Recovering Dense Tissue Multispectral Signal from in vivo RGB Images

Hyperspectral/multispectral imaging (HSI/MSI) contains rich information clinical applications, such as 1) narrow band imaging for vascular visualisation; 2) oxygen saturation for intraoperative perfusion monitoring and clinical decision making [1]; 3) tissue classification and identification of pathology [2]. The current systems which provide pixel-level HSI/MSI signal can be generally divided into two types: spatial scanning and spectral scanning. However, the trade-off between spatial/spectral resolution, the acquisition time, and the hardware complexity hampers implementation in real-world applications, especially intra-operatively. Acquiring high resolution images in real-time is important for HSI/MSI in intra-operative imaging, to alleviate the side effect caused by breathing, heartbeat, and other sources of motion. Therefore, we developed an algorithm to recover a pixel-level MSI stack using only the captured snapshot RGB images from a normal camera. We refer to this technique as "super-spectral-resolution". The proposed method enables recovery of pixel-level-dense MSI signals with 24 spectral bands at ~11 frames per second (FPS) on a GPU. Multispectral data captured from porcine bowel and sheep/rabbit uteri in vivo has been used for training, and the algorithm has been validated using unseen in vivo animal experiments.Comment: accepted by Hamlyn Symposium 201

Minimally-invasive surgical application of multispectral and polarization resolved imaging

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Quantifying Uncertainty

Many of us interact with automated agents every day (e.g., Microsoft\u27s Cortana, Apple’s Siri, Amazon’s Alexa, etc.), and decision-makers at all levels of organizations utilize automated systems that are designed to enable better, faster, and more effective decisions. Understanding the conditions under which humans trust and rely upon automated agents recommendations is important, as trust is one of the mechanisms that allows for humans to interact effectively with a variety of teammates. Reliance and trust in automated systems is changing the way we process information, make decisions, and perform tasks. We conducted an experiment to determine the conditions and personality characteristics that affect human-machine interactions. Our analysis focused on the use of an automated decision aid in conditions of uncertainty. We also looked to see how perceptions of an automated decision aid’s ability related to human trust. Last, we explored how extraversion, a broad factor that encompasses the tendency to be energetic, affiliative, and dominant, related to perceptions of trust in the automated agent. We observed that in conditions of uncertainty, human decision outcomes moved in accordance with the recommendation of the agent. In addition, we found a correlation between perceptions of ability and user trust in the automated agent

Illumination uniformity in endoscopic imaging

Standardised endoscopic digital images were taken and analysed using an image analysis software (National Instruments Vision Assistant version 7.1.1). The luminance plane was extracted and the pixel intensity distribution was determined along a horizontal line at the position of highest average intensity (centroid). The data was exported to MS Excel and the pixel intensity (y-axis) was plotted against pixel position (x-axis). A trendline using a 2nd order polynomial curve was fitted to each data set. The resultant equation for each curve was compared with equations obtained from other images taken under various illumination conditions and settings

Clinically insignificant association between anterior knee pain and patellofemoral lesions which are found incidentally.

Patellofemoral chondral lesions are frequently identified incidentally during the arthroscopic treatment of other knee pathologies. A role has been described for arthroscopic debridement of such lesions when symptoms are known to originate from pathology of the patellofemoral joint. However, it remains unclear how to manage lesions which are found incidentally whilst tackling other pathologies. The purpose of this study was to establish the strength of association between anterior knee pain and patellofemoral lesions identified incidentally in a typical arthroscopic population. A consecutive series of patients undergoing arthroscopy for a range of standard indications formed the basis of this cross section study. We excluded those with patellofemoral conditions in order to identify patellofemoral lesions which were solely incidental. Pre-operative assessments were performed on 64 patients, where anterior knee pain was sought by three methods: an annotated photographic knee pain map (PKPM), patient indication with one finger and by palpated tenderness. A single surgeon, who was blinded to previous recordings, performed standard arthroscopies and recorded patellofemoral lesions. Statistical correlations were performed to identify the association magnitude. Associations were identified between incidental patellofemoral lesions and tenderness palpated on the medial patella (P=0.007, χ2=0.32) and the quadriceps tendon (P=0.029, χ2=0.26), but these associations were at best fair, which could be interpreted as clinically insignificant. In which case incidental patellofemoral lesions are not necessarily associated with anterior knee pain, we suggest that they could be left alone. This recommendation is only applicable to patellofemoral lesions which are found incidentally whilst addressing other pathology