Prospects for Theranostics in Neurosurgical Imaging: Empowering Confocal Laser Endomicroscopy Diagnostics via Deep Learning

Confocal laser endomicroscopy (CLE) is an advanced optical fluorescence imaging technology that has the potential to increase intraoperative precision, extend resection, and tailor surgery for malignant invasive brain tumors because of its subcellular dimension resolution. Despite its promising diagnostic potential, interpreting the gray tone fluorescence images can be difficult for untrained users. In this review, we provide a detailed description of bioinformatical analysis methodology of CLE images that begins to assist the neurosurgeon and pathologist to rapidly connect on-the-fly intraoperative imaging, pathology, and surgical observation into a conclusionary system within the concept of theranostics. We present an overview and discuss deep learning models for automatic detection of the diagnostic CLE images and discuss various training regimes and ensemble modeling effect on the power of deep learning predictive models. Two major approaches reviewed in this paper include the models that can automatically classify CLE images into diagnostic/nondiagnostic, glioma/nonglioma, tumor/injury/normal categories and models that can localize histological features on the CLE images using weakly supervised methods. We also briefly review advances in the deep learning approaches used for CLE image analysis in other organs. Significant advances in speed and precision of automated diagnostic frame selection would augment the diagnostic potential of CLE, improve operative workflow and integration into brain tumor surgery. Such technology and bioinformatics analytics lend themselves to improved precision, personalization, and theranostics in brain tumor treatment.Comment: See the final version published in Frontiers in Oncology here: https://www.frontiersin.org/articles/10.3389/fonc.2018.00240/ful

Computational Optical Sectioning in Fibre Bundle Endomicroscopy

The field of fibre bundle endomicroscopy has emerged to enable real-time imaging of cellular level features in-vivo. The gold standard is confocal laserscanning, enabling optical sectioning. Point-scan confocal suffers from lower speeds, a need for complex alignment, and the added cost of a laser. This thesis presents three developments in computational optical sectioning for fibre bundle endomicroscopy.The first development is in structured illumination (SIM) endomicroscopy. Lower-cost, simplified endomicroscopes have been developed which use widefield incoherent illumination. Optical sectioning can be introduced to these systems using SIM. SIM improves imaging using spatial modulation of the focal plane and capturing a three-frame sequence. The acquired images are then numerically processed to reject out-of-focus light. This thesis reports and characterises the first high-speed SIM endomicroscope built using a miniature array ofmirrors, a digital micromirror device. The second development is automated motion compensation in SIM endomicroscopy. As a multi frame process, SIM is susceptible to motion artefacts, making the technique difficult to use in vivo and preventing the use of mosaicking to synthesise a larger effective field of view. I report and validate an automatic motion compensation technique to overcome motion artefacts and report the firstmosaics in SIM endomicroscopy.The third development is improvements in subtraction-based enhanced line scanning (ELS) endomicroscopy. The 2D scanning of a point scan confocal endomicroscope can be replaced by a scanning line which is synchronised to the sequential readout of a rolling shutter camera. While this leads to high-speed sectioning, as with all line scanning systems, far-from-focus light degrades images. It is possible to remove this by subtracting a second image taken with an offset detection slit. This has previously required two-cameras or two sequentialframes. The latter introduces motion artefacts. This thesis presents a novel approach to ELS using single frame acquisition with real-time mosaicking at 240frames/s