Robust Nucleus/Cell Detection and Segmentation in Digital Pathology and Microscopy Images: A Comprehensive Review

Abstract

Digital pathology and microscopy image analysis is widely used for comprehensive studies of cell morphology or tissue structure. Manual assessment is labor intensive and prone to interobserver variations. Computer-aided methods, which can significantly improve the objectivity and reproducibility, have attracted a great deal of interest in recent literature. Among the pipeline of building a computer-aided diagnosis system, nucleus or cell detection and segmentation play a very important role to describe the molecular morphological information. In the past few decades, many efforts have been devoted to automated nucleus/cell detection and segmentation. In this review, we provide a comprehensive summary of the recent state-of-the-art nucleus/cell segmentation approaches on different types of microscopy images including bright-field, phase-contrast, differential interference contrast, fluorescence, and electron microscopies. In addition, we discuss the challenges for the current methods and the potential future work of nucleus/cell detection and segmentation.

Fig. 1

The marker detection results using [80] on two sample images from breast cancer and blood smear datasets. The green dots represent the detected seeds. The breast cancer image is acquired at 10× objective magnification. Note that seed detection is performed only on the segmented epithelial regions [80]. The blood smear image is acquired at 40× objective magnification.

Fig. 2

The marker detection results using [164] on three sample images from breast cancer, pancreatic neuroendocrine tumor (NET), and HeLa cell datasets, respectively. Original images are shown in row 1, and their corresponding detections are represented as yellow dots in row 2. The breast cancer and NET images are captured at 40× and 20× objective magnification, respectively. The HeLa cell line image is obtained from [92].

Fig. 3

The segmentation results using [208] on three sample skeletal muscle images. Original images are shown in row 1, and their corresponding segmentations are represented as blue contours in row 2. The images are captured at 10× objective magnification.

Fig. 4

The segmentation results using [82] on one sample pancreatic neuroendocrine tumor image. Red dots and contours represent results of marker detection and boundary segmentation in (b) and (c), respectively. The image is acquired at 20× objective magnification.