Effects of Bioactive Glass Scaffold and BMP-2 in Segmental Defects

poster abstractReconstruction of segmental defects in the load-bearing area has long been a challenge in orthopaedics. We have demonstrated the feasibility of a biodegradable load-bearing scaffold fabricated from poly(propylene fumarate)/tricalcium phosphate (PPF/TCP) loaded with bone morphogenetic protein-2 (BMP-2) to successfully induce healing in those defects. However, there is limited osteoconduction observed with the PPF/TCP scaffold itself. Furthermore, a recent review on BMP-2 revealed greater risks in radiculities, ectopic bone formation, osteolysis and poor global outcome in association with the use of BMP-2 for spinal fusion. The aims of this study were to evaluate the potential use of a more osteoconductive material 13-93 bioactive glass and the potential side effects of locally delivered BMP-2 on adjacent bones. 13-93 glass scaffolds were fabricated by indirect selective laser sintering and implanted into critical size defects created in rat right femurs with and without 10 micrograms of BMP-2. The X-ray and micro-CT results showed that bridging callus was found as soon as 3 weeks and progressed gradually in the BMP group while minimal bone formation was observed in the control group. As expected, stiffness, peak load and energy to break of the BMP group were all higher than the control group. Higher healing rates in the 13-93 group was found compared to the healing rate in PPF/TCP group evaluated in the past indicating a more osteoconductive nature of the 13-93 scaffolds. The scaffolds of both control and BMP groups were partially degraded after 15 weeks as seen in the histological images. For the effects of local BMP-2 delivery to adjacent bones, no statistical difference in the bone area, mineral content and mineral density was found between control and BMP groups. In conclusion, a 13-93 bioactive glass scaffold with local BMP-2 delivery has been demonstrated for its potential application in treating large bone defects

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

SnappySonic: An Ultrasound Acquisition Replay Simulator

SnappySonic provides an ultrasound acquisition replay simulator designed for public engagement and training. It provides a simple interface to allow users to experience ultrasound acquisition without the need for specialist hardware or acoustically compatible phantoms. The software is implemented in Python, built on top of a set of open source Python modules targeted at surgical innovation. The library has high potential for reuse, most obviously for those who want to simulate ultrasound acquisition, but it could also be used as a user interface for displaying high dimensional images or video data

Hypergraph model of social tagging networks

The past few years have witnessed the great success of a new family of paradigms, so-called folksonomy, which allows users to freely associate tags to resources and efficiently manage them. In order to uncover the underlying structures and user behaviors in folksonomy, in this paper, we propose an evolutionary hypergrah model to explain the emerging statistical properties. The present model introduces a novel mechanism that one can not only assign tags to resources, but also retrieve resources via collaborative tags. We then compare the model with a real-world dataset: \emph{Del.icio.us}. Indeed, the present model shows considerable agreement with the empirical data in following aspects: power-law hyperdegree distributions, negtive correlation between clustering coefficients and hyperdegrees, and small average distances. Furthermore, the model indicates that most tagging behaviors are motivated by labeling tags to resources, and tags play a significant role in effectively retrieving interesting resources and making acquaintance with congenial friends. The proposed model may shed some light on the in-depth understanding of the structure and function of folksonomy.Comment: 7 pages,7 figures, 32 reference

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

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

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

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

The evaluation of interrelation between the state of sportsmens’ health and environmental occupational factors

Catedra Igienă, USMF „Nicolae Testemiţanu” Centrul Naţional de Medicină Sportivă „Atletmed” al MS RMIn this paper there are presented the peculiarities of correlation between physiological indexes of funcţional systems of sportsmens’ organism and occupational factors of the environment. On the basis of correlation analysis it was established the fact that the functional state of sportsmen’s organisms correlates with the environmental factors of training, not only directly but also indirectly, from unimportant connections till the manifested ones. Sunt prezentate particularităţile de corelaţie a indicilor fiziologici ai sistemelor funcţionale ale organismului sportivilor cu factorii mediului ocupaţional. S-a stabilit, că starea funcţională a organismului sportivilor corelează cu factorii mediului de antrenament, atât direct cât şi indirect, de la legături neînsemnate pănă la cele exprimate

Прогресс в классификации неходжкинских лимфом

We present the review of literature related to the classification of non-Hodgkin’s lymphomas, which form the group of morphologically and biologically heterogeneous malignant disorders of lymphoid tissue. The conceptions about these lymphomas have repeatedly undergone changes depending on interpretation of the origin of cells, which constitute the morphological substrate of the tumor. As far as the new data about morphological, immunological and cytogenetic features of lymphoid cells has been accumulated, the classifications of non-Hodgkin’s lymphomas have been elaborated with respect to histological types and subtypes, which are of clinical significance. The evolution and progress of classification of these tumors are analyzed in this article. The clearly outlined histological types of non-Hodgkin’s lymphomas were stipulated on the basis of morphological, immunophenotyping, cytogenetic and clinical patterns in the last classification, adopted by World Health Organization in 2001. Those changed the approaches to the examination and differentiated treatment of patients with such pathology.Приведен литературный обзор по классификациям неходжкинских лимфом, представляющих собой группу злокачественных процессов лимфатической ткани неоднородных с точки зрения морфологии и биологии. Концепции об этих лимфомах неоднократно менялись в зависимости от интерпретации происхождения клеток, образующих морфологический субстрат опухоли. По мере накопления новых данных о морфологии лимфоидных клеток, об их иммунологических и цитогенетических особенностях, были разработаны классификации с отражением вариантов и подвариантов неходжкинских лимфом, имеющих клиническую значимость. В этой работе проанализирована эволюция и прогресс классификаций этих опухолей. В самой последней классификации неходжкинских лимфом, принятой ВОЗ в 2001 году, сформулированы хорошо очерченные варианты на основании характеристики морфологии, иммунофенотипирования, цитогенетики и клиники, изменивших подход к исследованию и дифференцированному лечению больных, страдающих этой патологией