Deep learning–based medical image segmentation is henceforth widely established as a powerful segmentation process. This article proposes a new U‐Net architecture based on a convolutional neural network for cytology image segmentation. This structure is more suitable to take into account pixel neighborhood in deconvolution. The goal is to develop an accurate segmentation method for white blood cells segmentation based on cells types features. This new proposed method yields a significant improvement compared to our previous work on the cytological medical dataset. In addition, the performance of the new architecture was also successfully tested on the Digital Retinal Image for Vessel Extraction databases benchmark. The images of this challenge are similar to our cytology image segmentation. Our approach achieved 25% relative improvement of the accuracy compared to the state‐of‐the‐art.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.