OCT Segmentation Errors with Bruch's Membrane Opening-Minimum Rim Width as Compared with Retinal Nerve Fiber Layer Thickness.

OBJECTIVE: To compare the magnitude and location of automated segmentation errors of the Bruch's membrane opening-minimum rim width (BMO-MRW) and retinal nerve fiber layer thickness (RNFLT). DESIGN: Cross-sectional study. PARTICIPANTS: We included 162 glaucoma suspect or open-angle glaucoma eyes from 162 participants. METHODS: We used spectral-domain optic coherence tomography (Spectralis 870 nm, Heidelberg Engineering) to image the optic nerve with 24 radial optic nerve head B-scans and a 12-degree peripapillary circle scan, and exported the native "automated segmentation only" results for BMO-MRW and RNFLT. We also exported the results after "manual refinement" of the measurements. MAIN OUTCOME MEASURES: We calculated the absolute and proportional error globally and within the 12 30-degree sectors of the optic disc. We determined whether the glaucoma classifications were different between BMO-MRW and RNFLT as a result of manual and automatic segmentation. RESULTS: The absolute error mean was larger for BMO-MRW than for RNFLT (10.8 µm vs. 3.58 µm, P < 0.001). However, the proportional errors were similar (4.3% vs. 4.4%, P = 0.47). In a multivariable regression model, errors in BMO-MRW were not significantly associated with age, location, magnitude, or severity of glaucoma loss (all P ≥ 0.05). However, larger RNFLT errors were associated with the superior and inferior sector location, thicker nerve fiber layer, and worse visual field (all P < 0.05). Errors in BMO-MRW and RNFLT were not likely to occur in the same sector location (R2 = 0.001; P = 0.15). With manual refinement, the glaucoma classification changed in 7.8% and 6.2% of eyes with BMO-MRW and RNFLT, respectively. CONCLUSIONS: Both BMO-MRW and RNFLT measurements included segmentation errors, which did not seem to have a common location, and may result in differences in glaucoma classification. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Customized Slab-Segmentation Method for Projection-Artifact Elimination in Best Vitelliform Macular Dystrophy: A Swept-Source Optical Coherence Tomography Angiography Study.

PURPOSE: To assess the efficacy of customized slab segmentation in eliminating projection artifacts in swept-source optical coherence tomography angiography (SS-OCTA) images of Best vitelliform macular dystrophy (BVMD). METHODS: Prospective case series including different stages of BVMD. We analyzed SS-OCTA images for flow signals in the outer retina and coregistered B-scan images for distortion of the segmentation slabs defining the outer retina. We applied a customized method for slab realignment whenever BVMD lesions produced distortion of the slabs. Afterward, we checked the images to determine whether the previously noted flow signal had persisted or disappeared, described as "true flow" or "pseudoflow", respectively. Categorical variables were analyzed with X2 or Fisher's exact tests, while quantitative variables were analyzed with independent t-test at p<0.05. RESULTS: The study included 39 eyes of 22 patients. We detected BVMD patterns I (dome-shaped hyperreflective lesion without neurosensory retinal detachment), II (knob-like hyperreflective lesion with localized neurosensory retinal detachment), and III (heterogeneous scattered hyperreflective material) in 49%, 23%, and 28% of eyes, respectively. Pseudoflow was evident mostly in eyes with pattern II lesions, presence of flow signal within BVMD lesions, and lesions whose height represented >80% of the retinal thickness (p<0.001). CONCLUSION: Customized slab segmentation is effective in eliminating projection artifact in SS-OCTA images of BVMD. SUMMARY: Projection artifact is a significant confounding factor in emerging SS-OCTA technology through production of pseudoflow signals that can lead to misinterpretation of images of BVMD lesions. The present study proposes a customized method for correction of segmentation errors to eliminate projection artifacts in SS-OCTA images of BVMD patients.

Developmental anomalies and South American paleopathology: A comparison of block vertebrae and co-occurring axial anomalies among three skeletal samples from the El Brujo archaeological complex of northern coastal Peru.

Though developmental anomalies have been noted for over a century in South American paleopathology, they have received less attention than other pathological conditions. When anomalies are reported, they tend to be unusual case studies or incidental findings. Paleopathological research should also consider anomalies from a population perspective, to broaden our understanding about the frequency of specific anomalies, and potentially offer insight into genetic relationships, cultural behavior, or environmental factors. This investigation compared block vertebrae and co-occurring postcranial axial anomalies among three skeletal samples reflecting an occupational sequence at the El Brujo Archaeological Complex of Peru. Block vertebrae demonstrate both considerable antiquity and persistence through time, though frequencies, vertebral level, and co-occurring anomalies varied by sex and cultural period. Among the Late Preceramic sample, the frequency of C2-C3 block vertebrae was highest and only seen among females, which may suggest familial influence, genetic isolation, and potentially matrilocality. The Moche sample demonstrated a moderate frequency of blocks, which in combination with the frequencies of other developmental anomalies, may suggest population continuity paired with an influx of new genes, demographic expansion, and possible cultural change with regard to postmarital residence. The Lambayeque sample demonstrated the lowest frequency of blocks and the highest frequency and greatest diversity of anomalies, which may suggest genetic continuity from the Moche, an expanded gene pool, broader opportunities for mate choice, and cultural change. It is hoped that this investigation will provide data for future comparisons and call attention to the need for the broader study of developmental anomalies in South America.

A multi-time-point modality-agnostic patch-based method for lesion filling in multiple sclerosis.

Multiple sclerosis lesions influence the process of image analysis, leading to tissue segmentation problems and biased morphometric estimates. Existing techniques try to reduce this bias by filling all lesions as normal-appearing white matter on T1-weighted images, considering each time-point separately. However, due to lesion segmentation errors and the presence of structures adjacent to the lesions, such as the ventricles and deep grey matter nuclei, filling all lesions with white matter-like intensities introduces errors and artefacts. In this paper, we present a novel lesion filling strategy inspired by in-painting techniques used in computer graphics applications for image completion. The proposed technique uses a five-dimensional (5D), patch-based (multi-modality and multi-time-point), Non-Local Means algorithm that fills lesions with the most plausible texture. We demonstrate that this strategy introduces less bias, fewer artefacts and spurious edges than the current, publicly available techniques. The proposed method is modality-agnostic and can be applied to multiple time-points simultaneously. In addition, it preserves anatomical structures and signal-to-noise characteristics even when the lesions are neighbouring grey matter or cerebrospinal fluid, and avoids excess of blurring or rasterisation due to the choice of the segmentation plane, shape of the lesions, and their size and/or location.

A geometric method for the detection and correction of segmentation leaks of anatomical structures in volumetric medical images.

PURPOSE: Patient-specific models of anatomical structures and pathologies generated from volumetric medical images play an increasingly central role in many aspects of patient care. A key task in generating these models is the segmentation of anatomical structures and pathologies of interest. Although numerous segmentation methods are available, they often produce erroneous delineations that require time-consuming modifications. METHODS: We present a new geometry-based algorithm for the reliable detection and correction of segmentation errors in volumetric medical images. The method is applicable to anatomical structures consisting of a few 3D star-shaped components. First, it detects segmentation errors by casting rays from the initial segmentation interior to its outer surface. It then classifies the segmentation surface into correct and erroneous regions by minimizing an energy functional that incorporates first- and second-order properties of the rays lengths. Finally, it corrects the segmentation errors by computing new locations for the erroneous surface points by Laplace deformation so that the new surface has maximum smoothness with respect to the rays-length gradient magnitude. RESULTS: Our evaluation on initial segmentations of 16 abdominal aortic aneurysm and 12 lung tumors in CT scans obtained by both adaptive region-growing and active contours level-set segmentation improved the volumetric overlap error by 66 and 70.5% respectively, with respect to the ground-truth. CONCLUSIONS: The advantages of our method are that it is independent of the initial segmentation algorithm that covers a variety of anatomical structures and pathologies, that it does not require a shape prior, and that it requires minimal user interaction.

Frequency and Causes of Segmentation Errors in Spectral Domain Optical Coherence Tomography Imaging in Glaucoma

PURPOSE: To determine the frequency and potential causes of segmentation errors in spectral domain optical coherence tomography (SD-OCT) imaging of retinal nerve fiber layer (RNFL) scans. METHODS: Segmentation errors for the RNFL thickness analysis were recorded during a retrospective chart review of 214 eye scans from 132 consecutive patients with glaucoma or glaucoma suspect who underwent a complete eye exam using Spectralis™ OCT scanning from August 2014 to November 2014. Segmentation errors were classified as inner, outer, inner and outer segmentation errors, and degraded images. The risk factors including age, sex, intraocular pressure, spherical equivalents, severity of glaucoma, and associated ocular disorders were evaluated using logistic regression analysis. RESULTS: A total of 71 eye scans included segmentation errors. Risk factors of inner segmentation error (8.9%) were age, epiretinal membrane, and degenerative myopia. Risk factors of outer segmentation error (29.9%) were age, peripapillary atrophy, posterior vitreous detachment, and severity of glaucoma. Risk factors of inner and outer segmentation errors (6.1%) were age and degenerative myopia. The single risk factor of degraded image (2.3%) was degenerative myopia. CONCLUSIONS: Segmentation errors for SD-OCT RNFL scans in glaucoma patients are common. Clinicians should carefully review the scans for segmentation errors when using SD-OCT images in glaucoma diagnosis or during patient follow-up.