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Deep learning approaches for intraoperative pixel-based diagnosis of colon cancer metastasis in a liver from phase-contrast images of unstained specimens (CROSBI ID 689015)

Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija

Sitnik, Dario ; Aralica, Gorana ; Pačić, Arijana ; Popović Hadžija, Marijana ; Hadžija, Mirko ; Kopriva, Ivica Deep learning approaches for intraoperative pixel-based diagnosis of colon cancer metastasis in a liver from phase-contrast images of unstained specimens // Digital Pathology Conference - SPIE Medical Imaging Symposium 2020 / Tomaszewski, John E. ; Ward, Aaron D. (ur.). Bellingham (WA): SPIE, 2020. doi: 10.1117/12.2542799

Podaci o odgovornosti

Sitnik, Dario ; Aralica, Gorana ; Pačić, Arijana ; Popović Hadžija, Marijana ; Hadžija, Mirko ; Kopriva, Ivica

engleski

Deep learning approaches for intraoperative pixel-based diagnosis of colon cancer metastasis in a liver from phase-contrast images of unstained specimens

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.

U-net ; Structured autoencoders ; intraoperative diagnosis ; colon cancer metastasis in a liver ; phase-contrast imaging ; unstained specimens

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Podaci o prilogu

1132009

2020.

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objavljeno

10.1117/12.2542799

Podaci o matičnoj publikaciji

Digital Pathology Conference - SPIE Medical Imaging Symposium 2020

Tomaszewski, John E. ; Ward, Aaron D.

Bellingham (WA): SPIE

Podaci o skupu

SPIE Medical Imaging Symposium 2020

predavanje

15.02.2020-20.02.2020

Houston (TX), Sjedinjene Američke Države

Povezanost rada

Kliničke medicinske znanosti, Računarstvo

Poveznice