Surgical consideration for benign bone tumors

Background: The surgical management of symptomatic benign bone tumor has been described in various manners in medical literature. However, there are few published reports on the presentation and surgical management of benign bone tumors in black African patients.Objectives: To determine the pattern of presentation of benign bone tumors and evaluate the common indications for surgery in a Nigerian Orthopedic Center.Materials and Methods: This is a prospective study of 67 patients, surgically treated for benign bone tumors, over a three-year period, at the National Orthopedic Hospital, Lagos, Nigeria.Results: The common histological types include, osteochondroma, giant cell tumor, and the simple bone cyst. These tumors have varying anatomic locations, but are more commonly located around the knee joint. In this series, most of the patients have presented with an active or aggressive stage of the disease. The most common indication for surgery is painful swelling; other indications include a pathological fracture, restricted range of movement, and peripheral nerve compression. The surgical procedures performed are simple excision, curettage, and stabilization; and 1-stage and 2-stage wide resection with reconstruction. Patients with significant bone defects have autologous bone grafting or methylmethacrylate cement application. Further stabilization is achieved with intramedullary or compression plate and screw fixation. Amputation has only been necessary in one patient with a huge aneurysmal bone cyst. At the average follow-up period of 28.6 months, five patients showed recurrence. All were with a histological diagnosis of giant cell tumor.Conclusions: The mode of presentation of benign bone tumors in this group of black African patients is heterogenous, demanding various surgical options. Limb sparing is a largely feasible option, but the recurrence rate is particularly higher for giant cell tumors. Increase in the number of patients presenting with giant cell tumors raises the possibility of an increase in the incidence of this condition in the black African population. Larger multicenter studies in the black African population may shed more light on the actual incidence of giant cell tumors and other bone tumors in this group of patients

Prostate Motion Modelling Using Biomechanically-Trained Deep Neural Networks on Unstructured Nodes

In this paper, we propose to train deep neural networks with biomechanical simulations, to predict the prostate motion encountered during ultrasound-guided interventions. In this application, unstructured points are sampled from segmented pre-operative MR images to represent the anatomical regions of interest. The point sets are then assigned with point-specific material properties and displacement loads, forming the un-ordered input feature vectors. An adapted PointNet can be trained to predict the nodal displacements, using finite element (FE) simulations as ground-truth data. Furthermore, a versatile bootstrap aggregating mechanism is validated to accommodate the variable number of feature vectors due to different patient geometries, comprised of a training-time bootstrap sampling and a model averaging inference. This results in a fast and accurate approximation to the FE solutions without requiring subject-specific solid meshing. Based on 160,000 nonlinear FE simulations on clinical imaging data from 320 patients, we demonstrate that the trained networks generalise to unstructured point sets sampled directly from holdout patient segmentation, yielding a near real-time inference and an expected error of 0.017 mm in predicted nodal displacement

On the localization of the cleavage site in human alpha‐2‐antiplasmin, involved in the generation of the non‐plasminogen binding form

Background: Alpha‐2‐antiplasmin (α2AP) is the main natural inhibitor of plasmin. The C‐terminus of α2AP is crucial for the initial interaction with plasmin(ogen) and the rapid inhibitory mechanism. Approximately 35% of circulating α2AP has lost its C‐terminus (non‐plasminogen binding α2AP/NPB‐α2AP) and thereby its rapid inhibitory capacity. The C‐terminal cleavage site of α2AP is still unknown. A commercially available monoclonal antibody against α2AP (TC 3AP) detects intact but not NPB‐α2AP, suggesting that the cleavage site is located N‐terminally from the epitope of TC 3AP. Objectives: To determine the epitope of TC 3AP and then to localize the C‐terminal cleavage site of α2AP. Methods: For epitope mapping of TC 3AP, commercially available plasma purified α2AP was enzymatically digested with Asp‐N, Glu‐C, or Lys‐N. The resulting peptides were immunoprecipitated using TC 3AP‐loaded Dynabeads® Protein G. Bound peptides were eluted and analyzed by liquid chromatography‐tandem mass spectometry (LC‐MS/MS). To localize the C‐terminal cleavage site precisely, α2AP (intact and NPB) was purified from plasma and analyzed by LC‐MS/MS after enzymatic digestion with Arg‐C. Results: We localized the epitope of TC 3AP between amino acid residues Asp428 and Gly439. LC‐MS/MS data from plasma purified α2AP showed that NPB‐α2AP results from cleavage at Gln421‐Asp422 as preferred site, but also after Leu417, Glu419, Gln420, or Asp422. Conclusions: The C‐terminal cleavage site of human α2AP is located N‐terminally from the TC 3AP epitope. Because C‐terminal cleavage of α2AP can occur after multiple residues, different proteases may be responsible for the generation of NPB‐α2AP

Segregation of the polyphyletic genus Polyalthia (Annonaceae)

Oral Presentation - Session 1: Taxonoly & Biosystematic-1Main Theme: Contributions of Flora Malesiana to the Welfare of People in AsiaThe circumscription of the species-rich genus Polyalthia (Annonaceae, with ca. 155 species) has long been recognised to be highly problematic: as previously circumscribed, the genus was a morphologically heterogeneous assemblage lacking conspicuous synap...postprin

DeepReg: a deep learning toolkit for medical image registration

Image fusion is a fundamental task in medical image analysis and computer-assisted intervention. Medical image registration, computational algorithms that align different images together (Hill et al., 2001), has in recent years turned the research attention towards deep learning. Indeed, the representation ability to learn from population data with deep neural networks has opened new possibilities for improving registration generalisability by mitigating difficulties in designing hand-engineered image features and similarity measures for many realworld clinical applications (Fu et al., 2020; Haskins et al., 2020). In addition, its fast inference can substantially accelerate registration execution for time-critical tasks. DeepReg is a Python package using TensorFlow (Abadi et al., 2015) that implements multiple registration algorithms and a set of predefined dataset loaders, supporting both labelledand unlabelled data. DeepReg also provides command-line tool options that enable basic and advanced functionalities for model training, prediction and image warping. These implementations, together with their documentation, tutorials and demos, aim to simplify workflows for prototyping and developing novel methodology, utilising latest development and accessing quality research advances. DeepReg is unit tested and a set of customised contributor guidelines are provided to facilitate community contributions. A submission to the MICCAI Educational Challenge has utilised the DeepReg code and demos to explore the link between classical algorithms and deep-learning-based methods (Montana Brown et al., 2020), while a recently published research work investigated temporal changes in prostate cancer imaging, by using a longitudinal registration adapted from the DeepReg code (Yang et al., 2020)

Learning image quality assessment by reinforcing task amenable data selection

In this paper, we consider a type of image quality assessment as a task-specific measurement, which can be used to select images that are more amenable to a given target task, such as image classification or segmentation. We propose to train simultaneously two neural networks for image selection and a target task using reinforcement learning. A controller network learns an image selection policy by maximising an accumulated reward based on the target task performance on the controller-selected validation set, whilst the target task predictor is optimised using the training set. The trained controller is therefore able to reject those images that lead to poor accuracy in the target task. In this work, we show that the controller-predicted image quality can be significantly different from the task-specific image quality labels that are manually defined by humans. Furthermore, we demonstrate that it is possible to learn effective image quality assessment without using a ``clean'' validation set, thereby avoiding the requirement for human labelling of images with respect to their amenability for the task. Using $6712$, labelled and segmented, clinical ultrasound images from $259$ patients, experimental results on holdout data show that the proposed image quality assessment achieved a mean classification accuracy of $0.94\pm0.01$ and a mean segmentation Dice of $0.89\pm0.02$, by discarding $5\%$ and $15\%$ of the acquired images, respectively. The significantly improved performance was observed for both tested tasks, compared with the respective $0.90\pm0.01$ and $0.82\pm0.02$ from networks without considering task amenability. This enables image quality feedback during real-time ultrasound acquisition among many other medical imaging applications

Adaptable image quality assessment using meta-reinforcement learning of task amenability

The performance of many medical image analysis tasks are strongly associated with image data quality. When developing modern deep learning algorithms, rather than relying on subjective (human-based) image quality assessment (IQA), task amenability potentially provides an objective measure of task-specific image quality. To predict task amenability, an IQA agent is trained using reinforcement learning (RL) with a simultaneously optimised task predictor, such as a classification or segmentation neural network. In this work, we develop transfer learning or adaptation strategies to increase the adaptability of both the IQA agent and the task predictor so that they are less dependent on high-quality, expert-labelled training data. The proposed transfer learning strategy re-formulates the original RL problem for task amenability in a meta-reinforcement learning (meta-RL) framework. The resulting algorithm facilitates efficient adaptation of the agent to different definitions of image quality, each with its own Markov decision process environment including different images, labels and an adaptable task predictor. Our work demonstrates that the IQA agents pre-trained on non-expert task labels can be adapted to predict task amenability as defined by expert task labels, using only a small set of expert labels. Using 6644 clinical ultrasound images from 249 prostate cancer patients, our results for image classification and segmentation tasks show that the proposed IQA method can be adapted using data with as few as respective 19.7 % % and 29.6 % % expert-reviewed consensus labels and still achieve comparable IQA and task performance, which would otherwise require a training dataset with 100 % % expert labels

Evidence for long-term sensitization of the bowel in patients with post-infectious-IBS.

Post-infectious irritable bowel syndrome (PI-IBS) is a common gastrointestinal disorder characterized by persistent abdominal pain despite recovery from acute gastroenteritis. The underlying mechanisms are unclear, although long-term changes in neuronal function, and low grade inflammation of the bowel have been hypothesized. We investigated the presence and mechanism of neuronal sensitization in a unique cohort of individuals who developed PI-IBS following exposure to contaminated drinking water 7 years ago. We provide direct evidence of ongoing sensitization of neuronal signaling in the bowel of patients with PI-IBS. These changes occur in the absence of any detectable tissue inflammation, and instead appear to be driven by pro-nociceptive changes in the gut micro-environment. This is evidenced by the activation of murine colonic afferents, and sensitization responses to capsaicin in dorsal root ganglia (DRGs) following application of supernatants generated from tissue biopsy of patients with PI-IBS. We demonstrate that neuronal signaling within the bowel of PI-IBS patients is sensitized 2 years after the initial infection has resolved. This sensitization appears to be mediated by a persistent pro-nociceptive change in the gut micro-environment, that has the capacity to stimulate visceral afferents and facilitate neuronal TRPV1 signaling

Prostate Motion Modelling Using Biomechanically-Trained Deep Neural Networks on Unstructured Nodes

In this paper, we propose to train deep neural networks with biomechanical simulations, to predict the prostate motion encountered during ultrasound-guided interventions. In this application, unstructured points are sampled from segmented pre-operative MR images to represent the anatomical regions of interest. The point sets are then assigned with point-specific material properties and displacement loads, forming the un-ordered input feature vectors. An adapted PointNet can be trained to predict the nodal displacements, using finite element (FE) simulations as ground-truth data. Furthermore, a versatile bootstrap aggregating mechanism is validated to accommodate the variable number of feature vectors due to different patient geometries, comprised of a training-time bootstrap sampling and a model averaging inference. This results in a fast and accurate approximation to the FE solutions without requiring subject-specific solid meshing. Based on 160,000 nonlinear FE simulations on clinical imaging data from 320 patients, we demonstrate that the trained networks generalise to unstructured point sets sampled directly from holdout patient segmentation, yielding a near real-time inference and an expected error of 0.017 mm in predicted nodal displacement