Ex vivo tumor imaging

Intraoperative label-free multimodal nonlinear optical imaging

The majority of the current intraoperative optical imaging techniques involve labeling which perturbs the tissue microenvironment and alters the optical signatures of various biochemical processes. In contrast, multiple nonlinear optical imaging (NLOI) modalities have been demonstrated to have the ability to visualize microstructures and provide molecular and functional information. With the development and demonstration of our lab-based simultaneous label-free autofluorescence and multi-harmonics (SLAM) microscope,  continuous efforts have been made to adapt this multimodal NLOI platform to a portable system for intraoperative cancer imaging. Previously we have demonstrated one version of our intraoperative label-free NLOI system in human breast cancer surgeries. Correlations with the histology results have shown that this intraoperative NLOI platform has the potential to provide diagnostic information at point-of-procedure without destroying the biological tissue integrity. With our recent upgrades on the imaging system, we were able to provide optical assessment of needle biopsies taken from live animal patients during veterinary surgeries. We are working towards further improvement of the intraoperative label-free NLOI system to enable real-time screening of needle biopsies and surgical specimens for cancer diagnosis at point-of-procedure. 

Label-free multimodal NLOI results (left) along with corresponding histology images (right) from human breast tissue specimens diagnosed as (A) invasive ductal carcinoma (IDC), with the red dashed arrow showing an overall orientation of collagen alignment, (B-C) ductal carcinoma in situ (DCIS), showing adipocytes (red dashed arrows), blood vessel (red solid arrow in B), and mammary duct (red solid arrow in C), and (D) healthy breast tissue from a breast reduction surgery. Scale bars represent 100 µm. Channel pseudo-colors: THG, magenta; 3PF, cyan; SHG, green; 2PF, yellow.

  • Sun, Y., You, S., Tu, H., Spillman, D. R., Chaney, E. J. Marjanovic, M. Li, J., Barkalifa, R., Wang, J., Higham, A. M., Luckey, N. N., Cradock, K. A., Liu, Z. G., & Boppart, S. A. “Intraoperative visualization of the tumor microenvironment and of extracellular vesicles by label-free nonlinear imaging,” Science Advances, vol. 4, no. 12, eaau5603, 2018.
  • Yang, L., Park, J., Marjanovic, M., Chaney, E. J., Spillman, D. R., Phillips, H. & Boppart, S. A. "Intraoperative label-free multimodal nonlinear optical imaging for point-of-procedure cancer diagnostics." IEEE JSTQE, submitted. 

Automated diagnosis of cancer using deep learning models

Histopathology has been widely adopted in tissue assessment in clinical decision making and in research. However, its complex procedure of tissue preparation and analysis prevent histopathology from intraoperative assessment and cause delays in treatment planning. Our lab developed an Artificial Intelligence (AI)-driven real-time intraoperative diagnosis system based on stain-free slide-free multimodal multiphoton microscopy. This system captures rich molecular and structural information from living or freshly excised tissue and use Deep Neural Networks (DNN) to extract meaningful features and generate diagnoses. Compared to traditional histopathology, which may take several days to process the tissue and generate analysis reports, our highly automated system only requires minimal tissue processing and generates reliable diagnosis within minutes, which makes this technique an attractive alternative or adjunct to histochemistry in clinical settings, especially in time-sensitive scenarios such as the intraoperative assessment of breast tissue during surgical oncology procedures.

Images showing classification results of human breast cancer made by our system(a) Multiphoton composite image of the four channels (THG, NADH, SHG, FAD) generated by our stain-free slide-free multimodal multiphoton microscopy. (b) Classification results corresponding to the multiphoton images in (a). The cancer probability map was coded with red color and overlaid with the original multiphoton composite image. Lipid-filled adipocytes are coded with blue color. (c) AUC statistics on test set, for different DNN architectures and different tile sizes. (d) Representative example of false positive (normal tiles classified as cancer, highlighted by the yellow oval) and false negative (cancer tiles classified as normal, highlighted by the yellow oval). Scale bar: 200 µm. 


Virtual histology

Histology imaging, invented centuries ago, serves an essential clinical imaging method to provide evidence for cancer-related diagnosis. However, the time- and labor- intense tissue preparation processes of histology causes delay in cancer diagnosis and increases the cost of cancer treatment. Our lab uses a novel imaging method, the simultaneous label-free autofluorescence multiharmonic (SLAM) microscopy, to generate histology-like images from label-free tissues, saving the time and labor of tissue processing. 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 and converted to histology-like contrastsIn particular, we realized for the first time the real-time 3D histology-like imaging. 

  • Sun, Y. et al. Real-time three-dimensional histology-like imaging by label-free nonlinear optical microscopy. Quantitative imaging in surgery and medicine. 10(11), 2177 (2020).