The application provides a kind of semi-automatic intelligent calibration method of
cell in
digital image.The method comprises: preparing the image dataset to be labeled containing target category
cell;Manual
ellipse marking is carried out on the target
cell in the to-be-labeled dataset;
Ellipse marking is taken as a reference, and a suitable
mask generation function is designed according to the shape prior knowledge of the target cell;
Ellipse marking is taken as a reference, and a suitable clustering method is selected to cluster the to-be-labeled image;Take the intersection of the generated
mask and the clustering result as the primary pseudo-
label of
cell labeling;Relying on the
ellipse marking, a
loss function is constructed, and the pseudo-
label is used to
train the
deep learning segmentation network, the false positive connected domain outside the
ellipse marking is removed, and the remaining result is used as the new pseudo-
label of
cell labeling;Experts correct the pseudo-label generated by
deep learning, remove the obvious false positive and supplement the labeling of the obvious missed connected domain to obtain more accurate pseudo-label;Repeat the first two steps for several times until the expert is satisfied with the label generation effect, save the intelligent labeling model and the labeling result.The scheme of the application combines the shape prior of the target cell, only needs manual ellipse marking of the cell, greatly reduces the cost of manual labeling under the premise of ensuring the accuracy of label generation, can be applied to the labeling task of various morphologies of cells under various modalities, and has the advantages of convenient and efficient, high precision, wide application range, good generalization, etc. in the field of
cell labeling.