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

Shamima Nasrin; Md. Zahangir Alom; Vijayan K. Asari; Tarek M. Taha

doi:10.1117/12.2570793

20 August 2020 PColorSeg_Net: investigating the impact of different color spaces for pathological image segmentation

Shamima Nasrin, Md. Zahangir Alom, Vijayan K. Asari, Tarek M. Taha

Proceedings Volume 11511, Applications of Machine Learning 2020; 115110C (2020) https://doi.org/10.1117/12.2570793
Event: SPIE Optical Engineering + Applications, 2020, Online Only

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.

Conference Presentation

Citation Download Citation

Shamima Nasrin, Md. Zahangir Alom, Vijayan K. Asari, and Tarek M. Taha "PColorSeg_Net: investigating the impact of different color spaces for pathological image segmentation", Proc. SPIE 11511, Applications of Machine Learning 2020, 115110C (20 August 2020); https://doi.org/10.1117/12.2570793

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