Automatic Construction of Immobilisation Masks for use in Radiotherapy Treatment of Head-and-Neck Cancer

Current clinical practice for immobilisation for patients undergoing brain or head and neck radiotherapy is normally achieved using Perspex or thermoplastic shells that are moulded to patient anatomy during a visit to the mould room. The shells are “made to measure” and the methods currently employed to make them require patients to visit the mould room. The mould room visit can be depressing and some patients find this process particularly unpleasant. In some cases, as treatment progresses, the tumour may shrink and therefore there may be a need for a further mould room visits. With modern manufacturing and rapid prototyping comes the possibility of determining the shape of the shells from the CT-scan of the patient directly, alleviating the need for making physical moulds from the patients’ head. However, extracting such a surface model remains a challenge and is the focus of this thesis. The aim of the work in this thesis is to develop an automatic pipeline capable of creating physical models of immobilisation shells directly from CT scans. The work includes an investigation of a number of image segmentation techniques to segment the skin/air interface from CT images. To enable the developed pipeline to be quantitatively evaluated we compared the 3D model generated from the CT data to ground truth obtained by 3D laser scans of masks produced by the mould room in the frame of a clinical trial. This involved automatically removing image artefacts due to fixations from CT imagery, automatic alignment (registration) between two meshes, measuring the degree of similarity between two 3D volumes, and automatic approach to evaluate the accuracy of segmentation. This thesis has raised and addressed many challenges within this pipeline. We have examined and evaluated each stage of the pipeline separately. The outcomes of the pipeline as a whole are currently being evaluated by a clinical trial (IRAS ID:209119, REC Ref.:16/YH/0485). Early results from the trial indicate that the approach is viable

Automatic Removal of Mechanical Fixations from CT Imagery with Particle Swarm Optimisation

Fixation devices are used in radiotherapy treatment of head and neck cancers to ensure successive treatment fractions are accurately targeted. Typical fixations usually take the form of a custom made mask that is clamped to the treatment couch and these are evident in many CT data sets as radiotherapy treatment is normally planned with the mask in place. But the fixations can make planning more difficult for certain tumor sites and are often unwanted by third parties wishing to reuse the data. Manually editing the CT images to remove the fixations is time consuming and error prone. This paper presents a fast and automatic approach that removes artifacts due to fixations in CT images without affecting pixel values representing tissue. The algorithm uses particle swarm optimisation to speed up the execution time and presents results from five CT data sets that show it achieves an average specificity of 92.01% and sensitivity of 99.39%

A fast and automatic approach for removing artefacts due to immobilisation masks in X-ray CT

Immobilisation masks are fixation devices that are used when administering radiotherapy treatment to patients with tumours affecting the head and neck. Radiotherapy planning X-ray Computer Tomography (CT) data sets for these patients are captured with the immobilisation mask fitted and manually editing the X-ray CT images to remove artefacts due to the mask is time consuming and error prone. This paper represents the first study that employs a fast and automatic approach to remove image artefacts due to masks in X-ray CT images without affecting pixel values representing tissue. Our algorithm uses a fractional order Darwinian particle swarm optimisation of Otsu’s method combined with morphological post-processing to classify pixels belonging to the mask. The proposed approach is tested on five X-ray CT data sets and achieves an average specificity of 92.01% and sensitivity of 99.39%. We also present results demonstrating the comparative speed-up obtained by fractional order Darwinian particle swarm optimisation

Evaluation of 3D Printed Immobilisation Shells for Head and Neck IMRT

This paper presents the preclinical evaluation of a novel immobilization system for patients undergoing external beam radiation treatment of head and neck tumors. An immobilization mask is manufactured directly from a 3-D model, built using the CT data routinely acquired for treatment planning so there is no need to take plaster of Paris moulds. Research suggests that many patients find the mould room visit distressing and so rapid prototyping could potentially improve the overall patient experience. Evaluation of a computer model of the immobilization system using an anthropomorphic phantom shows that >99% of vertices are within a tolerance of ±0.2 mm. Hausdorff distance was used to analyze CT slices obtained by rescanning the phantom with a printed mask in position. These results show that for >80% of the slices the median “worse-case” tolerance is approximately 4 mm. These measurements suggest that printed masks can achieve similar levels of immobilization to those of systems currently in clinical use

Pre-operative Over-investigation of Routine Tests Prior to Elective Surgeries

Background: Previous studies stressed on the burden raised by routine pre-operative test ordering, which should be based on the assessment of patient physical status. In a tertiary hospital in Jordan, we aim to study the compliance with guidelines regarding pre-operative routine testing prior to an elective surgery, cholecystectomy, and calculate the estimated cost from non-compliance with the guidelines.Methods: We included laparoscopic cholecystectomy (through ICD-9 code) to represent an elective surgery. For each surgery done from the period 1/1/2016 to 31/12/2016, data regarding preoperative investigations, admission history note, operative and discharge note were obtained. Tests that are considered routine investigations are Complete blood count (CBC), kidney function tests (KFT), electrolytes, chest X-ray, electrocardiogram, coagulation studies, and urine-analysis. We classified patients who underwent cholecystectomy according to the latest version of the American Society of Anesthesiologists (ASA) physical status system to assess the need for routine tests, then we calculated the number and cost of excess tests.Results: A total 382 routine, non-emergent laparoscopic cholecystectomy surgeries were performed. 319 (83.5%) of patients were classified as ASA-1, 60 (15.7%) were classified as ASA-2, and only 3 (0.8%) were classified as ASA-3. Age was a significant determinant in obtaining chest X-ray ordering and findings (p< 0.001) and electrolytes ordering and findings (p= 0.001). Total routine tests cost for elective cholecystectomy during 2016 was 16,021$. Regarding operative compilations, only 14 (3.7%) complication occurred, all of which were bleeding related.Conclusion: Oversighting routine preoperative test ordering before elective cholecystectomy will significantly reduce the cost without increasing post-operative complications

Analysis of clinical records of dental patients attending Jordan University Hospital: Documentation of drug prescriptions and local anesthetic injections

Najla Dar-Odeh1, Soukaina Ryalat1, Mohammad Shayyab1, Osama Abu-Hammad21Department of Oral and Maxillofacial Surgery Oral Medicine and Periodontics, Faculty of Dentistry, University of Jordan, Jordan; 2Department of Prosthetic Dentistry, Faculty of Dentistry, University of Jordan, JordanObjectives: The aim of this study was to analyze clinical records of dental patients attending the Dental Department at the University of Jordan Hospital: a teaching hospital in Jordan. Analysis aimed at determining whether dental specialists properly documented the drug prescriptions and local anesthetic injections given to their patients.Methods: Dental records of the Dental Department at the Jordan University Hospital were reviewed during the period from April 3rd until April 26th 2007 along with the issued prescriptions during that period.Results: A total of 1000 records were reviewed with a total of 53 prescriptions issued during that period. Thirty records documented the prescription by stating the category of the prescribed drug. Only 13 records stated the generic or the trade names of the prescribed drugs. Of these, 5 records contained the full elements of a prescription. As for local anesthetic injections, the term &amp;ldquo;LA used&amp;rdquo; was found in 22 records while the names and quantities of the local anesthetics used were documented in only 13 records. Only 5 records documented the full elements of a local anesthetic injection.Conclusion: The essential data of drug prescriptions and local anesthetic injections were poorly documented by the investigated group of dental specialists. It is recommended that the administration of the hospital and the dental department implement clear and firm guidelines for dental practitioners in particular to do the required documentation procedure.Keywords: dental records, documentation, prescriptions, local anesthesi

A review of UAV Visual Detection and Tracking Methods

This paper presents a review of techniques used for the detection and tracking of UAVs or drones. There are different techniques that depend on collecting measurements of the position, velocity, and image of the UAV and then using them in detection and tracking. Hybrid detection techniques are also presented. The paper is a quick reference for a wide spectrum of methods that are used in the drone detection process.Comment: 10 page

A CNN-BiLSTM Deep Learning Model for Automatic Scoring of EEG Signals

Recently, several automatic sleep stage classification methods have been proposed based on deep learning using convolutional (CNN) and recurrent (RNN) neural networks. However, the state of the art CNN methods are still complex which usually require significant time and considerable computational resources in order to set up and sufficiently train a deep CNN from scratch. This study eliminates the need to establish and train a deep CNN from scratch by leveraging a pre-trained deep architecture that has been previously trained from sufficient labeled data in a different context. A convolutional neural network (CNN) and a Bidrectional long short term memory network (BiLSTM) are integrated for automatic feature extraction and sleep stage scoring using only a singlechannel EEG signal. To demonstrate the generalizability of our results, the proposed model was evaluated using PSG records of 81 patients that were collected in different environments, through different recording hardware, and annotated with different sleep experts. The use of a single EEG source and a one-to-one classification scheme in the proposed model can allow further development towards wearable systems and online in home monitoring applications