https://www.selleckchem.com/products/srpin340.html The proposed method overcomes the limitations of the state-of-the-art stain-separation methods, like the requirement of pure stain color basis as a prerequisite or stain color basis learning on each image. Experimental results are presented for automatic counts using deep learning-based and hand-crafted algorithms for sections immunostained for neurons (Neu-N) or microglial cells (Iba-1) with cresyl violet counterstain. Our findings show more accurate counts by deep learning methods compared to the handcrafted method. Thus, stain-separated images can function as input for automatic deep learning-based quantification methods designed for single-stained tissue sections. Our findings show more accurate counts by deep learning methods compared to the handcrafted method. Thus, stain-separated images can function as input for automatic deep learning-based quantification methods designed for single-stained tissue sections.Spatiotemporal patterns of neural activity generate brain functions, such as perception, memory, and behavior. Four-dimensional (4-D x, y, z, t) analyses of such neural activity will facilitate understanding of brain functions. However, conventional two-photon microscope systems observe single-plane brain tissue alone at a time with cellular resolution. It faces a trade-off between the spatial resolution in the x-, y-, and z-axes and the temporal resolution by a limited point-by-point scan speed. To overcome this trade-off in 4-D imaging, we developed a holographic two-photon microscope for dual-plane imaging. A spatial light modulator (SLM) provided an additional focal plane at a different depth. Temporal multiplexing of split lasers with an optical chopper allowed fast imaging of two different focal planes. We simultaneously recorded the activities of neurons on layers 2/3 and 5 of the cerebral cortex in awake mice in vivo. The present study demonstrated the proof-of-concept of dual-plane two-photon ima