Patch-based Carcinoma Detection on Confocal Laser Endomicroscopy Images - A Cross-site Robustness Assessment

Abstract

Deep learning technologies such as convolutional neural networks (CNN) provide powerful methods for image recognition and have recently been employed in the field of automated carcinoma detection in confocal laser endomicroscopy (CLE) images. CLE is a (sub-)surface microscopic imaging technique that reaches magnifications of up to 1000x and is thus suitable for in vivo structural tissue analysis. In this work, we aim to evaluate the prospects of a priorly developed deep learning-based algorithm targeted at the identification of oral squamous cell carcinoma with regard to its generalization to further anatomic locations of squamous cell carcinomas in the area of head and neck. We applied the algorithm on images acquired from the vocal fold area of five patients with histologically verified squamous cell carcinoma and presumably healthy control images of the clinically normal contra-lateral vocal cord. We find that the network trained on the oral cavity data reaches an accuracy of 89.45% and an area-underthe-curve (AUC) value of 0.955, when applied on the vocal cords data. Compared to the state of the art, we achieve very similar results, yet with an algorithm that was trained on a completely disjunct data set. Concatenating both data sets yielded further improvements in cross-validation with an accuracy of 90.81% and AUC of 0.970. In this study, for the first time to our knowledge, a deep learning mechanism for the identification of oral carcinomas using CLE Images could be applied to other disciplines in the area of head and neck. This study shows the prospect of the algorithmic approach to generalize well on other malignant entities of the head and neck, regardless of the anatomical location and furthermore in an examiner-independent manner.

Probe-based confocal laser endomicroscopy in detecting malignant lesions of vocal folds

“…The application of deep learning algorithms to CLE images, as described by Aubreville et al based on Convolutional Neural Networks, was able to correctly recognise CLE images of oral SCC with an accuracy of 88.3%, a sensitivity of 86.6% and a specificity of 90% 15 . To confirm the robustness of this model the group applied the algorithm to our dataset of the vocal cords and obtained an accuracy of 90.7% 33 . The quality of the video sequences can be also diminished due to motion artefacts that are usually caused by slight movements of the probe during examination.…”

In-vivo Barrett’s esophagus digital pathology stage classification through feature enhancement of confocal laser endomicroscopy

“…22 CLE can give a magnified microscopy image (up to 1000×) for the subsurface of tissue analysis. 23 It is adequate for real-time examination for diagnosis to reduce the number of samples performed through traditional biopsies. 24 After injecting an intravenous contrast agent (typically fluorescein) to stain the intercellular gap to outline cell borders, a blue laser is used to scan the mucosal surface focusing at a determined depth.…”

Automatic multi‐organ segmentation in dual‐energy CT (DECT) with dedicated 3D fully convolutional DECT networks

“…Automatic multi-organ segmentation on DECT images is a challenging task due to the inter-subject variance of human abdomen, the complex 3-D intra-subject variance among organs, soft anatomy deformation, as well as different HU values for the same organ by different spectra. Recent researches show the power of deep learning in medical image processing [7]. To solve the DECT segmentation problem, we use the successful experience from multi-organ segmentation in volumetric SECT images using deep learning [8,9].…”