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Detecting and segmenting individual cells from microscopy images is critical to various life science applications.
We propose a cell segmentation method for analyzing images of densely clustered cells.
In this paper, we present a method for the segmentation of touching cells in microscopy images.
Current in vivo microscopy allows us detailed spatiotemporal imaging (3D+t) of complete organisms and offers insights into their development on the cellular level.
We design a novel convolutional encoder-decoder framework along with VGG-16 as the pixel-level feature extraction model.
Our model achieves 97. 5% sensitivity (Sens) and 67. 8% specificity (Spec) on cervical cell image-level screening.
Cell detection and segmentation is fundamental for all downstream analysis of digital pathology images.
Liquid-based cytology (LBC) is a reliable automated technique for the screening of Papanicolaou (Pap) smear data.
In order to use the data gained with calcium imaging, it is necessary to extract individual cells and their activity from the recordings.
In this paper, we propose a novel Instance Relation Network (IRNet) for robust overlapping cell segmentation by exploring instance relation interaction.