Global rigid registration of CT to video in laparoscopic liver surgery

PURPOSE: Image-guidance systems have the potential to aid in laparoscopic interventions by providing sub-surface structure information and tumour localisation. The registration of a preoperative 3D image with the intraoperative laparoscopic video feed is an important component of image guidance, which should be fast, robust and cause minimal disruption to the surgical procedure. Most methods for rigid and non-rigid registration require a good initial alignment. However, in most research systems for abdominal surgery, the user has to manually rotate and translate the models, which is usually difficult to perform quickly and intuitively. METHODS: We propose a fast, global method for the initial rigid alignment between a 3D mesh derived from a preoperative CT of the liver and a surface reconstruction of the intraoperative scene. We formulate the shape matching problem as a quadratic assignment problem which minimises the dissimilarity between feature descriptors while enforcing geometrical consistency between all the feature points. We incorporate a novel constraint based on the liver contours which deals specifically with the challenges introduced by laparoscopic data. RESULTS: We validate our proposed method on synthetic data, on a liver phantom and on retrospective clinical data acquired during a laparoscopic liver resection. We show robustness over reduced partial size and increasing levels of deformation. Our results on the phantom and on the real data show good initial alignment, which can successfully converge to the correct position using fine alignment techniques. Furthermore, since we can pre-process the CT scan before surgery, the proposed method runs faster than current algorithms. CONCLUSION: The proposed shape matching method can provide a fast, global initial registration, which can be further refined by fine alignment methods. This approach will lead to a more usable and intuitive image-guidance system for laparoscopic liver surgery

Intelligent viewpoint selection for efficient CT to video registration in laparoscopic liver surgery

PURPOSE: Minimally invasive surgery offers advantages over open surgery due to a shorter recovery time, less pain and trauma for the patient. However, inherent challenges such as lack of tactile feedback and difficulty in controlling bleeding lower the percentage of suitable cases. Augmented reality can show a better visualisation of sub-surface structures and tumour locations by fusing pre-operative CT data with real-time laparoscopic video. Such augmented reality visualisation requires a fast and robust video to CT registration that minimises interruption to the surgical procedure. METHODS: We propose to use view planning for efficient rigid registration. Given the trocar position, a set of camera positions are sampled and scored based on the corresponding liver surface properties. We implement a simulation framework to validate the proof of concept using a segmented CT model from a human patient. Furthermore, we apply the proposed method on clinical data acquired during a human liver resection. RESULTS: The first experiment motivates the viewpoint scoring strategy and investigates reliable liver regions for accurate registrations in an intuitive visualisation. The second experiment shows wider basins of convergence for higher scoring viewpoints. The third experiment shows that a comparable registration performance can be achieved by at least two merged high scoring views and four low scoring views. Hence, the focus could change from the acquisition of a large liver surface to a small number of distinctive patches, thereby giving a more explicit protocol for surface reconstruction. We discuss the application of the proposed method on clinical data and show initial results. CONCLUSION: The proposed simulation framework shows promising results to motivate more research into a comprehensive view planning method for efficient registration in laparoscopic liver surgery

On Pattern Selection for Laparoscope Calibration

Camera calibration is a key requirement for augmented reality in surgery. Calibration of laparoscopes provides two challenges that are not sufficiently addressed in the literature. In the case of stereo laparoscopes the small distance (less than 5mm) between the channels means that the calibration pattern is an order of magnitude more distant than the stereo separation. For laparoscopes in general, if an external tracking system is used, hand-eye calibration is difficult due to the long length of the laparoscope. Laparoscope intrinsic, stereo and hand-eye calibration all rely on accurate feature point selection and accurate estimation of the camera pose with respect to a calibration pattern. We compare 3 calibration patterns, chessboard, rings, and AprilTags. We measure the error in estimating the camera intrinsic parameters and the camera poses. Accuracy of camera pose estimation will determine the accuracy with which subsequent stereo or hand-eye calibration can be done. We compare the results of repeated real calibrations and simulations using idealised noise, to determine the expected accuracy of different methods and the sources of error. The results do indicate that feature detection based on rings is more accurate than a chessboard, however this doesn’t necessarily lead to a better calibration. Using a grid with identifiable tags enables detection of features nearer the image boundary, which may improve calibration

Deep residual networks for automatic segmentation of laparoscopic videos of the liver

MOTIVATION: For primary and metastatic liver cancer patients undergoing liver resection, a laparoscopic approach can reduce recovery times and morbidity while offering equivalent curative results; however, only about 10% of tumours reside in anatomical locations that are currently accessible for laparoscopic resection. Augmenting laparoscopic video with registered vascular anatomical models from pre-procedure imaging could support using laparoscopy in a wider population. Segmentation of liver tissue on laparoscopic video supports the robust registration of anatomical liver models by filtering out false anatomical correspondences between pre-procedure and intra-procedure images. In this paper, we present a convolutional neural network (CNN) approach to liver segmentation in laparoscopic liver procedure videos. METHOD: We defined a CNN architecture comprising fully-convolutional deep residual networks with multi-resolution loss functions. The CNN was trained in a leave-one-patient-out cross-validation on 2050 video frames from 6 liver resections and 7 laparoscopic staging procedures, and evaluated using the Dice score. RESULTS: The CNN yielded segmentations with Dice scores ≥0.95 for the majority of images; however, the inter-patient variability in median Dice score was substantial. Four failure modes were identified from low scoring segmentations: minimal visible liver tissue, inter-patient variability in liver appearance, automatic exposure correction, and pathological liver tissue that mimics non-liver tissue appearance. CONCLUSION: CNNs offer a feasible approach for accurately segmenting liver from other anatomy on laparoscopic video, but additional data or computational advances are necessary to address challenges due to the high inter-patient variability in liver appearance

ruvA Mutants that resolve Holliday junctions but do not reverse replication forks

RuvAB and RuvABC complexes catalyze branch migration and resolution of Holliday junctions (HJs) respectively. In addition to their action in the last steps of homologous recombination, they process HJs made by replication fork reversal, a reaction which occurs at inactivated replication forks by the annealing of blocked leading and lagging strand ends. RuvAB was recently proposed to bind replication forks and directly catalyze their conversion into HJs. We report here the isolation and characterization of two separation-of-function ruvA mutants that resolve HJs, based on their capacity to promote conjugational recombination and recombinational repair of UV and mitomycin C lesions, but have lost the capacity to reverse forks. In vivo and in vitro evidence indicate that the ruvA mutations affect DNA binding and the stimulation of RuvB helicase activity. This work shows that RuvA's actions at forks and at HJs can be genetically separated, and that RuvA mutants compromised for fork reversal remain fully capable of homologous recombination

CSAP localizes to polyglutamylated microtubules and promotes proper cilia function and zebrafish development

The diverse populations of microtubule polymers in cells are functionally distinguished by different posttranslational modifications, including polyglutamylation. Polyglutamylation is enriched on subsets of microtubules including those found in the centrioles, mitotic spindle, and cilia. However, whether this modification alters intrinsic microtubule dynamics or affects extrinsic associations with specific interacting partners remains to be determined. Here we identify the microtubule-binding protein centriole and spindle–associated protein (CSAP), which colocalizes with polyglutamylated tubulin to centrioles, spindle microtubules, and cilia in human tissue culture cells. Reducing tubulin polyglutamylation prevents CSAP localization to both spindle and cilia microtubules. In zebrafish, CSAP is required for normal brain development and proper left–right asymmetry, defects that are qualitatively similar to those reported previously for depletion of polyglutamylation-conjugating enzymes. We also find that CSAP is required for proper cilia beating. Our work supports a model in which polyglutamylation can target selected microtubule-associated proteins, such as CSAP, to microtubule subpopulations, providing specific functional capabilities to these populations.National Institutes of Health (U.S.) (Grant no. GM074746)American Cancer Society. Research Scholar Grant (121776)National Institute of General Medical Sciences (U.S.) (GM088313

Anaplastic Lymphoma Kinase Is Required for Neurogenesis in the Developing Central Nervous System of Zebrafish

10.1371/journal.pone.0063757PLoS ONE85

More unlabelled data or label more data? A study on semi-supervised laparoscopic image segmentation

Improving a semi-supervised image segmentation task has the option of adding more unlabelled images, labelling the unlabelled images or combining both, as neither image acquisition nor expert labelling can be considered trivial in most clinical applications. With a laparoscopic liver image segmentation application, we investigate the performance impact by altering the quantities of labelled and unlabelled training data, using a semi-supervised segmentation algorithm based on the mean teacher learning paradigm. We first report a significantly higher segmentation accuracy, compared with supervised learning. Interestingly, this comparison reveals that the training strategy adopted in the semi-supervised algorithm is also responsible for this observed improvement, in addition to the added unlabelled data. We then compare different combinations of labelled and unlabelled data set sizes for training semi-supervised segmentation networks, to provide a quantitative example of the practically useful trade-off between the two data planning strategies in this surgical guidance application

Automatic, global registration in laparoscopic liver surgery

PURPOSE: The initial registration of a 3D pre-operative CT model to a 2D laparoscopic video image in augmented reality systems for liver surgery needs to be fast, intuitive to perform and with minimal interruptions to the surgical intervention. Several recent methods have focussed on using easily recognisable landmarks across modalities. However, these methods still need manual annotation or manual alignment. We propose a novel, fully automatic pipeline for 3D-2D global registration in laparoscopic liver interventions. METHODS: Firstly, we train a fully convolutional network for the semantic detection of liver contours in laparoscopic images. Secondly, we propose a novel contour-based global registration algorithm to estimate the camera pose without any manual input during surgery. The contours used are the anterior ridge and the silhouette of the liver. RESULTS: We show excellent generalisation of the semantic contour detection on test data from 8 clinical cases. In quantitative experiments, the proposed contour-based registration can successfully estimate a global alignment with as little as 30% of the liver surface, a visibility ratio which is characteristic of laparoscopic interventions. Moreover, the proposed pipeline showed very promising results in clinical data from 5 laparoscopic interventions. CONCLUSIONS: Our proposed automatic global registration could make augmented reality systems more intuitive and usable for surgeons and easier to translate to operating rooms. Yet, as the liver is deformed significantly during surgery, it will be very beneficial to incorporate deformation into our method for more accurate registration

Intraoperative Liver Surface Completion with Graph Convolutional VAE

In this work we propose a method based on geometric deep learning to predict the complete surface of the liver, given a partial point cloud of the organ obtained during the surgical laparoscopic procedure. We introduce a new data augmentation technique that randomly perturbs shapes in their frequency domain to compensate the limited size of our dataset. The core of our method is a variational autoencoder (VAE) that is trained to learn a latent space for complete shapes of the liver. At inference time, the generative part of the model is embedded in an optimisation procedure where the latent representation is iteratively updated to generate a model that matches the intraoperative partial point cloud. The effect of this optimisation is a progressive non-rigid deformation of the initially generated shape. Our method is qualitatively evaluated on real data and quantitatively evaluated on synthetic data. We compared with a state-of-the-art rigid registration algorithm, that our method outperformed in visible areas