell instance in a plurality of cell instances identified from the original and rotated cell images such that a first plurality of sets of predictions is obtained for the plurality of cell instances, and wherein the set of predictions includes a bounding box for bounding the individual cell instance, and a foreground-background binary mask for predicting a space occupied by the individual cell instance;
• removing, from the first plurality of sets of predictions, any set of predictions having the predicted space not simply connected to thereby form a second plurality of sets of predictions;
• using a second machine-learning model to classify an individual set of predictions in the second plurality of sets of predictions as a wanted set of predictions or an unwanted set of predictions according to a prediction performance of the individual set of predictions;
• removing, from the second plurality of sets of predictions, any unwanted set of predictions to form a third plurality of sets of predictions;
• removing, from the third plurality of sets of predictions, any redundant set of predictions to form a fourth plurality of sets of predictions, wherein an individual redundant set of predictions as generated for a cell instance located or equivalently located at a location on the original cell image has another set of predictions generated for the same location in the third plurality of sets of predictions;
• and
• using the fourth plurality of sets of predictions to produce a segmentation map for the original cell image.