Deeply-supervised density regression for automatic cell counting in microscopy images

• Published in: Medical image analysis Vol. 68; p. 101892
• Main Authors: He, Shenghua, Minn, Kyaw Thu, Solnica-Krezel, Lilianna, Anastasio, Mark A., Li, Hua
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.02.2021; Elsevier BV
• Subjects: Artificial neural networks; Automatic cell counting; Cell density; Counting methods; Datasets; Deeply-supervised learning; Density; Fully convolutional neural network; Image contrast; Image Processing, Computer-Assisted; Medical imaging; Microscopy; Microscopy images; Neural networks; Neural Networks, Computer; Regression models; Deeply-supervised learning; Automatic cell counting; Microscopy images; Fully convolutional neural network
• ISSN: 1361-8415; 1361-8423
• DOI: 10.1016/j.media.2020.101892

•Accurately counting the number of cells in microscopy images is desired.•Proposed a new density regression-based method for automatically counting cells.•Designed a fully convolutional regression network with concatenated layers (C-FCRN).•Concatenated layers allow multi-scale image features for cell density estimation.•Auxiliary CNNs assist in the training of intermediate layers of C-FCRN. [Display omitted] Accurately counting the number of cells in microscopy images is required in many medical diagnosis and biological studies. This task is tedious, time-consuming, and prone to subjective errors. However, designing automatic counting methods remains challenging due to low image contrast, complex background, large variance in cell shapes and counts, and significant cell occlusions in two-dimensional microscopy images. In this study, we proposed a new density regression-based method for automatically counting cells in microscopy images. The proposed method processes two innovations compared to other state-of-the-art density regression-based methods. First, the density regression model (DRM) is designed as a concatenated fully convolutional regression network (C-FCRN) to employ multi-scale image features for the estimation of cell density maps from given images. Second, auxiliary convolutional neural networks (AuxCNNs) are employed to assist in the training of intermediate layers of the designed C-FCRN to improve the DRM performance on unseen datasets. Experimental studies evaluated on four datasets demonstrate the superior performance of the proposed method.