SAU-Net: A Unified Network for Cell Counting in 2D and 3D Microscopy Images.

ABSTRACT: Image-based cell counting is a fundamental yet challenging task with wide applications in biological research. In this paper, we propose a novel unified deep network framework designed to solve this problem for various cell types in both 2D and 3D images. Specifically, we first propose SAU-Net for cell counting by extending the segmentation network U-Net with a Self-Attention module. Second, we design an extension of Batch Normalization (BN) to facilitate the training process for small datasets. In addition, a new 3D benchmark dataset based on the existing mouse blastocyst (MBC) dataset is developed and released to the community. Our SAU-Net achieves state-of-the-art results on four benchmark 2D datasets - synthetic fluorescence microscopy (VGG) dataset, Modified Bone Marrow (MBM) dataset,

SUBMITTER: Guo Y 

PROVIDER: S-EPMC8924707 | biostudies-literature | 2022 Jul-Aug

REPOSITORIES: biostudies-literature