Histological staining of tissue biopsies, especially hematoxylin and eosin (H&E) staining, serves as the benchmark for disease diagnosis and comprehensive clinical assessment of tissue. However, the process is laborious and time-consuming, often limiting its usage in crucial applications such as surgical margin assessment. To address these challenges, we combine an emerging 3D quantitative phase imaging technology, termed quantitative oblique back illumination microscopy (qOBM), with an unsupervised generative adversarial network pipeline to map qOBM phase images of unaltered thick tissues (i.e., label- and slide-free) to virtually stained H&E-like (vH&E) images. We demonstrate that the approach achieves high-fidelity conversions to H&E with subcellular detail using fresh tissue specimens from mouse liver, rat gliosarcoma, and human gliomas. We also show that the framework directly enables additional capabilities such as H&E-like contrast for volumetric imaging. The quality and fidelity of the vH&E images are validated using both a neural network classifier trained on real H&E images and tested on virtual H&E images, and a user study with neuropathologists. Given its simple and low-cost embodiment and ability to provide real-time feedback in vivo, this deep learning-enabled qOBM approach could enable new workflows for histopathology with the potential to significantly save time, labor, and costs in cancer screening, detection, treatment guidance, and more.

Applications of QPI with an emphasis on using machine learning approaches. Who Should Attend: Young researchers in optics and photonics who would like …

According to the American Cancer Society, cancer kills more than 8 million people each year. Early detection can boost survival rates. Researchers and clinicians are feverishly exploring avenues to provide early and accurate diagnoses, as well as more targeted treatments. Blood screenings are used to detect many types of cancers, including liver, ovarian, colon and lung cancers. Current blood screening methods typically rely on affixing biochemical labels to cells or biomolecules.

According to the American Cancer Society, cancer kills more than 8 million people each year. Early detection can boost survival rates. Researchers and clinicians are feverishly exploring avenues to provide early and accurate diagnoses, as well as more targeted treatments. Blood screenings are used to detect many types of cancers, including liver, ovarian, colon and lung cancers. Current blood screening methods typically rely on affixing biochemical labels to cells or biomolecules.

In a recent issue of Nature Scientific Reports, a multidisciplinary team of UCLA researchers combined a new form of microscopy called photonic time-stretch imaging with deep learning. With this powerful new technique, they were able to capture 36 million video frames per second. Not content with simply developing a new technique, the team then utilized the augmented microscope to determine which strains of algae provide the most lipids for subsequent refining into biofuels. They also were able to detect cancer cells more accurately and in less time than current techniques. Yes, one methodology that tackled both biofuels and cancer.