SAU-Net

Abstract

Image-based cell counting is a fundamental yet challenging task with wide applications in biological research. In this paper, we propose a novel Deep Network designed to universally solve this problem for various cell types. Specifically, we first extend the segmentation network, U-Net with a Self-Attention module, named SAU-Net, for cell counting. Second, we design an online version of Batch Normalization to mitigate the generalization gap caused by data augmentation in small datasets. We evaluate the proposed method on four public cell counting benchmarks-synthetic fluorescence microscopy (VGG) dataset, Modified Bone Marrow (MBM) dataset, human subcutaneous adipose tissue (ADI) dataset, and Dublin Cell Counting (DCC) dataset. Our method surpasses the current state-ofthe-art performance in the three real datasets (MBM, ADI and DCC) and achieves competitive results in the synthetic dataset (VGG).

WSCNet: Biomedical Image Recognition for Cell Encapsulated Microfluidic Droplets

“…The WSCNet consists of classification and counting branches, as shown in Figure 2 f. The former serves as a filter to remove false positives from previously generated proposals. Similar to other counting tasks [ 27 , 28 ], the output of the latter branch is a single-channel density map, and its integral and local maxima may indicate the number and location of cells, respectively. Cross entropy was adopted by the classification branch as the loss function to provide a predicted label (droplet or false positive).…”

“…Research on cell counting mostly adopts regression approaches to estimate the density map of a given medical image [ 27 ], while the integral of the density map might intuitively indicate the cell number. These fully supervised learning approaches require tedious cell-level annotation for the training procedure, including a precise cell population [ 28 ] and the accurate location of each cell [ 29 ]. To avoid time-consuming annotation, three droplet-level labels (empty, single-cell, and multicell encapsulation) instead of cell-level labels are adopted in this paper.…”