Nuclei Spotting for Computational Pathology in Microscopic Images
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Nuclei spotting has been given a paramount importance in diagnosing and monitoring many medical conditions. It also helps the pharmacists to develop and discover new formulas of drugs/remedy by observing the effects of medicines on the patients. Nuclei spotting becomes a challenging task due to the natural variation in the appearance as well as the variation of image capturing devices. Besides, variation in the lightening conditions also pose extra challenges in the process of detection and segmentation of nuclei. In the current study, we employed a modified U-Net (mU-Net), a deep learning-based approach, for nuclei detection and segmentation. The results showed the supremacy of the proposed method. Intersection over Union (IOU) of 0.78 was achieved on BBBC038v1 dataset.
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