Supervised learning and dimension reduction techniques for quantification of retinal fluid in optical coherence tomography images.

PurposeThe purpose of the present study is to develop fast automated quantification of retinal fluid in optical coherence tomography (OCT) image sets.MethodsWe developed an image analysis pipeline tailored towards OCT images that consists of five steps for binary retinal fluid segmentation. The method is based on feature extraction, pre-segmention, dimension reduction procedures, and supervised learning tools.ResultsFluid identification using our pipeline was tested on two separate patient groups: one associated to neovascular age-related macular degeneration, the other showing diabetic macular edema. For training and evaluation purposes, retinal fluid was annotated manually in each cross-section by human expert graders of the Vienna Reading Center. Compared with the manual annotations, our pipeline yields good quantification, visually and in numbers.ConclusionsBy demonstrating good automated retinal fluid quantification, our pipeline appears useful to expert graders within their current grading processes. Owing to dimension reduction, the actual learning part is fast and requires only few training samples. Hence, it is well-suited for integration into actual manufacturer's devices, further improving segmentation by its use in daily clinical life.

A view of the current and future role of optical coherence tomography in the management of age-related macular degeneration.

Optical coherence tomography (OCT) has become an established diagnostic technology in the clinical management of age-related macular degeneration (AMD). OCT is being used for primary diagnosis, evaluation of therapeutic efficacy, and long-term monitoring. Computer-based advances in image analysis provide complementary imaging tools such as OCT angiography, further novel automated analysis methods as well as feature detection and prediction of prognosis in disease and therapy by machine learning. In early AMD, pathognomonic features such as drusen, pseudodrusen, and abnormalities of the retinal pigment epithelium (RPE) can be imaged in a qualitative and quantitative way to identify early signs of disease activity and define the risk of progression. In advanced AMD, disease activity can be monitored clearly by qualitative and quantified analyses of fluid pooling, such as intraretinal cystoid fluid, subretinal fluid, and pigment epithelial detachment (PED). Moreover, machine learning methods detect a large spectrum of new biomarkers. Evaluation of treatment efficacy and definition of optimal therapeutic regimens are an important aim in managing neovascular AMD. In atrophic AMD hallmarked by geographic atrophy (GA), advanced spectral domain (SD)-OCT imaging largely replaces conventional fundus autofluorescence (FAF) as it adds insight into the condition of the neurosensory layers and associated alterations at the level of the RPE and choroid. Exploration of imaging features by computerized methods has just begun but has already opened relevant and reliable horizons for the optimal use of OCT imaging for individualized and population-based management of AMD-the leading retinal epidemic of modern times.

Patient-specific computational hemodynamics of intracranial aneurysms from 3D rotational angiography and CT angiography: an in vivo reproducibility study.

BACKGROUND AND PURPOSE: Patient-specific simulations of the hemodynamics in intracranial aneurysms can be constructed by using image-based vascular models and CFD techniques. This work evaluates the impact of the choice of imaging technique on these simulations. MATERIALS AND METHODS: Ten aneurysms, imaged with 3DRA and CTA, were analyzed to assess the reproducibility of geometric and hemodynamic variables across the 2 modalities. RESULTS: Compared with 3DRA models, we found that CTA models often had larger aneurysm necks (P = .05) and that most of the smallest vessels (between 0.7 and 1.0 mm in diameter) could not be reconstructed successfully with CTA. With respect to the values measured in the 3DRA models, the flow rate differed by 14.1 ± 2.8% (mean ± SE) just proximal to the aneurysm and 33.9 ± 7.6% at the aneurysm neck. The mean WSS on the aneurysm differed by 44.2 ± 6.0%. Even when normalized to the parent vessel WSS, a difference of 31.4 ± 9.9% remained, with the normalized WSS in most cases being larger in the CTA model (P = .04). Despite these substantial differences, excellent agreement (κ ≥ 0.9) was found for qualitative variables that describe the flow field, such as the structure of the flow pattern and the flow complexity. CONCLUSIONS: Although relatively large differences were found for all evaluated quantitative hemodynamic variables, the main flow characteristics were reproduced across imaging modalities.

AngioLab: integrated technology for patient-specific management of intracranial aneurysms.

AngioLab is a software tool developed within the GIMIAS framework and is part of a more ambitious pipeline for the integrated management of cerebral aneurysms. AngioLab currently includes three plug-ins: angio segmentation, angio morphology and stenting, as well as supports advanced rendering techniques for the visualization of virtual angiographies. In December 2009, 23 clinicians completed an evaluation questionnaire about AngioLab. This activity was part of a teaching course held during the 2(nd) European Society for Minimally Invasive Neurovascular Treatment (ESMINT) Teaching Course held at the Universitat Pompeu Fabra, Barcelona, Spain. The Automated Morphological Analysis (angio morphology plug-in) and the Endovascular Treatment Planning (stenting plug-in) were evaluated. In general, the results provided by these tools were considered as relevant and as an emerging need in their clinical field.

Toward integrated management of cerebral aneurysms.

In the last few years, some of the visionary concepts behind the virtual physiological human began to be demonstrated on various clinical domains, showing great promise for improving healthcare management. In the current work, we provide an overview of image- and biomechanics-based techniques that, when put together, provide a patient-specific pipeline for the management of intracranial aneurysms. The derivation and subsequent integration of morphological, morphodynamic, haemodynamic and structural analyses allow us to extract patient-specific models and information from which diagnostic and prognostic descriptors can be obtained. Linking such new indices with relevant clinical events should bring new insights into the processes behind aneurysm genesis, growth and rupture. The development of techniques for modelling endovascular devices such as stents and coils allows the evaluation of alternative treatment scenarios before the intervention takes place and could also contribute to the understanding and improved design of more effective devices. A key element to facilitate the clinical take-up of all these developments is their comprehensive validation. Although a number of previously published results have shown the accuracy and robustness of individual components, further efforts should be directed to demonstrate the diagnostic and prognostic efficacy of these advanced tools through large-scale clinical trials.