Quantitative comparison of 3D third harmonic generation and fluorescence microscopy images

Abstract

Third harmonic generation (THG) microscopy is a label-free imaging technique that shows great potential for rapid pathology of brain tissue during brain tumor surgery. However, the interpretation of THG brain images should be quantitatively linked to images of more standard imaging techniques, which so far has been done qualitatively only. We establish here such a quantitative link between THG images of mouse brain tissue and all-nuclei-highlighted fluorescence images, acquired simultaneously from the same tissue area. For quantitative comparison of a substantial pair of images, we present here a segmentation workflow that is applicable for both THG and fluorescence images, with a precision of 91.3 % and 95.8 % achieved respectively. We find that the correspondence between the main features of the two imaging modalities amounts to 88.9 %, providing quantitative evidence of the interpretation of dark holes as brain cells. Moreover, 80 % bright objects in THG images overlap with nuclei highlighted in the fluorescence images, and they are 2 times smaller than the dark holes, showing that cells of different morphologies can be recognized in THG images. We expect that the described quantitative comparison is applicable to other types of brain tissue and with more specific staining experiments for cell type identification.

Second and third harmonic generation microscopy visualizes key structural components in fresh unprocessed healthy human breast tissue

“…Our next step is to apply such a machine in an operating room, in close collaboration with the surgeons and pathologists. Decision making will be greatly aided by automated image analysis , initially to be offset against the diagnosis made by pathologists. Furthermore, in our and other labs, micro‐endoscopes are in development to enable in‐situ multi‐photon imaging, including SHG/THG, and aid a surgeon in determining which tissue to excise.…”

Tensor regularized total variation for denoising of third harmonic generation images of brain tumors

“…Let u denote an m‐ dimensional ( m = 2 or 3) image, and f be the noisy image. An ADF model has originally been defined by the partial differential equation (PDE) as follows: together with an application‐driven diffusion tensor D , where the raw image f is used as the initial condition. D is computed from the gradient of a Gaussian smoothed version of the image ∇u σ in 3 consecutive steps.…”

“…Let u denote an m-dimensional (m = 2 or 3) image, and f be the noisy image. An ADF model [16][17][18][19][20][21][22][23][24]51] has originally been defined by the partial differential equation (PDE) as follows:…”

“…The structure tensor is responsible for capturing the distribution of local gradients, thus enabling ADF to reconstruct certain kinds of structures, such as one‐dimensional (1D) flow‐like and two‐dimensional (2D) membrane‐like structures , as well as 2D blob and ridges . In a previous study , we have applied the classical edge‐enhancing ADF model to restore the “dark” brain cells observed in THG images of mouse brain tissue. We have further developed in a salient edge‐enhancing ADF model to reconstruct the rich morphologies appearing in THG images of structurally normal human brain tissue.…”

Compact portable multiphoton microscopy reveals histopathological hallmarks of unprocessed lung tumor tissue in real time

“…Malignant and normal human brain tissue were successfully discriminated, based on histopathological hallmarks such as increased cellularity and nuclear pleomorphism. Furthermore, our group has quantitatively detected glioma infiltration by applying automated image analysis [45,[47][48][49].…”