Analysis on the Characterization of Multiphoton Microscopy Images for Malignant Neoplastic Colon Lesion Detection under Deep Learning Methods

Year: 2021

Authors: Terradillos E., Saratxaga C.L., Mattana S., Cicchi R., Pavone F.S., Andraka N., Glover B.J., Arbide N., Velasco J., Etxezarraga M.C., Picon A.

Autors Affiliation: TECNALIA, Basque Research and Technology Alliance, Derio, Spain
European Laboratory for Non‑Linear Spectroscopy, Sesto Fiorentino, Italy
National Institute of Optics, National Research Council, Sesto Fiorentino, Italy
Basque Foundation for Health Innovation and Research, Barakaldo, Spain
Department of Surgery and Cancer, Imperial College London, London, UK
Department of Pathological Anatomy, Osakidetza Basque Health Service, Basurto University Hospital, Bilbao, Spain
University of the Basque Country UPV/EHU, Bilbao, Spain

Abstract: Background: Colorectal cancer has a high incidence rate worldwide, with over 1.8 million new cases and 880,792 deaths in 2018. Fortunately,
its early detection significantly increases the survival rate, reaching a cure rate of 90% when diagnosed at a localized stage. Colonoscopy is
the gold standard technique for detection and removal of colorectal lesions with potential to evolve into cancer. When polyps are found in a
patient, the current procedure is their complete removal. However, in this process, gastroenterologists cannot assure complete resection and
clean margins which are given by the histopathology analysis of the removed tissue, which is performed at laboratory. Aims: In this paper,
we demonstrate the capabilities of multiphoton microscopy (MPM) technology to provide imaging biomarkers that can be extracted by deep
learning techniques to identify malignant neoplastic colon lesions and distinguish them from healthy, hyperplastic, or benign neoplastic tissue,
without the need for histopathological staining. Materials and Methods: To this end, we present a novel MPM public dataset containing
14,712 images obtained from 42 patients and grouped into 2 classes. A convolutional neural network is trained on this dataset and a spatially
coherent predictions scheme is applied for performance improvement. Results: We obtained a sensitivity of 0.8228 ± 0.1575 and a specificity
of 0.9114 ± 0.0814 on detecting malignant neoplastic lesions. We also validated this approach to estimate the self‑confidence of the network
on its own predictions, obtaining a mean sensitivity of 0.8697 and a mean specificity of 0.9524 with the 18.67% of the images classified
as uncertain. Conclusions: This work lays the foundations for performing in vivo optical colon biopsies by combining this novel imaging
technology together with deep learning algorithms, hence avoiding unnecessary polyp resection and allowing in situ diagnosis assessment.


Volume: 12      Pages from: 27  to: 27

KeyWords: Colorectal polyps, convolutional neural network, dataset, multiphoton microscopy, optical biopsy
DOI: 10.4103/jpi.jpi_113_20