Andrew H Song, Mane Williams, Drew F K Williamson, Sarah S L Chow, Guillaume Jaume, Gan Gao, Andrew Zhang, Bowen Chen, Alexander S Baras, Robert Serafin, Richard Colling, Michelle R Downes, Xavier Farré, Peter Humphrey, Clare Verrill, Lawrence D True, Anil V Parwani, Jonathan T C Liu, Faisal Mahmood
Human tissue, which is inherently three-dimensional (3D), is traditionally examined through standard-of-care histopathology as limited two-dimensional (2D) cross-sections that can insufficiently represent the tissue due to sampling bias. To holistically characterize histomorphology, 3D imaging modalities have been developed, but clinical translation is hampered by complex manual evaluation and lack of computational platforms to distill clinical insights from large, high-resolution datasets. We present TriPath, a deep-learning platform for processing tissue volumes and efficiently predicting clinical outcomes based on 3D morphological features...
May 9, 2024: Cell