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

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

A pre-operative planning framework for global registration of laparoscopic ultrasound to CT images

PURPOSE: Laparoscopic ultrasound (LUS) enhances the safety of laparoscopic liver resection by enabling real-time imaging of internal structures such as vessels. However, LUS probes can be difficult to use, and many tumours are iso-echoic and hence are not visible. Registration of LUS to a pre-operative CT or MR scan has been proposed as a method of image guidance. However, the field of view of the probe is very small compared to the whole liver, making the registration task challenging and dependent on a very accurate initialisation. METHODS: We propose the use of a subject-specific planning framework that provides information on which anatomical liver regions it is possible to acquire vascular data that is unique enough for a globally optimal initial registration. Vessel-based rigid registration on different areas of the pre-operative CT vascular tree is used in order to evaluate predicted accuracy and reliability. RESULTS: The planning framework is tested on one porcine subject where we have taken 5 independent sweeps of LUS data from different sections of the liver. Target registration error of vessel branching points was used to measure accuracy. Global registration based on vessel centrelines is applied to the 5 datasets. In 3 out of 5 cases registration is successful and in agreement with the planning. Further tests with a CT scan under abdominal insufflation show that the framework can provide valuable information in all of the 5 cases. CONCLUSIONS: We have introduced a planning framework that can guide the surgeon on how much LUS data to collect in order to provide a reliable globally unique registration without the need for an initial manual alignment. This could potentially improve the usability of these methods in clinic

Fishing the Molecular Bases of Treacher Collins Syndrome

Treacher Collins syndrome (TCS) is an autosomal dominant disorder of craniofacial development, and mutations in the TCOF1 gene are responsible for over 90% of TCS cases. The knowledge about the molecular mechanisms responsible for this syndrome is relatively scant, probably due to the difficulty of reproducing the pathology in experimental animals. Zebrafish is an emerging model for human disease studies, and we therefore assessed it as a model for studying TCS. We identified in silico the putative zebrafish TCOF1 ortholog and cloned the corresponding cDNA. The derived polypeptide shares the main structural domains found in mammals and amphibians. Tcof1 expression is restricted to the anterior-most regions of zebrafish developing embryos, similar to what happens in mouse embryos. Tcof1 loss-of-function resulted in fish showing phenotypes similar to those observed in TCS patients, and enabled a further characterization of the mechanisms underlying craniofacial malformation. Besides, we initiated the identification of potential molecular targets of treacle in zebrafish. We found that Tcof1 loss-of-function led to a decrease in the expression of cellular proliferation and craniofacial development. Together, results presented here strongly suggest that it is possible to achieve fish with TCS-like phenotype by knocking down the expression of the TCOF1 ortholog in zebrafish. This experimental condition may facilitate the study of the disease etiology during embryonic development

A Src-Tks5 Pathway Is Required for Neural Crest Cell Migration during Embryonic Development

In the adult organism, cell migration is required for physiological processes such as angiogenesis and immune surveillance, as well as pathological events such as tumor metastasis. The adaptor protein and Src substrate Tks5 is necessary for cancer cell migration through extracellular matrix in vitro and tumorigenicity in vivo. However, a role for Tks5 during embryonic development, where cell migration is essential, has not been examined. We used morpholinos to reduce Tks5 expression in zebrafish embryos, and observed developmental defects, most prominently in neural crest-derived tissues such as craniofacial structures and pigmentation. The Tks5 morphant phenotype was rescued by expression of mammalian Tks5, but not by a variant of Tks5 in which the Src phosphorylation sites have been mutated. We further evaluated the role of Tks5 in neural crest cells and neural crest-derived tissues and found that loss of Tks5 impaired their ventral migration. Inhibition of Src family kinases also led to abnormal ventral patterning of neural crest cells and their derivatives. We confirmed that these effects were likely to be cell autonomous by shRNA-mediated knockdown of Tks5 in a murine neural crest stem cell line. Tks5 was required for neural crest cell migration in vitro, and both Src and Tks5 were required for the formation of actin-rich structures with similarity to podosomes. Additionally, we observed that neural crest cells formed Src-Tks5-dependent cell protrusions in 3-D culture conditions and in vivo. These results reveal an important and novel role for the Src-Tks5 pathway in neural crest cell migration during embryonic development. Furthermore, our data suggests that this pathway regulates neural crest cell migration through the generation of actin-rich pro-migratory structures, implying that similar mechanisms are used to control cell migration during embryogenesis and cancer metastasis