Background and Objective:
A three-dimensional visualization of a human carcinoma could provide invaluable diagnostic information and redefine how we perceive and analyze cancer invasion. As deep learning begins automating the diagnostic workflow and cutting-edge microcopy provides unprecedented ways of visualizing tissue, combining these methologies could provide novel insight into malignant tumors and other pathologic entities. By combining Knife-Edge Scanning Microscopy with convolutional neural networks, we set out to visualize an entire threedimensional colorectal carcinoma segmented into specific tissue classifications.
Methods:
A Knife-Edge Scanning Microscope (KESM), developed by Strateos (San Francisco, CA, USA), was used to digitize a whole-mount, H&E stained, formalinfixed paraffin-embedded human tissue specimen obtained from the Radboudumc (Nijmegen, Netherlands). Sparse manual annotations of 5 tissue types (tumor, stroma, muscle, healthy glands, background) were provided using KESM data to train a convolutional neural network developed by the Computational Pathology Group (Radboudumc) for semantic segmentation of the colorectal carcinoma tissue. The three-dimensional visualization was generated using 3Scan's proprietary visualization pipeline.
Results: The convolutional neural network was used to process roughly 1200 slices of KESM data. The stitched and rendered segmentation maps demonstrate the formalin-fixed paraffin-embedded carcinoma of roughly 5 millimeters in depth. As shown in the figure, the tumor invasive margin can be seen advancing into the surrounding tumor stroma.
Conclusion: Based on our findings, we were capable of training a segmentation model on the 3D KESM data to create an accurate representation of a formalin-fixed paraffin-embedded colorectal carcinoma tissue block segmented into five tissue classifications. Going forward, this can have much broader implications on the research and understanding of invasive tumors.