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16 March 2020 Deep learning approaches for intraoperative pixel-based diagnosis of colon cancer metastasis in a liver from phase-contrast images of unstained specimens
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Abstract
There is a need for computer-aided diagnosis (CAD) systems to relieve the workload on pathologists. This seems to be especially important for intraoperative diagnosis during surgery, for which diagnostic time is very limited. This paper presents preliminary results of intraoperative pixel-based CAD of colon cancer metastasis in a liver from phase-contrast images of unstained frozen sections. In particular, two deep learning networks: the U-net and the structured autoencoder for deep subspace clustering, were trained on eighteen phase-contrast images belonging to five patients and tested on eight images belonging to three patients. Spectrum angle mapper was also used in comparative performance analysis. The best result achieved by the U-net yielded balanced accuracy of 83.70%±8%, sensitivity of 94.50%±8%, specificity of 72.9%±8% and Dice coefficient of 45.20%±25.4%. However, factors such as absence of tissue fixation and ethanol-induced dehydration, melting of the specimen under the microscope and/or frozen crystals in the specimen cause variations in quality of phase-contrast images of unstained frozen sections. This, in return, affects reproducibility of diagnostic performance.
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© (2020) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Dario Sitnik, Gorana Aralica, Arijana Pačić, Marijana Popović Hadžija, Mirko Hadžija, and Ivica Kopriva "Deep learning approaches for intraoperative pixel-based diagnosis of colon cancer metastasis in a liver from phase-contrast images of unstained specimens", Proc. SPIE 11320, Medical Imaging 2020: Digital Pathology, 1132009 (16 March 2020); https://doi.org/10.1117/12.2542799
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