Automatic Optic Nerve Measurement: A New Tool to Standardize Optic Nerve Assessment in Ultrasound B-Mode Images

Transorbital sonography provides reliable information about the estimation of intra-cranial pressure by measuring the optic nerve sheath diameter (ONSD), whereas the optic nerve (ON) diameter (OND) may reveal ON atrophy in patients with multiple sclerosis. Here, an AUTomatic Optic Nerve MeAsurement (AUTONoMA) system for OND and ONSD assessment in ultrasound B-mode images based on deformable models is presented. The automated measurements were compared with manual ones obtained by two operators, with no significant differences. AUTONoMA correctly segmented the ON and its sheath in 71 out of 75 images. The mean error compared with the expert operator was 0.06 ± 0.52 mm and 0.06 ± 0.35 mm for the ONSD and OND, respectively. The agreement between operators and AUTONoMA was good and a positive correlation was found between the readers and the algorithm with errors comparable with the inter-operator variability. The AUTONoMA system may allow for standardization of OND and ONSD measurements, reducing manual evaluation variability

Automatic segmentation of the optic nerve in transorbital ultrasound images using a deep learning approach

Transorbital sonography is able to provide reliable information about (a) intra-cranial pressure estimation through the optic nerve sheath diameter (ONSD) measurement, and (b) optic nerve atrophy in patients with multiple sclerosis through the optic nerve diameter (OND). In this study, we present the first method for the automatic measurement of the OND and ONSD using a deep learning technique (UNet with ResNet50 encoder) for the optic nerve segmentation. The dataset included 201 images from 50 patients. The automated measurements were compared with manual ones obtained by one operator. The mean error was equal to 0.07 ± 0.34 mm and -0.07 ± 0.67 mm, for the OND and ONSD, respectively. The developed system should aid in standardizing OND and ONSD measurements and reduce manual evaluation variability

What volume increase is needed for the management of raised intracranial pressure in children with craniosynostosis?

Craniosynostosis describes a fusion of one or more sutures in the skull. It can occur in isolation or as part of a syndrome. In either setting, it is a condition which may lead to raised intracranial pressure. The exact cause of raised intracranial pressure in craniosynostosis is unknown. It may be due to; a volume mismatch between the intracranial contents and their containing cavity, venous hypertension, hydrocephalus or airway obstruction, which is often a sequela of an associated syndrome. At Great Ormond Street Hospital, after hydrocephalus and airway obstruction have been treated, the next surgical treatment of choice is cranial vault expansion. This expansion has been shown to reduce intracranial pressure, interestingly despite its success, the reasons behind its benefits are not fully understood. Using reconstructed 3-dimensional imaging, accurate measurement of cranial volumes can now be achieved. The aim of this project is to use the advances in 3-dimensional imaging and image processing to provide novel information on the volume changes that occur following cranial vault expansion. This information will be combined with clinical metrics to create a greater understanding of the causes of raised intracranial pressure in craniosynostosis, why cranial vault expansion treats them and whether there is an optimal volume expansion

Segmentation in the Canine Head

The abstract of this item is unavailable due to an embargo

Head to toe ultrasound: a narrative review of experts’ recommendations of methodological approaches

Critical care ultrasonography (US) is widely used by intensivists managing critically ill patients to accurately and rapidly assess different clinical scenarios, which include pneumothorax, pleural effusion, pulmonary edema, hydronephrosis, hemoperitoneum, and deep vein thrombosis. Basic and advanced critical care ultrasonographic skills are routinely used to supplement physical examination of critically ill patients, to determine the etiology of critical illness and to guide subsequent therapy. European guidelines now recommend the use of US for a number of practical procedures commonly performed in critical care. Full training and competence acquisition are essential before significant therapeutic decisions are made based on the US assessment. However, there are no universally accepted learning pathways and methodological standards for the acquisition of these skills. Therefore, in this review, we aim to provide a methodological approach of the head to toe ultrasonographic evaluation of critically ill patients considering different districts and clinical applications

Computer Vision Based Early Intraocular Pressure Assessment From Frontal Eye Images

Intraocular Pressure (IOP) in general, refers to the pressure in the eyes. Gradual increase of IOP and high IOP are conditions or symptoms that may lead to certain diseases such as glaucoma, and therefore, must be closely monitored. While the pressure in the eye increases, different parts of the eye may become affected until the eye parts are damaged. An effective way to prevent rise in eye pressure is by early detection. Exiting IOP monitoring tools include eye tests at clinical facilities and computer-aided techniques from fundus and optic nerves images. In this work, a new computer vision-based smart healthcare framework is presented to evaluate the intraocular pressure risk from frontal eye images early-on. The framework determines the status of IOP by analyzing frontal eye images using image processing and machine learning techniques. A database of images from the Princess Basma Hospital was used in this work. The database contains 400 eye images; 200 images with normal IOP and 200 high eye pressure case images. This study proposes novel features for IOP determination from two experiments. The first experiment extracts the sclera using circular hough transform, after which four features are extracted from the whole sclera. These features are mean redness level, red area percentage, contour area and contour height. The pupil/iris diameter ratio feature is also extracted from the frontal eye image after a series of pre-processing techniques. The second experiment extracts the sclera and iris segment using a fully conventional neural network technique, after which six features are extracted from only part of the segmented sclera and iris. The features include mean redness level, red area percentage, contour area, contour distance and contour angle along with the pupil/iris diameter ratio. Once the features are extracted, classification techniques are applied in order to train and test the images and features to obtain the status of the patients in terms of eye pressure. For the first experiment, neural network and support vector machine algorithms were adopted in order to detect the status of intraocular pressure. The second experiment adopted support vector machine and decision tree algorithms to detect the status of intraocular pressure. For both experiments, the framework detects the status of IOP (normal or high IOP) with high accuracies. This computer vison-based approach produces evidence of the relationship between the extracted frontal eye image features and IOP, which has not been previously investigated through automated image processing and machine learning techniques from frontal eye images

On Nature of the Gradient Echo MR Signal and Its Application to Monitoring Multiple Sclerosis

Multiple Sclerosis is a common disease, affecting 2.5 million people world-wide. The clinical course is heterogeneous, ranging from benign disease in which patients live an almost normal life to severe and devastating disease that may shorten life. Despite much research, a fully effective treatment for MS is still unavailable and diagnostic techniques for monitoring MS disease evolution are much needed. As a non-invasive tool, Magnetic resonance imaging: MRI) plays a key role in MS diagnosis. Numerous MRI techniques have been proposed over the years. Among most widely used are conventional T1-weighted: T1W), T2-weighted: T2W) and FLuid Attenuated Inversion Recovery: FLAIR) imaging techniques. However their results do not correlate well with neurological findings. Several advanced MRI techniques are also used as research tools to study MS. Among them are magnetization transfer contrast imaging: MT), MR spectroscopy: MRS), and Diffusion Tensor Imaging: DTI) but they have not penetrated to clinical arena yet. Gradient Echo Plural Contrast Imaging: GEPCI) developed in our laboratory is a post processing technique based on multi-echo gradient echo sequence. It offers basic contrasts such as T1W images and T2* maps obtained from magnitude of GEPCI signal, and frequency maps obtained from GEPCI signal phase. Phase information of Gradient Echo MR signal has recently attracted much attention of the MR community since it manifests superior gray matter/ white matter contrast and sub-cortical contrast, especially at high field: 7 T) MRI. However the nature of this contrast is under intense debates. Our group proposed a theoretical framework - Generalized Lorentzian Approach - which emphasizes that, contrary to a common-sense intuition, phase contrast in brain tissue is not directly proportional to the tissue bulk magnetic susceptibility but is rather determined by the geometrical arrangement of brain tissue components: lipids, proteins, iron, etc.) at the cellular and sub-cellular levels - brain tissue magnetic architecture . In this thesis we have provide first direct prove of this hypothesis by measurement of phase contrast in isolated optic nerve. We have also provided first quantitative measurements of the contribution to phase contrast from the water-macromolecule exchange effect. Based on our measurement in protein solutions, we demonstrated that the magnitude of exchange effect is 1/2 of susceptibility effect and to the opposite sign. GEPCI technique also offers a scoring method for monitoring Multiple Sclerosis based on the quantitative T2* maps generated from magnitude information of gradient echo signal. Herein we demonstrated a strong agreement between GEPCI quantitative scores and traditional lesion load assessment. We also established a correlation between GEPCI scores and clinical tests for MS patients. We showed that this correlation is stronger than that found between traditional lesion load and clinical tests. Such studies will be carried out for longer period and on MS subjects with broader range of disease severity in the future. We have also demonstrated that the magnitude and phase information available from GEPCI experiment can be combined in multiple ways to generate novel contrasts that can help with visualization of neurological brain abnormalities beyond Multiple Sclerosis. In summary, in this study, we 1) propose novel contrasts for GEPCI from its basic images; 2) investigate the biophysical mechanisms behind phase contrast; 3) evaluate the benefits of quantitative T2* map offered by GEPCI in monitoring disease of Multiple Sclerosis by comparing GEPCI results to clinical standard techniques; 4) apply our theoretical framework - Generalized Lorentzian Approach - to better understand phase contrast in MS lesions