1,696
Views
9
CrossRef citations to date
0
Altmetric
Author's Views

An automated tissue-to-diagnosis pipeline using intraoperative stimulated Raman histology and deep learning

ORCID Icon & ORCID Icon
Article: 1736742 | Received 16 Feb 2020, Accepted 25 Feb 2020, Published online: 01 Apr 2020

References

  • Gal AA, Cagle PT. The 100-year anniversary of the description of the frozen section procedure. JAMA. 2005;294:1–3. doi:10.1001/jama.294.24.3135.
  • Orringer DA, Pandian B, Niknafs YS, Hollon TC, Boyle J, Lewis S, Garrard M, Hervey-Jumper SL, Garton HJL, Maher CO, et al. Rapid intraoperative histology of unprocessed surgical specimens via fibre-laser-based stimulated Raman scattering microscopy. Nat Biomed Eng. 2017;1. doi:10.1038/s41551-016-0027.
  • Robboy SJ, Weintraub S, Horvath AE, Jensen BW, Alexander CB, Fody EP, Crawford JM, Clark JR, Cantor-Weinberg J, Joshi MG, et al. Pathologist workforce in the United States: I. Development of a predictive model to examine factors influencing supply. Arch Pathol Lab Med. 2013;137:1723–1732. doi:10.5858/arpa.2013-0200-OA.
  • Hollon TC, Pandian B, Adapa AR, Urias E, Save AV, Khalsa SSS, Eichberg DG, D’Amico RS, Farooq ZU, Lewis S, et al. Near real-time intraoperative brain tumor diagnosis using stimulated Raman histology and deep neural networks. Nat Med. 2020;26:52–58. doi:10.1038/s41591-019-0715-9.
  • Freudiger CW, Min W, Saar BG, Lu S, Holtom GR, He C, Tsai JC, Kang JX, Xie XS. Label-free biomedical imaging with high sensitivity by stimulated Raman scattering microscopy. Science. 2008;322:1857–1861. doi:10.1126/science.1165758.
  • Hollon TC, Lewis S, Pandian B, Niknafs YS, Garrard MR, Garton H, Maher CO, McFadden K, Snuderl M, Lieberman AP, et al. Rapid intraoperative diagnosis of pediatric brain tumors using stimulated raman histology. Cancer Res. 2018;78:278–289. doi:10.1158/0008-5472.CAN-17-1974.
  • Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. In: Pereira F, Burges CJC, Bottou L, Weinberger KQ, editors. Advances in neural information processing systems 25. Red Hook (NY): Curran Associates, Inc.; 2012. p. 1097–1105.
  • Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, Venugopalan S, Widner K, Madams T, Cuadros J, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA. 2016;316:2402–2410. doi:10.1001/jama.2016.17216.
  • Titano JJ, Badgeley M, Schefflein J, Pain M, Su A, Cai M, Swinburne N, Zech J, Kim J, Bederson J, et al. Automated deep-neural-network surveillance of cranial images for acute neurologic events. Nat Med. 2018;24:1337–1341. doi:10.1038/s41591-018-0147-y.
  • Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, Thrun S. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542:115–118. doi:10.1038/nature21056.