KEYWORDS: Image segmentation, Education and training, Medical imaging, Performance modeling, Semantics, Image processing, Data modeling, 3D modeling, Network architectures, 3D image processing
Image segmentation as a crucial step in image processing and analysis, has important applications in video surveillance, medical detection and wafer detection, etc. Accurate and efficient image segmentation can bring great advantages and convenience to the realization of related tasks in these fields. In this paper, a 2.5D UNet network based on ConvNeXt is proposed to realize the image segmentation task based on the gastroscopy image dataset. The experimental results show that the proposed method has better segmentation performance than the UNet model based on ResNet50, UNet model based on EfficientNetB0, and UNET2.5D model based on EfficientNetB1.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.