Procrustes-based geometric morphometrics on MRI images: An example of inter-operator bias in 3D landmarks and its impact on big datasets.

Using 3D anatomical landmarks from adult human head MRIs, we assessed the magnitude of inter-operator differences in Procrustes-based geometric morphometric analyses. An in depth analysis of both absolute and relative error was performed in a subsample of individuals with replicated digitization by three different operators. The effect of inter-operator differences was also explored in a large sample of more than 900 individuals. Although absolute error was not unusual for MRI measurements, including bone landmarks, shape was particularly affected by differences among operators, with up to more than 30% of sample variation accounted for by this type of error. The magnitude of the bias was such that it dominated the main pattern of bone and total (all landmarks included) shape variation, largely surpassing the effect of sex differences between hundreds of men and women. In contrast, however, we found higher reproducibility in soft-tissue nasal landmarks, despite relatively larger errors in estimates of nasal size. Our study exemplifies the assessment of measurement error using geometric morphometrics on landmarks from MRIs and stresses the importance of relating it to total sample variance within the specific methodological framework being used. In summary, precise landmarks may not necessarily imply negligible errors, especially in shape data; indeed, size and shape may be differentially impacted by measurement error and different types of landmarks may have relatively larger or smaller errors. Importantly, and consistently with other recent studies using geometric morphometrics on digital images (which, however, were not specific to MRI data), this study showed that inter-operator biases can be a major source of error in the analysis of large samples, as those that are becoming increasingly common in the 'era of big data'

A level set based framework for quantitative evaluation of breast tissue density from MRI data.

Breast density is a risk factor associated with the development of breast cancer. Usually, breast density is assessed on two dimensional (2D) mammograms using the American College of Radiology (ACR) classification. Magnetic resonance imaging (MRI) is a non-radiation based examination method, which offers a three dimensional (3D) alternative to classical 2D mammograms. We propose a new framework for automated breast density calculation on MRI data. Our framework consists of three steps. First, a recently developed method for simultaneous intensity inhomogeneity correction and breast tissue and parenchyma segmentation is applied. Second, the obtained breast component is extracted, and the breast-air and breast-body boundaries are refined. Finally, the fibroglandular/parenchymal tissue volume is extracted from the breast volume. The framework was tested on 37 randomly selected MR mammographies. All images were acquired on a 1.5T MR scanner using an axial, T1-weighted time-resolved angiography with stochastic trajectories sequence. The results were compared to manually obtained groundtruth. Dice's Similarity Coefficient (DSC) as well as Bland-Altman plots were used as the main tools for evaluation of similarity between automatic and manual segmentations. The average Dice's Similarity Coefficient values were 0.96±0.0172 and 0.83±0.0636 for breast and parenchymal volumes, respectively. Bland-Altman plots showed the mean bias (%) ± standard deviation equal 5.36±3.9 for breast volumes and -6.9±13.14 for parenchyma volumes. The automated framework produced sufficient results and has the potential to be applied for the analysis of breast volume and breast density of numerous data in clinical and research settings

A deep cascaded segmentation of obstructive sleep apnea-relevant organs from sagittal spine MRI

Purpose!#!The main purpose of this work was to develop an efficient approach for segmentation of structures that are relevant for diagnosis and treatment of obstructive sleep apnea syndrome (OSAS), namely pharynx, tongue, and soft palate, from mid-sagittal magnetic resonance imaging (MR) data. This framework will be applied to big data acquired within an on-going epidemiological study from a general population.!##!Methods!#!A deep cascaded framework for subsequent segmentation of pharynx, tongue, and soft palate is presented. The pharyngeal structure was segmented first, since the airway was clearly visible in the T1-weighted sequence. Thereafter, it was used as an anatomical landmark for tongue location. Finally, the soft palate region was extracted using segmented tongue and pharynx structures and used as input for a deep network. In each segmentation step, a UNet-like architecture was applied.!##!Results!#!The result assessment was performed qualitatively by comparing the region boundaries obtained from the expert to the framework results and quantitatively using the standard Dice coefficient metric. Additionally, cross-validation was applied to ensure that the framework performance did not depend on the specific selection of the validation set. The average Dice coefficients on the test set were [Formula: see text], [Formula: see text], and [Formula: see text] for tongue, pharynx, and soft palate tissues, respectively. The results were similar to other approaches and consistent with expert readings.!##!Conclusion!#!Due to high speed and efficiency, the framework will be applied for big epidemiological data with thousands of participants acquired within the Study of Health in Pomerania as well as other epidemiological studies to provide information on the anatomical structures and aspects that constitute important risk factors to the OSAS development

Reproducibility (analysis II) of size and shape in the 20 women sample with three replicas: Procrustes ANOVA comparing individual variation, in centroid size and shape, to measurement error.

<p>Reproducibility (analysis II) of size and shape in the 20 women sample with three replicas: Procrustes ANOVA comparing individual variation, in centroid size and shape, to measurement error.</p

Reproducibility (analysis II) of size and shape in the replica sample: Between operators pairwise correlations of centroid size (Pearson correlation) and shape (correlation of shape Procrustes distance matrices).

<p>Reproducibility (analysis II) of size and shape in the replica sample: Between operators pairwise correlations of centroid size (Pearson correlation) and shape (correlation of shape Procrustes distance matrices).</p

(II) Reproducibility of centroid size visualized using jitter plots for the three sets of landmarks (nose, bone and all landmarks) using estimates from the three operators.

<p>(II) Reproducibility of centroid size visualized using jitter plots for the three sets of landmarks (nose, bone and all landmarks) using estimates from the three operators.</p

(II) Reproducibility of shape: UPGMA phenogram based on Procrustes shape distances for the 20 women (each indentified by a progressive number from 1 to 20) digitized by all three operators: With high reproducibility, all three replicas, or at least two of them, should cluster together 'within' individual (black numbers); when this does not happen, numbers are shown using light grey.

<p>As in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0197675#pone.0197675.g004" target="_blank">Fig 4</a>, nasal data are used as an example.</p

Landmark detection using multi planar reconstruction (MPR) with axial view as the centre of orientation.

<p>Plotted landmarks: 1. Glabella, 2. Soft Nasion, 3. Hard Nasion, 4. Pronasale 5. Subnasale 6. Anterior nasal spine, 7. Sella 8 & 9. Alare 10 & 11. Orbitale 12 & 13 Porion 14 & 15 Zygion.</p