Automated Detection of Uninformative Frames in Pulmonary Optical Endomicroscopy.

SIGNIFICANCE: Optical endomicroscopy (OEM) is a novel real-time imaging technology that provides endoscopic images at a microscopic level. The nature of OEM data, as acquired in clinical use, gives rise to the presence of uninformative frames (i.e., pure-noise and motion-artefacts). Uninformative frames can comprise a considerable proportion (up to > 25%) of a dataset, increasing the resources required for analyzing the data (both manually and automatically), as well as diluting the results of any automated quantification analysis. OBJECTIVE: There is, therefore, a need to automatically detect and remove as many of these uninformative frames as possible while keeping frames with structural information intact. METHODS: This paper employs Gray Level Cooccurrence Matrix texture measures and detection theory to identify and remove such frames. The detection of pure-noise and motion-artefacts frames is treated as two independent problems. RESULTS: Pulmonary OEM frame sequences of the distal lung are employed for the development and assessment of the approach. The proposed approach identifies and removes uninformative frames with a sensitivity of 93% and a specificity of 92.6%. CONCLUSION: The detection algorithm is accurate and robust in pulmonary OEM frame sequences. Conditional to appropriate model refinement, the algorithms can become applicable in other organs.

Assessing the utility of autofluorescence-based pulmonary optical endomicroscopy to predict the malignant potential of solitary pulmonary nodules in humans.

Solitary pulmonary nodules are common, often incidental findings on chest CT scans. The investigation of pulmonary nodules is time-consuming and often leads to protracted follow-up with ongoing radiological surveillance, however, clinical calculators that assess the risk of the nodule being malignant exist to help in the stratification of patients. Furthermore recent advances in interventional pulmonology include the ability to both navigate to nodules and also to perform autofluorescence endomicroscopy. In this study we assessed the efficacy of incorporating additional information from label-free fibre-based optical endomicrosopy of the nodule on assessing risk of malignancy. Using image analysis and machine learning approaches, we find that this information does not yield any gain in predictive performance in a cohort of patients. Further advances with pulmonary endomicroscopy will require the addition of molecular tracers to improve information from this procedure.