This work presents a deep learning model and image processing based processing flow to
detect and segment nuclei from microscopy images. This work aims at isolating each nuclei …

Cancer is termed as one of the deadliest disease, and it is becoming a major health problem
in the world. This deadly disease can be cured, if it is found at earlier stages. Medical …

Nuclei segmentation of cells is the preliminary and essential step of pathological image
analysis. However, robust and accurate cell nuclei segmentation is challenging due to the …

During the last few years, medical image segmentation using deep learning has become the
most active research area in computer vision. Effectively, researchers become more and …

Image processing techniques and algorithms are extensively used for biomedical
applications. Convolution Neural Network (CNN) is gaining popularity in fields such as the …

Histology analysis is currently a gold standard in analyzing cancer. Nuclei segmentation is
vital in histopathology analysis. However, it is challenging due to limited data and extreme …

Automatic segmentation of microscopic images is an important task in medical image
processing and analysis. Nuclei detection is an important example of this task. Imagine a …

Fluorescence in situ hybridization (FISH) is a powerful cytogenetic method used to precisely
detect and localize nucleic acid sequences. This technique is proving to be an invaluable …

Mitotic cell detection in pathological images is significant for predicting the malignancy of
tumors and the intelligent segmentation of these cells. Overcoming human error generated …

Introduction Automatic nuclear segmentation in digital microscopic tissue images can aid
pathologists to extract high-quality features for nuclear morphometrics and other analyses …