Cancer is the second deadliest disease globally that can affect any human body organ. Early detection of cancer can increase the chances of survival in humans. Morphometric appearances of histopathology images make it difficult to segment nuclei effectively. We proposed a model to segment overlapped nuclei from H&E stained images. U-Net model achieved state-of-the-art performance in many medical image segmentation tasks; however, we modified the U-Net to learn a distinct set of consistent features. In this paper, we proposed the DenseRes-Unet model by integrating dense blocks in the last layers of the encoder block of U-Net, focused on relevant features from previous layers of the model. Moreover, we take advantage of residual connections with Atrous blocks instead of conventional skip connections, which helps to reduce the semantic gap between encoder and decoder paths. The distance map and binary threshold techniques intensify the nuclei interior and contour information in the images, respectively. The distance map is used to detect the center point of nuclei; moreover, it differentiates among nuclei interior boundary and core area. The distance map lacks a contour problem, which is resolved by using a binary threshold. Binary threshold helps to enhance the pixels around nuclei. Afterward, we fed images into the proposed DenseRes-Unet model, a deep, fully convolutional network to segment nuclei in the images. We have evaluated our model on four publicly available datasets for Nuclei segmentation to validate the model's performance. Our proposed model achieves 89.77% accuracy 90.36% F1-score, and 78.61% Aggregated Jaccard Index (AJI) on Multi organ Nucleus Segmentation (MoNuSeg).
Keywords: Deep learning; Dense blocks; Histopathology; Nuclei segmentation; Overlapped nuclei; Residual connections.
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