Unsupervised Region-based Anomaly Detection in Brain MRI with Adversarial Image Inpainting

Medical segmentation is performed to determine the bounds of regions of interest (ROI) prior to surgery. By allowing the study of growth, structure, and behaviour of the ROI in the planning phase, critical information can be obtained, increasing the likelihood of a successful operation. Usually, segmentations are performed manually or via machine learning methods trained on manual annotations. In contrast, this paper proposes a fully automatic, unsupervised inpainting-based brain tumour segmentation system for T1-weighted MRI. First, a deep convolutional neural network (DCNN) is trained to reconstruct missing healthy brain regions. Then, upon application, anomalous regions are determined by identifying areas of highest reconstruction loss. Finally, superpixel segmentation is performed to segment those regions. We show the proposed system is able to segment various sized and abstract tumours and achieves a mean and standard deviation Dice score of 0.771 and 0.176, respectively

OrthopedVR: clinical assessment and pre-operative planning of paediatric patients with lower limb rotational abnormalities in virtual reality

Rotational abnormalities in the lower limbs causing patellar mal-tracking negatively affect patients’ lives, particularly young patients (10–17 years old). Recent studies suggest that rotational abnormalities can increase degenerative effects on the joints of the lower limbs. Rotational abnormalities are diagnosed using 2D CT imaging and X-rays, and these data are then used by surgeons to make decisions during an operation. However, 3D representation of data is preferable in the examination of 3D structures, such as bones. This correlates with added benefits for medical judgement, pre-operative planning, and clinical training. Virtual reality can enable the transformation of standard clinical imaging examination methods (CT/MRI) into immersive examinations and pre-operative planning in 3D. We present a VR system (OrthopedVR) which allows orthopaedic surgeons to examine patients’ specific anatomy of the lower limbs in an immersive three-dimensional environment and to simulate the effect of potential surgical interventions such as corrective osteotomies in VR. In OrthopedVR, surgeons can perform corrective incisions and re-align segments into desired rotational angles. From the system evaluation performed by experienced surgeons we found that OrthopedVR provides a better understanding of lower limb alignment and rotational profiles in comparison with isolated 2D CT scans. In addition, it was demonstrated that using VR software improves pre-operative planning, surgical precision and post-operative outcomes for patients. Our study results indicate that our system can become a stepping stone into simulating corrective surgeries of the lower limbs, and suggest future improvements which will help adopt VR surgical planning into the clinical orthopaedic practice

A Virtual Reality System for the Assessment of Patients with Lower Limb Rotational Abnormalities

Rotational lower limb abnormalities cause patellar mal-tracking which impacts young patients. Repetitive patellar dislocation may require knee arthroplasty. Surgeons employ CT to identify rotational abnormalities and make surgical decisions. Recent studies also demonstrated that immersive 3D visualisation is preferred when examining 3D volumes of patient's data. We describe a prototype VR system that allows orthopaedic surgeons assess patients' lower limb anatomy in an immersive three-dimensional environment and simulate the effects of surgical procedures such as corrective os-teotomies on a standalone VR headset. Preliminary results show an increased understanding of the patient's specific anatomy and predicted surgery outcomes

MedNeRF: Medical Neural Radiance Fields for Reconstructing 3D-aware CT-Projections from a Single X-ray

Computed tomography (CT) is an effective med-ical imaging modality, widely used in the field of clinical medicine for the diagnosis of various pathologies. Advances in Multidetector CT imaging technology have enabled additional functionalities, including generation of thin slice multi planar cross-sectional body imaging and 3D reconstructions. However, this involves patients being exposed to a considerable dose of ionising radiation. Excessive ionising radiation can lead to deterministic and harmful effects on the body. This paper proposes a Deep Learning model that learns to reconstruct CT projections from a few or even a single-view X-ray. This is based on a novel architecture that builds from neural radiance fields, which learns a continuous representation of CT scans by disentangling the shape and volumetric depth of surface and internal anatomical structures from 2D images. Our model is trained on chest and knee datasets, and we demonstrate qual-itative and quantitative high-fidelity renderings and compare our approach to other recent radiance field-based methods. Our code and link to our datasets are available at https://qithub.com/abrilcf/mednerf Clinical relevance- Our model is able to infer the anatomical 3D structure from a few or a single-view X-ray showing future potential for reduced ionising radiation exposure during the imaging process

The London 2012 Summer Olympic Games - an analysis of usage of the Olympic Village ‘Polyclinic’ by competing athletes

AbstractBackground The London 2012 Summer Olympic Games involved 10 568 elite athletes representing 204 competing nations. To manage the varied healthcare needs of this diverse population, a Polyclinic was constructed in the athletes’ village.Aim This work aims to summarise the usage of the Polyclinic by competing athletes and the facilities available to them.Methods All Polyclinic encounters were entered into a database from which data were exported for the time frame 28 July–12 August 2012, inclusive to cover the first to last full day of competition. Only Polyclinic data involving accredited athletes were analysed. All types of encounters were collected for analysis, not just sports-related issues.Results There were a total of 3220 encounters within the Polyclinic. This figure combines medical consultations, radiology/pathology investigations and prescriptions dispensed. Of these 3220 encounters, there were 2105 medical consultations; musculoskeletal comprised the greatest number (52%), followed by dental (30%) and ophthalmic (9%). The most frequently used imaging modality was MRI and diagnostic CT was used the least. After correction for multiple entries, Africa provided the largest proportion of athletes attending the Polyclinic (44%) and Europe the least (9%). Peak usage of all facilities was seen around days 9 and 10 of competition, reflecting the busiest time of the competition and the largest number of athletes in the village.Conclusions The Polyclinic managed a wide variety of both sports-related and non-sports-related injuries and illnesses. The breadth of specialists available for consultation was appropriate as was the ease of access to them. The radiology department was able to satisfy the demand, as were the pharmacy and pathology services. We would recommend a similar structure of facilities and available expertise in one clinic when planning future mass participation sporting events