PColorSeg_Net: Investigating the impact of different color spaces for pathological image segmentation

Abstract

Deep Learning (DL) shows the state-of-art performance in detection, classification and segmentation tasks compared to existing methods. As computational pathology becomes a very promising area of research, it is important to determine which color space provides better results for pathological image segmentation tasks. In this paper, we have considered six different color spaces, namely RGB, LAB, CIE, YCrCb, HSV and HSL for nuclei segmentation tasks where the Recurrent Residual based U-Net (R2U-Net) model is applied. The ISMI-2017 publicly available dataset is used for evaluating the model in this implementation. The Lab color space shows an F1-score of 0.9365, which is the highest segmentation performance when compared to the other color spaces. The Lab color space model shows around 0.38% better performance compared to the RGB color space for nuclei segmentation tasks. This investigation will provide a clear guidance in advance of pathological image segmentation and analysis tasks.