Pneumonia, a respiratory disease often caused by bacterial infection in the distal lung, requires rapid and accurate identification, especially in settings such as critical care. Initiating or de-escalating antimicrobials should ideally be guided by the quantification of pathogenic bacteria for effective treatment. Optical endomicroscopy is an emerging technology with the potential to expedite bacterial detection in the distal lung by enabling in vivo and in situ optical tissue characterisation. With advancements in detector technology, Optical endomicroscopy can utilize fluorescence lifetime imaging (FLIM) to help detect events that were previously challenging or impossible to identify using fluorescence intensity imaging. In this paper, we propose an iterative Bayesian approach for bacterial detection in FLIM. We model the FLIM image as a linear combination of background intensity, Gaussian noise, and additive outliers (labelled bacteria). While previous bacteria detection methods model anomalous pixels as bacteria, here the FLIM outliers are modelled as circularly symmetric Gaussian-shaped objects, based on their discrete shape observed through visual analysis and the physical nature of the imaging modality. A Hierarchical Bayesian model is used to solve the bacterial detection problem where prior distributions are assigned to unknown parameters. A Metropolis-Hastings within Gibbs sampler draws samples from the posterior distribution. The proposed method’s detection performance is initially measured using synthetic images, and shows significant improvement over existing approaches. Further analysis is conducted on real Optical endomicroscopy FLIM images annotated by trained personnel. The experiments show the proposed approach outperforms existing methods by a margin of +16.85% (F1) for detection accuracy.

Bronchoscopy is a medical procedure that involves the insertion of a flexible tube with a camera into the airways to survey, diagnose and treat lung diseases. Due to the complex branching anatomical structure of the bronchial tree and the similarity of the inner surfaces of the segmental airways, navigation systems are now being routinely used to guide the operator during procedures to access the lung periphery. Current navigation systems rely on sensor-integrated bronchoscopes to track the position of the bronchoscope in real-time. This approach has limitations, including increased cost and limited use in non-specialized settings. To address this issue, researchers have proposed visual odometry algorithms to track the bronchoscope camera without the need for external sensors. However, due to the lack of publicly available datasets, limited progress is made. To this end, we have developed a database of bronchoscopy videos in a phantom lung model and ex-vivo human lungs. The dataset contains 34 video sequences with over 23,000 frames with odometry ground truth data collected using electromagnetic tracking sensors. With our dataset, we empower the robotics and machine learning community to advance the field. We share our insights on challenges in endoscopic visual odometry. Furthermore, we provide benchmark results for this dataset. State-of-the-art feature extraction algorithms including SIFT, ORB, Superpoint, Shi- Tomasi, and LoFTR are tested on this dataset. The benchmark results demonstrate that the LoFTR algorithm outperforms other approaches, but still has significant errors in the presence of rapid movements and occlusions.

Accurate shape estimation of concentric tube robots (CTRs) using mathematical models remains a challenge, reinforcing the need to develop techniques for accurate and real-time shape sensing of CTRs. In this paper, we develop a fusion algorithm that predicts the robot's shape by combining a mathematical model of the CTR with a measurement of the Cartesian coordinates of the robot's tip using an electro-magnetic sensor. We experimentally validated our method in static and dynamic scenarios with and without external loading. Results demonstrated that the fusion algorithm improves the error of model-based shape prediction by an average of 44.3%, corresponding to 2.43% of the robot's arc length. Furthermore, we demonstrate that our method can be used in real-time to simultaneously track the robot's tip position and predict its shape.

Abstract In this paper, we introduce our unique dataset of fluorescence lifetime imaging endo/microscopy (FLIM), containing over 100,000 different FLIM images collected from 18 pairs of cancer/non-cancer human lung tissues of 18 patients by our custom fibre-based FLIM system. The aim of providing this dataset is that more researchers from relevant fields can push forward this particular area of research. Afterwards, we describe the best practice of image post-processing suitable per the dataset. In addition, we propose a novel hierarchically aggregated multi-scale architecture to improve the binary classification performance of classic CNNs. The proposed model integrates the advantages of multi-scale feature extraction at different levels, where layer-wise global information is aggregated with branch-wise local information. We integrate the proposal, namely ResNetZ, into ResNet, and appraise it on the FLIM dataset. Since ResNetZ can be configured with a shortcut connection and the aggregations by Addition or Concatenation, we first evaluate the impact of different configurations on the performance. We thoroughly examine various ResNetZ variants to demonstrate the superiority. We also compare our model with a feature-level multi-scale model to illustrate the advantages and disadvantages of multi-scale architectures at different levels.

The Cosserat rod equations are used to model continuum and soft robots. Solving these equations are computationally expensive, particularly due to mixed boundary values and kinematic constraints. In this paper, we present a novel nonlinear observer that can rapidly estimate the solution of the Cosserat rod equations. We present details of the observer design and analyse its convergence and stability. Furthermore, we compare the accuracy and performance of the observer with common solvers used in the literature. Our results show that the proposed observer can significantly improve the computational efficiency of continuum robots’ models and estimates the solution of the Cosserat rod equations 7 times faster than common solvers.

Concentric Tube Robots (CTR) have been gaining ground in minimally-invasive robotic surgeries due to their small footprint, compliance, and high dexterity. CTRs can assure safe interaction with soft tissue, provided that precise and effective motion control is achieved. Controlling the motion of CTRs is still challenging. Commonly used model-based control approaches often employ simplified geometric/dynamic assumptions, which could be very inaccurate in the presence of unmodelled disturbances and external interaction forces. Additionally, application of emerging data-driven algorithms in real-time control of CTRs is limited due to the fact that these controllers require considerable amount of time to let the algorithm develop enough to reach a desired accuracy and relevancy. In this paper, we present a hybrid approach to overcome the aforementioned difficulties. This hybrid solution uses the solution of a kinematic model of the robot to estimate initial values for a model-free data-driven method. The proposed algorithm combines both model-based and data-driven algorithms to provide real-time motion control of CTRs interacting with an unknown external environment. Three different simulations studies were performed to thoroughly evaluate the efficacy of the proposed hybrid control approach as compared to two common model-based and data-driven control techniques. The results demonstrate superior performance of the proposed method. The root-mean-square error of the proposed hybrid approach is less than 1.1 mm, which is 9 times less than a common model-based controller.

OBJECTIVE: Risk prediction models are widely used to inform evidence-based clinical decision making. However, few models developed from single cohorts can perform consistently well at population level where diverse prognoses exist (such as the SARS-CoV-2 [severe acute respiratory syndrome coronavirus 2] pandemic). This study aims at tackling this challenge by synergizing prediction models from the literature using ensemble learning. MATERIALS AND METHODS: In this study, we selected and reimplemented 7 prediction models for COVID-19 (coronavirus disease 2019) that were derived from diverse cohorts and used different implementation techniques. A novel ensemble learning framework was proposed to synergize them for realizing personalized predictions for individual patients. Four diverse international cohorts (2 from the United Kingdom and 2 from China; N = 5394) were used to validate all 8 models on discrimination, calibration, and clinical usefulness. RESULTS: Results showed that individual prediction models could perform well on some cohorts while poorly on others. Conversely, the ensemble model achieved the best performances consistently on all metrics quantifying discrimination, calibration, and clinical usefulness. Performance disparities were observed in cohorts from the 2 countries: all models achieved better performances on the China cohorts. DISCUSSION: When individual models were learned from complementary cohorts, the synergized model had the potential to achieve better performances than any individual model. Results indicate that blood parameters and physiological measurements might have better predictive powers when collected early, which remains to be confirmed by further studies. CONCLUSIONS: Combining a diverse set of individual prediction models, the ensemble method can synergize a robust and well-performing model by choosing the most competent ones for individual patients.

Pneumonia is a major cause of morbidity and mortality of patients in intensive care. Rapid determination of the presence and gram status of the pathogenic bacteria in the distal lung may enable a more tailored treatment regime. Optical Endomicroscopy (OEM) is an emerging medical imaging platform with preclinical and clinical utility. Pulmonary OEM via multi-core fibre bundles has the potential to provide in vivo, in situ, fluorescent molecular signatures of the causes of infection and inflammation. This paper presents a Bayesian approach for bacterial detection in OEM images. The model considered assumes that the observed pixel fluorescence is a linear combination of the actual intensity value associated with tissues or background, corrupted by additive Gaussian noise and potentially by an additional sparse outlier term modelling anomalies (bacteria). The bacteria detection problem is formulated in a Bayesian framework and prior distributions are assigned to the unknown model parameters. A Markov chain Monte Carlo algorithm based on a partially collapsed Gibbs sampler is used to sample the posterior distribution of the unknown parameters. The proposed algorithm is first validated by simulations conducted using synthetic datasets for which good performance is obtained. Analysis is then conducted using two ex vivo lung datasets in which fluorescently labelled bacteria are present in the distal lung. A good correlation between bacteria counts identified by a trained clinician and those of the proposed method, which detects most of the manually annotated regions, is observed.