Semantic Segmentation has benefited a lot from the advancement of Deep Learning
technologies. Image Segmentation has been a boon for medical field in both application as …

Immunohistology fluorescence image analysis is an important method for cancer diagnosis.
With the widespread application of convolutional neural networks in computer vision …

Accurate segmentation is a basic and crucial step for medical image processing and
analysis. In the last few years, U-Net, and its variants, have become widely adopted models …

Accurate segmentation of cellular structures is critical for automating the analysis of
microscopy data. Advances in deep learning have facilitated extensive improvements in …

Image segmentation is consistently an important task for computer vision and the analysis of
medical images. The analysis and diagnosis of histopathology images by using efficient …

Background The digital pathology images obtain the essential information about the
patient's disease, and the automated nuclei segmentation results can help doctors make …

Medical images serve as a very important tool for medical diagnosis. Medical image
segmentation is an area of image processing that segments critical parts of a medical image …

Cell nuclei segmentation is important for several applications, such as the detection of
cancerous cells and cell cycle staging. The main challenges and difficulties, associated with …

Cancer was found to be a leading cause of human mortality in the year 2020, accounting for
one in six deaths worldwide, as per data published by the World Health Organization. Early …

Medical image segmentation is a bottleneck for physicians and radiologists in diagnosis of
diseases. Deep learning based convolutional neural networks (CNNs) is used to support …