Abstract
3-D histology has become an attractive technique providing insights into morphology of histologic specimens. However, existing techniques in generating 3-D views from a stack of whole slide images are scarce or suffer from poor co-registration performance when displaying diagnostically important areas at sub-cellular resolution. Our team developed a new scale-invariant feature transform (SIFT)-based workflow to co-register histology images and facilitate 3-D visualization of micro-structures important in histopathology of lung adenocarcinoma. The co-registration accuracy and visualization capacity of the workflow were tested by digitally perturbing the staining coloration seven times. The perturbation slightly affected the co-registration but overall the co-registration errors remained very small when compared to those published to date. The workflow yielded accurate visualizations of expert-selected regions permitting confident 3-D evaluation of the clusters. Our workflow could support the evaluation of histologically complex tumors such as lung adenocarcinomas that are currently routinely viewed by pathologists in 2-D on slides, but could benefit from 3-D visualization.
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Acknowledgements
This work has been supported by in part by the Precision Health Grant at C-S and seed grants from the Department of Surgery at Cedars-Sinai Medical Center. The authors would like to thank Dr. Mari Mino-Kenudson from the Massachusetts General Hospital for her help in data preparation and input on the manuscript.
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Pyciński, B., Yagi, Y., Walts, A.E., Gertych, A. (2021). 3-D Tissue Image Reconstruction from Digitized Serial Histologic Sections to Visualize Small Tumor Nests in Lung Adenocarcinomas. In: Pietka, E., Badura, P., Kawa, J., Wieclawek, W. (eds) Information Technology in Biomedicine. Advances in Intelligent Systems and Computing, vol 1186. Springer, Cham. https://doi.org/10.1007/978-3-030-49666-1_5
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