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Confocal laser endomicroscopy (CLE), although capable of obtaining images atcellular resolution during surgery of brain tumors in real time, creates asmany non-diagnostic as diagnostic images. Non-useful images are often distorteddue to relative motion between probe and brain or blood artifacts. Many images,however, simply lack diagnostic features immediately informative to thephysician. Examining all the hundreds or thousands of images from a single caseto discriminate diagnostic images from nondiagnostic ones can be tedious.Providing a real-time diagnostic value assessment of images (fast enough to beused during the surgical acquisition process and accurate enough for thepathologist to rely on) to automatically detect diagnostic frames wouldstreamline the analysis of images and filter useful images for thepathologist/surgeon. We sought to automatically classify images as diagnosticor non-diagnostic. AlexNet, a deep-learning architecture, was used in a 4-foldcross validation manner. Our dataset includes 16,795 images (8572 nondiagnosticand 8223 diagnostic) from 74 CLE-aided brain tumor surgery patients. The groundtruth for all the images is provided by the pathologist. Average model accuracyon test data was 91% overall (90.79 % accuracy, 90.94 % sensitivity and 90.87 %specificity). To evaluate the model reliability we also performed receiveroperating characteristic (ROC) analysis yielding 0.958 average for the areaunder ROC curve (AUC). These results demonstrate that a deeply trained AlexNetnetwork can achieve a model that reliably and quickly recognizes diagnostic CLEimages.

proceedings of spie, the international society for optical engineering/proceedings of spie

Improving utility of brain tumor confocal laser endomicroscopy:  objective value assessment and diagnostic frame detection with convolutional  neural networks