A simultaneous label-free autofluorescence multiharmonic (SLAM) microscope is used to conduct ex vivo and in vivo imaging of fresh human and rat tissues.  Four nonlinear optical imaging modalities are integrated into this SLAM microscope, including second harmonic generation (SHG), two-photon fluorescence (2PF), third harmonic generation (THG), and three-photon fluorescence (3PF).  By imaging fresh human and rat tissues without any tissue processing or staining, various biological tissue features are effectively visualized by one or multiple imaging modalities of the SLAM microscope.  In particular, some of the most essential features in hematoxylin and eosin (H&E)-stained histology, such as collagen fibers and nuclei, are also present in the SLAM microscopy images with good contrast.  Because nuclei are evident from negative contrast, the nuclei are segmented from the SLAM images using deep learning.  Finally, a color-transforming algorithm is developed to convert the grey-scale images acquired by the SLAM microscope to the virtually H&E-stained histology-like images.  In addition, the nuclear-to-cytoplasmic ratios (N/C ratios) of the cells in the SLAM image are quantified, which has diagnostic relevance for cancer.


 Various histological correlations are identified with high similarities for the color-converted histology-like SLAM microscopy images.  By applying the color transforming algorithm on real-time SLAM image sequences and 3D SLAM image stacks, we report, for the first time and to the best our knowledge, real-time 3D histology-like imaging.  Furthermore, the quantified N/C ratio of the cells in the SLAM image are overlaid on the converted histology-like image as a new image contrast.


 We demonstrated real-time 3D histology-like imaging and its future potential using SLAM microscopy aided by color remapping and deep-learning-based feature segmentation.
SLAM
Four nonlinear optical imaging modalities are integrated into this SLAM microscope, including second harmonic generation (SHG), two-photon fluorescence (2PF), third harmonic generation (THG), and three-photon fluorescence (3PF).  By imaging fresh human and rat tissues without any tissue processing or staining, various biological tissue features are effectively visualized by one or multiple imaging modalities of the SLAM microscope.  Finally, a color-transforming algorithm is developed to convert the grey-scale images acquired by the SLAM microscope to the virtually H&E-stained histology-like images. 
SLAM microscope
In particular, some of the most essential features in hematoxylin and eosin (H&E)-stained histology, such as collagen fibers and nuclei, are also present in the SLAM microscopy images with good contrast.  Various histological correlations are identified with high similarities for the color-converted histology-like SLAM microscopy images.  We demonstrated real-time 3D histology-like imaging and its future potential using SLAM microscopy aided by color remapping and deep-learning-based feature segmentation.
SLAM microscopy
Because nuclei are evident from negative contrast, the nuclei are segmented from the SLAM images using deep learning.  In addition, the nuclear-to-cytoplasmic ratios (N/C ratios) of the cells in the SLAM image are quantified, which has diagnostic relevance for cancer.


 By applying the color transforming algorithm on real-time SLAM image sequences and 3D SLAM image stacks, we report, for the first time and to the best our knowledge, real-time 3D histology-like imaging.  Furthermore, the quantified N/C ratio of the cells in the SLAM image are overlaid on the converted histology-like image as a new image contrast.



SLAM image