8 research outputs found
Automated Whole Slide Imaging for Label-Free Histology using Photon Absorption Remote Sensing Microscopy
The field of histology relies heavily on antiquated tissue processing and
staining techniques that limit the efficiency of pathologic diagnoses of cancer
and other diseases. Current staining and advanced labeling methods are often
destructive and mutually incompatible, requiring new tissue sections for each
stain. This prolongs the diagnostic process and depletes valuable biopsy
samples. In this study, we present an alternative label-free histology platform
using the first transmission-mode Photon Absorption Remote Sensing microscope.
Optimized for automated whole slide scanning of unstained tissue samples, the
system provides slide images at magnifications up to 40x that are fully
compatible with existing digital pathology tools. The scans capture high
quality and high-resolution images with subcellular diagnostic detail. After
imaging, samples remain suitable for histochemical, immunohistochemical, and
other staining techniques. Scattering and absorption (radiative and
non-radiative) contrasts are shown in whole slide images of malignant human
breast and skin tissues samples. Clinically relevant features are highlighted,
and close correspondence and analogous contrast is demonstrated with one-to-one
gold standard H&E stained images. Our previously reported pix2pix virtual
staining model is applied to an entire whole slide image, showcasing the
potential of this approach in whole slide label-free H&E emulation. This work
is a critical advance for integrating label-free optical methods into standard
histopathology workflows, both enhancing diagnostic efficiency, and broadening
the number of stains that can be applied while preserving valuable tissue
samples.Comment: 10 pages, 10 figure
Multi-channel feature extraction for virtual histological staining of photon absorption remote sensing images
Abstract Accurate and fast histological staining is crucial in histopathology, impacting diagnostic precision and reliability. Traditional staining methods are time-consuming and subjective, causing delays in diagnosis. Digital pathology plays a vital role in advancing and optimizing histology processes to improve efficiency and reduce turnaround times. This study introduces a novel deep learning-based framework for virtual histological staining using photon absorption remote sensing (PARS) images. By extracting features from PARS time-resolved signals using a variant of the K-means method, valuable multi-modal information is captured. The proposed multi-channel cycleGAN model expands on the traditional cycleGAN framework, allowing the inclusion of additional features. Experimental results reveal that specific combinations of features outperform the conventional channels by improving the labeling of tissue structures prior to model training. Applied to human skin and mouse brain tissue, the results underscore the significance of choosing the optimal combination of features, as it reveals a substantial visual and quantitative concurrence between the virtually stained and the gold standard chemically stained hematoxylin and eosin images, surpassing the performance of other feature combinations. Accurate virtual staining is valuable for reliable diagnostic information, aiding pathologists in disease classification, grading, and treatment planning. This study aims to advance label-free histological imaging and opens doors for intraoperative microscopy applications
Photon Absorption Remote Sensing Imaging of Breast Needle Core Biopsies Is Diagnostically Equivalent to Gold Standard H&E Histologic Assessment
Photon absorption remote sensing (PARS) is a new laser-based microscope technique that permits cellular-level resolution of unstained fresh, frozen, and fixed tissues. Our objective was to determine whether PARS could provide an image quality sufficient for the diagnostic assessment of breast cancer needle core biopsies (NCB). We PARS imaged and virtually H&E stained seven independent unstained formalin-fixed paraffin-embedded breast NCB sections. These identical tissue sections were subsequently stained with standard H&E and digitally scanned. Both the 40× PARS and H&E whole-slide images were assessed by seven breast cancer pathologists, masked to the origin of the images. A concordance analysis was performed to quantify the diagnostic performances of standard H&E and PARS virtual H&E. The PARS images were deemed to be of diagnostic quality, and pathologists were unable to distinguish the image origin, above that expected by chance. The diagnostic concordance on cancer vs. benign was high between PARS and conventional H&E (98% agreement) and there was complete agreement for within-PARS images. Similarly, agreement was substantial (kappa > 0.6) for specific cancer subtypes. PARS virtual H&E inter-rater reliability was broadly consistent with the published literature on diagnostic performance of conventional histology NCBs across all tested histologic features. PARS was able to image unstained tissues slides that were diagnostically equivalent to conventional H&E. Due to its ability to non-destructively image fixed and fresh tissues, and the suitability of the PARS output for artificial intelligence assistance in diagnosis, this technology has the potential to improve the speed and accuracy of breast cancer diagnosis