23 research outputs found
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On evaluating brain tissue classifiers without a ground truth
In this paper, we present a set of techniques for the evaluation of brain tissue classifiers on a large data set of MR images of the head. Due to the difficulty of establishing a gold standard for this type of data, we focus our attention on methods which do not require a ground truth, but instead rely on a common agreement principle. Three different techniques are presented: the Williamsâ index, a measure of common agreement; STAPLE, an Expectation Maximization algorithm which simultaneously estimates performance parameters and constructs an estimated reference standard; and Multidimensional Scaling, a visualization technique to explore similarity data. We apply these different evaluation methodologies to a set eleven different segmentation algorithms on forty MR images. We then validate our evaluation pipeline by building a ground truth based on human expert tracings. The evaluations with and without a ground truth are compared. Our findings show that comparing classifiers without a gold standard can provide a lot of interesting information. In particular, outliers can be easily detected, strongly consistent or highly variable techniques can be readily discriminated, and the overall similarity between different techniques can be assessed. On the other hand, we also find that some information present in the expert segmentations is not captured by the automatic classifiers, suggesting that common agreement alone may not be sufficient for a precise performance evaluation of brain tissue classifiers
On the evaluation of methods for the recovery of plant root systems from X-ray computed tomography images
X-ray micro computed tomography (”CT) allows non-destructive visualisation of plant root systems within their soil environment and thus offers an alternative to commonly used destructive methodologies for the examination of plant roots and their interaction with the surrounding soil. Various methods for the recovery of root system information from X-ray CT image data have been presented in the literature. Detailed, ideally quantitative, evaluation is essential, in order to determine the accuracy and limitations of the proposed methods, and to allow potential users to make informed choices between them. This, however, is a complicated task. Three-dimensional ground truth data is expensive to produce, and the complexity of X-ray CT data means that manually generated ground truth may not be definitive. Similarly, artificially generated data is not entirely representative of real samples. The aims of this work are to raise awareness of the evaluation problem and to propose experimental approaches that allow the performance of root extraction methods to be assessed, ultimately improving the techniques available. To illustrate the issues, tests are conducted using both artificially generated images and real data samples
Automatic segmentation of the Foveal Avascular Zone in ophthalmological OCT-A images
Angiography by Optical Coherence Tomography is a non-invasive retinal imaging
modality of recent appearance that allows the visualization of the vascular
structure at predefined depths based on the detection of the blood movement.
OCT-A images constitute a suitable scenario to analyse the retinal vascular
properties of regions of interest, measuring the characteristics of the foveal
vascular and avascular zones. Extracted parameters of this region can be used
as prognostic factors that determine if the patient suffers from certain
pathologies, indicating the associated pathological degree. The manual
extraction of these biomedical parameters is a long, tedious and subjective
process, introducing a significant intra and inter-expert variability, which
penalizes the utility of the measurements. In addition, the absence of tools
that automatically facilitate these calculations encourages the creation of
computer-aided diagnosis frameworks that ease the doctor's work, increasing
their productivity and making viable the use of this type of vascular
biomarkers.
We propose a fully automatic system that identifies and precisely segments
the region of the foveal avascular zone (FAZ) using a novel ophthalmological
image modality as is OCT-A. The system combines different image processing
techniques to firstly identify the region where the FAZ is contained and,
secondly, proceed with the extraction of its precise contour. The system was
validated using a representative set of 168 OCT-A images, providing accurate
results with the best correlation with the manual measurements of two experts
clinician of 0.93 as well as a Jaccard's index of 0.82 of the best experimental
case. This tool provides an accurate FAZ measurement with the desired
objectivity and reproducibility, being very useful for the analysis of relevant
vascular diseases through the study of the retinal microcirculation
An open, multi-vendor, multi-field-strength brain MR dataset and analysis of publicly available skull stripping methods agreement
This paper presents an open, multi-vendor, multi-field strength magnetic resonance (MR) T1-weighted volumetric brain imaging dataset, named Calgary-Campinas-359 (CC-359). The dataset is composed of images of older healthy adults (29-80 years) acquired on scanners from three vendors (Siemens, Philips and General Electric) at both 1.5 T and 3 T. CC-359 is comprised of 359 datasets, approximately 60 subjects per vendor and magnetic field strength. The dataset is approximately age and gender balanced, subject to the constraints of the available images. It provides consensus brain extraction masks for all volumes generated using supervised classification. Manual segmentation results for twelve randomly selected subjects performed by an expert are also provided. The CC-359 dataset allows investigation of 1) the influences of both vendor and magnetic field strength on quantitative analysis of brain MR; 2) parameter optimization for automatic segmentation methods; and potentially 3) machine learning classifiers with big data, specifically those based on deep learning methods, as these approaches require a large amount of data. To illustrate the utility of this dataset, we compared to the results of a supervised classifier, the results of eight publicly available skull stripping methods and one publicly available consensus algorithm. A linear mixed effects model analysis indicated that vendor (p - value < 0.001) and magnetic field strength (p - value < 0.001) have statistically significant impacts on skull stripping results170482494CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTĂFICO E TECNOLĂGICO - CNPQCOORDENAĂĂO DE APERFEIĂOAMENTO DE PESSOAL DE NĂVEL SUPERIOR - CAPESFUNDAĂĂO DE AMPARO Ă PESQUISA DO ESTADO DE SĂO PAULO - FAPESP311228/2014-3; 157534/2015-488881.062158/2014-012013/07559-3; 2013/23514-0; 2016/18332-
Atlas-Guided Segmentation of Vervet Monkey Brain MRI
The vervet monkey is an important nonhuman primate model that allows the study of isolated environmental factors in a controlled environment. Analysis of monkey MRI often suffers from lower quality images compared with human MRI because clinical equipment is typically used to image the smaller monkey brain and higher spatial resolution is required. This, together with the anatomical differences of the monkey brains, complicates the use of neuroimage analysis pipelines tuned for human MRI analysis. In this paper we developed an open source image analysis framework based on the tools available within the 3D Slicer software to support a biological study that investigates the effect of chronic ethanol exposure on brain morphometry in a longitudinally followed population of male vervets. We first developed a computerized atlas of vervet monkey brain MRI, which was used to encode the typical appearance of the individual brain structures in MRI and their spatial distribution. The atlas was then used as a spatial prior during automatic segmentation to process two longitudinal scans per subject. Our evaluation confirms the consistency and reliability of the automatic segmentation. The comparison of atlas construction strategies reveals that the use of a population-specific atlas leads to improved accuracy of the segmentation for subcortical brain structures. The contribution of this work is twofold. First, we describe an image processing workflow specifically tuned towards the analysis of vervet MRI that consists solely of the open source software tools. Second, we develop a digital atlas of vervet monkey brain MRIs to enable similar studies that rely on the vervet model