Hand eye coordination in surgery

The coordination of the hand in response to visual target selection has always been regarded as an essential quality in a range of professional activities. This quality has thus far been elusive to objective scientific measurements, and is usually engulfed in the overall performance of the individuals. Parallels can be drawn to surgery, especially Minimally Invasive Surgery (MIS), where the physical constraints imposed by the arrangements of the instruments and visualisation methods require certain coordination skills that are unprecedented. With the current paradigm shift towards early specialisation in surgical training and shortened focused training time, selection process should identify trainees with the highest potentials in certain specific skills. Although significant effort has been made in objective assessment of surgical skills, it is only currently possible to measure surgeons’ abilities at the time of assessment. It has been particularly difficult to quantify specific details of hand-eye coordination and assess innate ability of future skills development. The purpose of this thesis is to examine hand-eye coordination in laboratory-based simulations, with a particular emphasis on details that are important to MIS. In order to understand the challenges of visuomotor coordination, movement trajectory errors have been used to provide an insight into the innate coordinate mapping of the brain. In MIS, novel spatial transformations, due to a combination of distorted endoscopic image projections and the “fulcrum” effect of the instruments, accentuate movement generation errors. Obvious differences in the quality of movement trajectories have been observed between novices and experts in MIS, however, this is difficult to measure quantitatively. A Hidden Markov Model (HMM) is used in this thesis to reveal the underlying characteristic movement details of a particular MIS manoeuvre and how such features are exaggerated by the introduction of rotation in the endoscopic camera. The proposed method has demonstrated the feasibility of measuring movement trajectory quality by machine learning techniques without prior arbitrary classification of expertise. Experimental results have highlighted these changes in novice laparoscopic surgeons, even after a short period of training. The intricate relationship between the hands and the eyes changes when learning a skilled visuomotor task has been previously studied. Reactive eye movement, when visual input is used primarily as a feedback mechanism for error correction, implies difficulties in hand-eye coordination. As the brain learns to adapt to this new coordinate map, eye movements then become predictive of the action generated. The concept of measuring this spatiotemporal relationship is introduced as a measure of hand-eye coordination in MIS, by comparing the Target Distance Function (TDF) between the eye fixation and the instrument tip position on the laparoscopic screen. Further validation of this concept using high fidelity experimental tasks is presented, where higher cognitive influence and multiple target selection increase the complexity of the data analysis. To this end, Granger-causality is presented as a measure of the predictability of the instrument movement with the eye fixation pattern. Partial Directed Coherence (PDC), a frequency-domain variation of Granger-causality, is used for the first time to measure hand-eye coordination. Experimental results are used to establish the strengths and potential pitfalls of the technique. To further enhance the accuracy of this measurement, a modified Jensen-Shannon Divergence (JSD) measure has been developed for enhancing the signal matching algorithm and trajectory segmentations. The proposed framework incorporates high frequency noise filtering, which represents non-purposeful hand and eye movements. The accuracy of the technique has been demonstrated by quantitative measurement of multiple laparoscopic tasks by expert and novice surgeons. Experimental results supporting visual search behavioural theory are presented, as this underpins the target selection process immediately prior to visual motor action generation. The effects of specialisation and experience on visual search patterns are also examined. Finally, pilot results from functional brain imaging are presented, where the Posterior Parietal Cortical (PPC) activation is measured using optical spectroscopy techniques. PPC has been demonstrated to involve in the calculation of the coordinate transformations between the visual and motor systems, which establishes the possibilities of exciting future studies in hand-eye coordination

Recent Advances in Image Restoration with Applications to Real World Problems

In the past few decades, imaging hardware has improved tremendously in terms of resolution, making widespread usage of images in many diverse applications on Earth and planetary missions. However, practical issues associated with image acquisition are still affecting image quality. Some of these issues such as blurring, measurement noise, mosaicing artifacts, low spatial or spectral resolution, etc. can seriously affect the accuracy of the aforementioned applications. This book intends to provide the reader with a glimpse of the latest developments and recent advances in image restoration, which includes image super-resolution, image fusion to enhance spatial, spectral resolution, and temporal resolutions, and the generation of synthetic images using deep learning techniques. Some practical applications are also included

Region-based Appearance and Flow Characteristics for Anomaly Detection in Infrared Surveillance Imagery

Anomaly detection is a classical problem within automated visual surveillance, namely the determination of the normal from the abnormal when operational data availability is highly biased towards one class (normal) due to both insufficient sample size, and inadequate distribution coverage for the other class (abnormal). In this work, we propose the dual use of both visual appearance and localized motion characteristics, derived from optic flow, applied on a per-region basis to facilitate object-wise anomaly detection within this context. Leveraging established object localization techniques from a region proposal network, optic flow is extracted from each object region and combined with appearance in the far infrared (thermal) band to give a 3-channel spatiotemporal tensor representation for each object (1 × thermal - spatial appearance; 2 × optic flow magnitude as x and y components - temporal motion). This formulation is used as the basis for training contemporary semi-supervised anomaly detection approaches in a region-based manner such that anomalous objects can be detected as a combination of appearance and/or motion within the scene. Evaluation is performed using the LongTerm infrared (thermal) Imaging (LTD) benchmark dataset against which successful detection of both anomalous object appearance and motion characteristics are demonstrated using a range of semi-supervised anomaly detection approaches

Robust Tracking in Aerial Imagery Based on an Ego-Motion Bayesian Model

A novel strategy for object tracking in aerial imagery is presented, which is able to deal with complex situations where the camera ego-motion cannot be reliably estimated due to the aperture problem (related to low structured scenes), the strong ego-motion, and/or the presence of independent moving objects. The proposed algorithm is based on a complex modeling of the dynamic information, which simulates both the object and the camera dynamics to predict the putative object locations. In this model, the camera dynamics is probabilistically formulated as a weighted set of affine transformations that represent possible camera ego-motions. This dynamic model is used in a Particle Filter framework to distinguish the actual object location among the multiple candidates, that result from complex cluttered backgrounds, and the presence of several moving objects. The proposed strategy has been tested with the aerial FLIR AMCOM dataset, and its performance has been also compared with other tracking techniques to demonstrate its efficiency

Visual analysis and synthesis with physically grounded constraints

The past decade has witnessed remarkable progress in image-based, data-driven vision and graphics. However, existing approaches often treat the images as pure 2D signals and not as a 2D projection of the physical 3D world. As a result, a lot of training examples are required to cover sufficiently diverse appearances and inevitably suffer from limited generalization capability. In this thesis, I propose "inference-by-composition" approaches to overcome these limitations by modeling and interpreting visual signals in terms of physical surface, object, and scene. I show how we can incorporate physically grounded constraints such as scene-specific geometry in a non-parametric optimization framework for (1) revealing the missing parts of an image due to removal of a foreground or background element, (2) recovering high spatial frequency details that are not resolvable in low-resolution observations. I then extend the framework from 2D images to handle spatio-temporal visual data (videos). I demonstrate that we can convincingly fill spatio-temporal holes in a temporally coherent fashion by jointly reconstructing the appearance and motion. Compared to existing approaches, our technique can synthesize physically plausible contents even in challenging videos. For visual analysis, I apply stereo camera constraints for discovering multiple approximately linear structures in extremely noisy videos with an ecological application to bird migration monitoring at night. The resulting algorithms are simple and intuitive while achieving state-of-the-art performance without the need of training on an exhaustive set of visual examples

Online Mutual Foreground Segmentation for Multispectral Stereo Videos

The segmentation of video sequences into foreground and background regions is a low-level process commonly used in video content analysis and smart surveillance applications. Using a multispectral camera setup can improve this process by providing more diverse data to help identify objects despite adverse imaging conditions. The registration of several data sources is however not trivial if the appearance of objects produced by each sensor differs substantially. This problem is further complicated when parallax effects cannot be ignored when using close-range stereo pairs. In this work, we present a new method to simultaneously tackle multispectral segmentation and stereo registration. Using an iterative procedure, we estimate the labeling result for one problem using the provisional result of the other. Our approach is based on the alternating minimization of two energy functions that are linked through the use of dynamic priors. We rely on the integration of shape and appearance cues to find proper multispectral correspondences, and to properly segment objects in low contrast regions. We also formulate our model as a frame processing pipeline using higher order terms to improve the temporal coherence of our results. Our method is evaluated under different configurations on multiple multispectral datasets, and our implementation is available online.Comment: Preprint accepted for publication in IJCV (December 2018

Markerless Kinematics of Pediatric Manual Wheelchair Mobility

Pediatric manual wheelchair users face substantial risk of orthopaedic injury to the upper extremities, particularly the shoulders, during transition to wheelchair use and during growth and development. Propulsion strategy can influence mobility efficiency, activity participation, and quality of life. The current forefront of wheelchair biomechanics research includes translating findings from adult to pediatric populations, improving the quality and efficiency of care under constrained clinical funding, and understanding injury mechanisms and risk factors. Typically, clinicians evaluate wheelchair mobility using marker-based motion capture and instrumentation systems that are precise and accurate but also time-consuming, inconvenient, and expensive for repeated assessments. There is a substantial need for technology that evaluates and improves wheelchair mobility outside of the laboratory to provide better outcomes for wheelchair users, enhancing clinical data. Advancement in this area gives physical therapists better tools and the supporting research necessary to improve treatment efficacy, mobility, and quality of life in pediatric wheelchair users. This dissertation reports on research studies that evaluate the effect of physiotherapeutic training on manual wheelchair mobility. In particular, these studies (1) develop and characterize a novel markerless motion capture-musculoskeletal model systems interface for kinematic assessment of manual wheelchair propulsion biomechanics, (2) conduct a longitudinal investigation of pediatric manual wheelchair users undergoing intensive community-based therapy to determine predictors of kinematic response, and (3) evaluate propulsion pattern-dependent training efficacy and musculoskeletal behavior using visual biofeedback.Results of the research studies show that taking a systems approach to the kinematic interface produces an effective and reliable system for kinematic assessment and training of manual wheelchair propulsion. The studies also show that the therapeutic outcomes and orthopaedic injury risk of pediatric manual wheelchair users are significantly related to the propulsion pattern employed. Further, these subjects can change their propulsion pattern in response to therapy even in the absence of wheelchair-based training, and have pattern-dependent differences in joint kinematics, musculotendon excursion, and training response. Further clinical research in this area is suggested, with a focus on refining physiotherapeutic training strategies for pediatric manual wheelchair users to develop safer and more effective propulsion patterns