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Peripheral nerve tumors (PNT) are rare lesions. To date, no systematic multicenter studies on epidemiology, clinical symptoms, treatment strategies and outcomes, genetic and histopathologic features, as well as imaging characteristics of PNT were published. The main goal of our PNT Registry is the systematic multicenter investigation to improve our understanding of PNT and to assist future interventional studies in establishing hypotheses, determining potential endpoints, and assessing treatment efficacy.Aims of the PNT registry were set at the 2015 Meeting of the Section of Peripheral Nerve Surgery of the German Society of Neurosurgery. A study protocol was developed by specialists in PNT care. A minimal data set on clinical status, treatment types and outcomes is reported by each participating center at initial contact with the patient and after 1 year, 2 years, and 5 years. Since the study is coordinated by the Charité Berlin, the PNR Registry was approved by the Charité ethics committee (EA4/058/17) and registered with the German Trials Registry (www.drks.de). On a national level, patient inclusion began in June 2016. The registry was rolled out across Europe at the 2019 meeting of the European Association of Neurosurgery in Dublin.Patient recruitment has been initiated at 10 centers throughout Europe and 14 additional centers are currently applying for local ethics approval.To date, the PNT registry has grown into an international study group with regular scientific and clinical exchange awaiting the first results of the retrospective study arm.

Review question / Objective: This work aims to report and summarize the course of functional recovery following crush injuries, transections and segmental resection of the rat median nerve. Condition being studied: Peripheral nerve injuries. Eligibility criteria: Only such studies were included which featured at least two postoperative time points at in which functional recovery was evaluated. Main outcome(s): Functional recovery as assessed by the grasping test.

Chirurgische Interventionen bergen ein beachtliches Potenzial in der Rehabilitation spastischer Hand- und Armfehlstellungen. Therapeuten können dazu beitragen, dass eine chirurgische Indikationsprüfung möglichst frühzeitig eingeleitet wird, wenn durch konservative Maßnahmen keine Verbesserung mehr zu erreichen ist.

Muscle-in-vein conduits provide an alternative for bridging digital nerve defects when tension-free suture is not possible. Low donor site morbidity and absence of additional costs are favorable advantages compared with autografts or conduits.We retrospectively reviewed 37 patients with 43 defects of proper palmar digital nerves. Primary repair by muscle-in-vein conduits was performed in 22 cases, whereas 21 cases underwent secondary reconstruction. Recovery of sensibility was assessed using static and moving 2-point discrimination and Semmes-Weinstein monofilament testing. Results were compared with the contralateral side serving as a control. Outcome data were stratified according to international guidelines and evaluated for differences in terms of age, gap length, time of reconstruction, and concomitant injuries.The median gap length was 20 mm (range, 9-60 mm). After a median follow-up of 25.0 months (interquartile range, 29.0 months), the median static and moving 2-point discrimination were 7.0 mm and 5.0 mm (interquartile range, 3.0 mm), respectively. The evaluation with Semmes-Weinstein monofilament revealed a median reduction of sensibility of 2 levels compared with the contralateral side. According to the American Society for Surgery of the Hand guidelines, 81.4% of the results were classified as excellent or good, whereas fair and poor results were noted in 9.3% of the cases each. The modified Highet and Sander's criteria rated complete clinical recovery in 13 cases; 23 results were regarded as S3Muscle-in-vein conduits can be considered for primary and secondary reconstruction of digital nerves. Successful sensory recovery in terms of measurable 2-point discrimination was achieved in 91% of all cases.Therapeutic IV.

The gold-standard method for reconstruction of segmental nerve defects, the autologous nerve graft, has several drawbacks in terms of tissue availability and donor site morbidity. Therefore, feasible alternatives to autologous nerve grafts are sought. Muscle-in-vein conduits have been proposed as an alternative to autologous nerve grafts almost three decades ago, given the abundance of both tissues throughout the body. Based on the anti-inflammatory effects of veins and the proregenerative environment established by muscle tissue, this approach has been studied in various preclinical and some clinical trials. There is still no comprehensive systematic summary to conclude efficacy and feasibility of muscle-in-vein conduits for reconstruction of segmental nerve defects. Given this lack of a conclusive summary, we performed a meta-analysis to evaluate the potential of muscle-in-vein conduits. This work’s main findings are profound discrepancies regarding the results following nerve repair by means of muscle-in-vein conduits in a preclinical or clinical setting. We identified differences in study methodology, inter-species neurobiology and the limited number of clinical studies to be the main reasons for the still inconclusive results. In conclusion, we advise for large animal studies to elucidate the feasibility of muscle-in-vein conduits for repair of segmental defects of critical size in mixed nerves.

Peripheral and central nerve injuries are mostly studied in rodents, especially rats, given the fact that these animal models are both cost-effective and a lot of comparative data has been published in the literature. This includes a multitude of assessment methods to study functional recovery following nerve injury and repair. Besides evaluation of nerve regeneration by means of histology, electrophysiology, and other in vivo and in vitro assessment techniques, functional recovery is the most important criterion to determine the degree of neural regeneration. Automated gait analysis allows recording of a vast quantity of gait-related parameters such as Paw Print Area and Paw Swing Speed as well as measures of inter-limb coordination. Additionally, the method provides digital data of the rats' paws after neuronal damage and during nerve regeneration, adding to our understanding of how peripheral and central nervous injuries affect their locomotor behavior. Besides the predominantly used sciatic nerve injury model, other models of peripheral nerve injury such as the femoral nerve can be studied by means of this method. In addition to injuries of the peripheral nervous systems, lesions of the central nervous system, e.g., spinal cord contusion can be evaluated. Valid and reproducible data assessment is strongly dependent on meticulous adjustment of the hard- and software settings prior to data acquisition. Additionally, proper training of the experimental animals is of crucial importance. This work aims to illustrate the use of computerized automated gait analysis to assess functional recovery in different animal models of peripheral nerve injury as well as spinal cord contusion injury. It also emphasizes the method's limitations, e.g., evaluation of nerve regeneration in rats with sciatic nerve neurotmesis due to limited functional recovery. Therefore, this protocol is thought to help researchers interested in peripheral and central nervous injuries to assess functional recovery in rodent models.

Injuries of the peripheral nervous system are common among the population affecting around 3% of all trauma patients. This high clinical need in the field of peripheral nerve injury and regeneration has been steadily driving experimental and epidemiological research. Thereby, it is crucial to determine the exact degree of recovery of end-organ function. Regeneration after nerve injuries is assessed by a wide variety of techniques and pre-clinical model systems, where rodent models are among the most widely used. However, results from rodents are difficult to translate to human patients in general, and reproducible and comparable assessment of functional recovery is of highest importance. Computerized gait analysis allows comprehensive acquisition of locomotor function. As the animals cross the recording device voluntarily, functional recovery is assessable with a minimum degree of human interference on their behavior. This article aims to give a detailed overview on the existing literature on CatWalk gait analysis in rodent models of peripheral nerve injuries of upper and lower extremities, e.g. axonotmesis, neurotmesis or fibrosis, with special emphasis on differences between models. Researchers interested in assessment of locomotor function in such models will especially benefit from this work as it will provide them with an overview of the various experimental setups and expected outcomes. This work also addresses potential pitfalls and hurdles in order to promote well designed, comparable studies allowing for accelerated development of therapeutic strategies in peripheral repair and regeneration.

Background A great extent of knowledge on peripheral nerve regeneration has been gathered using the rat sciatic nerve model. The femoral nerve model of the rat offers an interesting alternative, as it lacks disadvantageous features such as automutilation. For the analysis of locomotor behavior in rats after sciatic nerve injury, the CatWalk™ XT Gait Analysis System is often used. However, lesions of the femoral nerve in the rat have yet remained unstudied with this method. Material and Methods Ten male Sprague Dawley rats were evaluated with the CatWalk XT to study their gait after a 6‐mm resection of the right femoral nerve and reconstruction with an autologous nerve graft. Animals were observed for 10 weeks after surgery. Results Print Area, Print Length, Swing Speed, and Duty Cycle decreased to a minimum of 40% of baseline 2 weeks after surgery. Swing Time was elevated more than twofold at this time point. However, all these parameters recovered back to >90% of baseline values at 10 weeks after surgery. This degree of functional recovery has not been reported after sciatic nerve resection and autograft repair. Base of support varied minimally postoperatively in contrast to a strong decrement after sciatic nerve resection and repair. Conclusion We hereby provide a comprehensive in‐depth analysis of how to study functional recovery after injury of the femoral nerve in the rat via the CatWalk XT. We place special emphasis on highlighting the differences between the femoral nerve and sciatic nerve injury model in this context., Peripheral nerve repair is often studied in the sciatic nerve model of the rat. However, the degree of functional recovery of locomotor function is limited after segmental damage of the sciatic nerve. The femoral nerve model offers an interesting alternative, as functional recovery ad integrum is observable ten weeks after surgery.

Evaluation of automated single-tree recognition from airborne laser scanning data In the present study, we investigated whether the detection tool FINT (Find Individual Trees) can identify single trees out of canopy height models (CHM) precisely enough to assess the protective effect of forests, even on steep slopes. For this purpose, reference trees were measured and described in twelve randomly selected sample plots in the Bündner Herrschaft and Schanfigg regions (Canton Graubünden, Switzerland). CHMs of different resolution and smoothing were generated from airborne laser scanning data for each sample plot and subsequently processed with FINT. In addition, we tested whether the use of a model that defines the minimum distance between a tree and its neighbours based on its height (MBA model) improved the quality of the results. The study showed that a finer-resolution CHM combined with stronger smoothing produced results comparable to those obtained with an unsmoothed and lower-resolution CHM. The smallest difference between the numbers of trees measured and detected was achieved with the 1-m resolution CHM, with no smoothing and no MBA model. In conclusion, FINT can provide a basis for assessing the protective effect of a forest with its existing structures, and its results – after evaluation in the field – can be directly integrated into natural hazard simulation models.

Direct assessment of forest regeneration from remote sensing data is a previously little-explored problem. This is due to several factors which complicate object detection of small trees in the understory. Most existing studies are based on airborne laser scanning (ALS) data, which often has insufficient point densities in the understory forest layers. The present study uses plot-based terrestrial laser scanning (TLS) and the survey design was similar to traditional forest inventory practices. Furthermore, a framework of methods was developed to solve the difficulties of detecting understory trees for quantifying regeneration in temperate montane forest. Regeneration is of special importance in our montane study area, since large parts are declared as protection forest against alpine natural hazards. Close to nature forest structures were tackled by separating 3D tree stem detection from overall tree segmentation. In support, techniques from 3D mathematical morphology, Hough transformation and state-of-the-art machine learning were applied. The methodical framework consisted of four major steps. These were the extraction of the tree stems, the estimation of the stem diameters at breast height (DBH), the image segmentation into individual trees and finally, the separation of two groups of regeneration. All methods were fully automated and utilized volumetric 3D image information which was derived from the original point cloud. The total amount of regeneration was split into established regeneration, consisting of trees with a height > 130 cm in combination with a DBH < 12 cm and unestablished regeneration, consisting of trees with a height < 130 cm. Validation was carried out against field-based expert estimates of percentage ground cover, differentiating seven classes that were similar to those used by forest inventory. The mean absolute error (MAE) of our method for established regeneration was 1.11 classes and for unestablished regeneration only 0.27 classes. Considering the metrical distances between the class centres, the MAE amounted 8.08% for established regeneration and 2.23% for unestablished regeneration.

Classification approaches for urban areas are mostly of qualitative and semantic nature. They produce interpreted classes similar to those from land cover and land use classifications. As a complement to those classes, quantitative measures directly derived from the image could lead to a metric characterization of the urban area. While these metrics lack of qualitative interpretation they are able to provide objective measure of the urban structures.Such quantitative measures are especially important in rapidly growing cities since, beside of the growth in area, they can provide structural information for specific areas and detect changes. Rustenburg, which serves as test area for the present study, is amongst the fastest growing cities in South Africa. It reveals a heterogeneous face of housing and building structures reflecting social and/or economic differences often linked to the spatial distribution of industrial and local mining sites. Up to date coverage with aerial photographs is provided by aerial surveys in regular intervals. Also recent satellite systems provide imagery with suitable resolution. Using such set of very high resolution images a fully automated algorithm has been developed which outputs metric classes by systematically combining important measures of building structure. The measurements are gained by decomposition of buildings directly from the imagery and by using methods from mathematical morphology. The decomposed building objects serve as basis for the computation of grid statistics. Finally a systematic combination of the single features leads to combined metrical classes.For the dominant urban structures verification results indicate an overall accuracy of at least 80% on the single feature level and 70% for the combined classes., JRC.G.2-Global security and crisis management

The automatic extraction of single trees from remotely sensed data is approached in numerous studies, but results are still insufficient in areas of dense temperate forest. Common watershed-based algorithms using digital surface models tend to produce erroneous results in difficult constellations because the treetop determination lacks an exact criterion for smoothing. In this article, a new approach is introduced that classifies crown size in advance and uses this information as prior knowledge for single-tree extraction. Crown size is classified from texture with an improved grey-scale granulometry followed by a crown size adapted watershed segmentation of single trees. The method is applied on a large area of 10 km2 and verified on six reference plots reflecting diverse and difficult compositions. The accuracy varies between 64% and 88%, and shows an average improvement of about 30% for deciduous and mixed stands compared to a non-crown-size-dependent algorithm.

Precise tree species classification with high density full-waveform LiDAR data is a key research topic for automated forest inventory. Most approaches constrain to geometric features and only a few consider intensity values. Since full-waveform data offers a much larger amount of deducible information this study explores a high number of parameter and feature combinations. Those variables having the highest impact on species differentiation are determined. To handle the large amount of airborne fullwaveform data and to extract a comprehensive number of variable combinations an improved algorithm was developed. The full-waveform point parameters amplitude, width, range corrected intensity and total number of targets within a beam are transferred into raster covering a test site of 10 km2 .I t was possible to isolate the three most important variables based on the intensity, the width and the total number of targets. Up to six tree species were classified with an overall accuracy of 57%, limiting to the four main species accuracy was improved to 78% and constraining just to conifers and broadleaved trees even 91% could be classified correctly. © 2010 Elsevier B.V. All rights reserved.

The present study introduces an advanced method for 3D segmentation of terrestrial laser scanning data into single tree clusters. It intentionally tackled difficult forest situations with dense and structured tree formations, which inventory practitioners are often faced with. The strongly interlocking tree crowns of different sizes and in different layers characterized the test conditions of close to nature forest plots. Volumetric 3D images of the plots were derived from the original point cloud data. A clustering method with automatically derived priors focused on the segmentation of these images by global optimization. Therefore, each image was segmented as a whole and partitioned into individual tree objects using a combination of state-of-the-art techniques. Multiple steps were combined in a workflow that included a morphological detection of the tree stems, the construction of a similarity graph from the image data, the computation of the eigenspectrum which was weighted with the tree stem priors and the final labelling of the transformed data points in a Markov Random Field framework. The detected trees were verified by number and position which allowed for comparison with other studies. Additionally, for a subset of the data, we provided a detailed verification of the three-dimensional extent of the complete trees. The detection rate by number and position was 97.40% for major trees with a stem diameter at breast height (DBH) ≥ 12 cm and 84.62% for regeneration trees with a DBH < 12 cm. The three-dimensional extent of the detected trees resulted in an average producer’s accuracy of 93.66% and a user’s accuracy of 94.06%. Overall, these numbers confirm the capacity of the method for accurate segmentation of strongly layered and understory trees. Future studies could test the method on wider areas with large scale data and different forest types in order to determine its general transferability.

Recently, a new method on tree stem isolation using volumetric image data from terrestrial laser scans (TLS) has been introduced by the same authors. The method transfers TLS data into a voxel grid data structure and isolates the tree stems from the overall forest vegetation. While the stem detection method yields on a three dimensional localisation of the tree stems, the present study introduces a supplemental technique, which accurately estimates the diameter at breast height (DBH) from the stem objects. Often, large pieces of the stems are occluded by other vegetation and are only partially represented in the laser scanning data, not covering the complete circumference. Therefore, it was not possible to measure the diameter at 130 cm height directly on the stem imagery. Instead, a method has been developed, which estimated the diameter from the fragmented stem information at the specific cross sections. The stem information was processed in a way, which allowed applying a Hough transform to the image for fitting circles to the cross sections. In contrast to other studies, Hough transform was applied to single stem images with information from other vegetation parts already being removed. Even in cases where only a single and very small fragment of a stem is available, the diameter could be estimated from the curvature. It also has been demonstrated that the image resolution for DBH measurement can be significantly higher than the resolution used for stem isolation in order to increase the precision. Verification of the computed DBH on nine spatially independent test sites showed that applying the Hough transform to single stem cross section images produced accurate results. When excluding the five strongest individual outliers a bias of −0.02 cm, a root mean square error (RMSE) of 2.9 cm and a R 2 of 0.98 were achieved.

Species-specific information is important for many tasks related to forest management. We review the use of airborne laser scanning (ALS) and complementary data for providing this information. The main ALS-based information is related to structural features, intensity of the echoes, and waveform parameters, whereas spectral information may be provided by fusing data from different sensors. Various types of classifiers are applied, the current emphasis being in non-linear or otherwise complex techniques. The results are successful with respect to the main species, whereas the overall accuracy depends on the desired level of detail in the classification. We expect fusion approaches combining ALS and especially hyperspectral data to become more common and further improvements by the development of advanced sensor technology.

Georadar and sedimentological data were acquired in the Ny-Alesund area on Brogger Peninsula in order to study diamicton in frontal areas of Vestre Broggerbreen and Midre Lovenbreen. Parallel common offset georadar lines were acquired for information on thickness and layering and multioffset measurements served to deduce the subground velocity. Short permafrost cores (