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Deep Learning-based algorithms have shown that they are the best at segmenting, processing, detecting, and classifying medical images. U-Net is a famous Deep Learning (DL) approach for these applications. U-Net conducts four down-samplings before the concatenate process, resulting in low resolution. The dense U-Net design overcomes this problem, but the huge semantic gap between low-level and high-level down-sampling and up-sampling features remains a key concern. This work designed Sharp Dense U-Net, an improved U-Net architecture for nucleus segmentation, to solve these constraints. In the down-sampling path, dense and transition operations are used instead of max pooling and convolution to extract more informative information. In the up-sampling path, a new up-sampling layer, merging, and dense blocks reconstitute high-resolution images. Sharpening spatial filters take the place of skip connections to stop feature mismatches between the decoder and encoder paths. The proposed model is trained on the combined dataset and obtains dice coefficients, IoU, and accuracy of 0.6856, 0.5248, and 84.49, respectively. For nucleus segmentation from histopathology images, the Sharp Dense U-Net model is better than the U-Net, Dense U-Net, SCPP-Net, and LiverNet.

Nucleus segmentation is an imperative step in the qualitative study of imaging datasets, considered as an intricate task in histopathology image analysis. Segmenting a nucleus is an important part of diagnosing, staging, and grading cancer, but overlapping regions make it hard to separate and tell apart independent nuclei. Deep Learning is swiftly paving its way in the arena of nucleus segmentation, attracting quite a few researchers with its numerous published research articles indicating its efficacy in the field. This paper presents a systematic survey on nucleus segmentation using deep learning in the last five years (2017–2021), highlighting various segmentation models (U-Net, SCPP-Net, Sharp U-Net, and LiverNet) and exploring their similarities, strengths, datasets utilized, and unfolding research areas.