Nuclei Segmentation Approach for Digestive Neuroendocrine Tumors Analysis Using optimized Color Space Conversion

Abstract

Microscopic examination plays a significant role in the decision making for a reliable diagnosis of digestive neuroendocrine tumors (NETs), an immunohistochemical (IHC) analysis should be conducted by pathologists in order to identify cell morphology, tissue structure, and various histological disorders. The visual and manual assessment task, performed by experts, is tedious, time-consuming, and prone to inter-observer variability. Hence, there is an urgent need for developing an automatic nuclei segmentation approach which can provide an accurate number of cancerous histological tissues and overcome the issue of overlapping cells. In the proposed study, a morphological method for microscopic image segmentation is presented, this approach is mainly based on the choice of the appropriate color space, which highlights stained cells nuclei caused by stain variability and insufficient lighting conditions. Stromal cells, that differ from tumor cells in their particular form and small size, should be removed using shape criterion. Then marker-controlled watershed technique is applied in order to reduce the over-segmentation and to detach the connected cells in the resulting images. The proposed method is compared to ground truth segmentation, the results gave a Dice score of 0.959.