Multi-headed U-Net: an automated nuclei segmentation technique using Tikhonov filter-based unsharp masking

An automated nuclei segmentation is the key technique for understanding and analyzing cellular properties, which are helpful for disease diagnosis and support computer-aided digital pathology. However, this task is challenging because of the variability in nuclei size and morphology, which results in blurry boundaries and overlapping nuclei. To address such issues, a multi-headed U-Net convolutional neural network (CNN) architecture has been proposed. This architecture has multiple heads to extract multi-resolution features of the source image by using different kernel sizes of the filters. The source images are pre-processed using an unsharp masking approach based on the Tikhonov filter. The Tikhonov filter decomposes the input image into low-frequency and high-frequency band images. The unsharp masking method improves the high-frequency information of the input image by primarily enhancing features such as boundaries, contours, and fine details. We have incorporated intersection over union (IOU) and F1Score as measures along with accuracy for our proposed objective functions. The proposed objective functions are tried to be maximized by the optimization algorithm, and the higher value of the metrics indicates better segmentation performance in the spatial domain during the testing phase. The proposed method attained IOU(JI), Accuracy, Precision, and F1Score values as 0.8299, 0.9642, 0.8918, and 0.9070, respectively. The quantitative and qualitative experimental outcomes indicate that our proposed technique outperforms the state-of-the-art techniques.