Label- and slide-free tissue histology using 3D epi-mode quantitative
phase imaging and virtual H&E staining
- URL: http://arxiv.org/abs/2306.00548v1
- Date: Thu, 1 Jun 2023 11:09:31 GMT
- Title: Label- and slide-free tissue histology using 3D epi-mode quantitative
phase imaging and virtual H&E staining
- Authors: Tanishq Mathew Abraham, Paloma Casteleiro Costa, Caroline Filan, Zhe
Guang, Zhaobin Zhang, Stewart Neill, Jeffrey J. Olson, Richard Levenson,
Francisco E. Robles
- Abstract summary: Histological staining of tissue biopsies serves as benchmark for disease diagnosis and comprehensive clinical assessment of tissue.
We combine emerging 3D quantitative phase imaging technology, termed quantitative oblique back illumination microscopy (qOBM), with an unsupervised generative adversarial network pipeline.
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.
- Score: 1.3141683929245986
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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 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.
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