Differential cell counts using center-point networks achieves human-level accuracy and efficiency over segmentation

Differential cell counts is a challenging task when applying computer vision algorithms to pathology. Existing approaches to train cell recognition require high availability of multi-class segmentation and/or bounding box annotations and suffer in performance when objects are tightly clustered.

Citation:
Lee SMW, Shaw A, Simpson JL, Uminsky D, Garratt LW. Differential cell counts using center-point networks achieves human-level accuracy and efficiency over segmentation. Sci Rep. 2021;11(1).

Keywords:
Algorithm; article; cell count; computer vision; human; molecular recognition; whole cell

Abstract:
Differential cell counts is a challenging task when applying computer vision algorithms to pathology. Existing approaches to train cell recognition require high availability of multi-class segmentation and/or bounding box annotations and suffer in performance when objects are tightly clustered.