yugantgajera/Dilated-Inception-U-Net-for-Nuclei-Segmentation-in-Multi-Organ-Histology-Images

Medical image processing using machine learning is an emerging field of study which involves making use of medical image data and drawing valuable inferences out of them. Segmentation of any body of interest from a medical image can be done automatically using machine learning algorithms. Deep learning has been proven effective in the segmentation of any entity of interest from its surroundings such as brain tumors, lesions, cysts, etc which helps doctors diagnose several diseases. In several medical image segmentation tasks, the U-Net model achieved impressive performance. In this study, a Dilated Inception U-Net model is employed to effectively generate feature sets over a broad region on the input in order to segment the compactly packed and clustered nuclei in the Molecular Nuclei Segmentation dataset that contains H&E histopathology pictures. A comprehensive review of published work based on deep learning on this dataset has also been exhibited.