Label- and slide-free tissue histology using 3D epi-mode quantitative phase imaging and virtual hematoxylin and eosin staining

Abstract

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 typical formalin-fixation, paraffin-embedding (FFPE) process is laborious and time consuming, often limiting its usage in time-sensitive 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.

Deep learning-assisted low-cost autofluorescence microscopy for rapid slide-free imaging with virtual histological staining

“…Nonetheless, this imaging technique should also be applicable to fresh tissue as supported by the fresh tissue images reported in [ 10 ]. There are also other promising widefield imaging methods, such as DRUM and quantitative oblique back-illumination microscopy (qOBM) [ 31 ], for virtual histology demonstrated on fresh tissue. As detailed in the introduction, imaging contrast is one of the differences between DRUM and our approach based on CHAMP.…”

Epi-mode 3D quantitative phase imaging with quantitative oblique back illumination microcopy (qOBM): recent advances in functional and dynamic contrast

“…To overcome the limitation of QPI and ODT, we have recently introduced quantitative oblique back illumination microscopy (qOBM), which achieves epiillumination quantitative phase imaging and 3D RI tomography in thick samples, including intact thick tissues [2][3][4][5][6] . We have recently shown its potential utility for identifying mononuclear cells in blood collection bags 2,3 , quantifying vessel RI differences in mice with sickle cell disease 7 , identifying brain tumor margins in an animal model 6 and human tissues with added virtual hematoxylin and eosin stains 8 , and have shown its potential use for microendoscopy 9 , among other applications.…”

Neural network assisted fast refractive index tomography in thick tissues with quantitative oblique back-illumination microscopy

“…To overcome this limitation, we recently developed quantitative oblique back-illumination microscopy (qOBM), 56 which enables QPI and 3D RI tomography in epi-mode, and can be utilized to study arbitrarily thick samples such as fixed or fresh brain ?, [7][8][9] or blood in the collection bag. 10 qOBM uses partially coherent light source (LEDs), allows the retrieval of phase information from thick samples while having a higher resolution and lower noise.…”