Disambiguating the optic nerve from the surrounding cerebrospinal fluid: Application to MS-related atrophy.

PURPOSE: Our goal is to develop an accurate, automated tool to characterize the optic nerve (ON) and cerebrospinal fluid (CSF) to better understand ON changes in disease. METHODS: Multi-atlas segmentation is used to localize the ON and sheath on T2-weighted MRI (0.6 mm(3) resolution). A sum of Gaussian distributions is fit to coronal slice-wise intensities to extract six descriptive parameters, and a regression forest is used to map the model space to radii. The model is validated for consistency using tenfold cross-validation and for accuracy using a high resolution (0.4 mm(2) reconstructed to 0.15 mm(2)) in vivo sequence. We evaluated this model on 6 controls and 6 patients with multiple sclerosis (MS) and a history of optic neuritis. RESULTS: In simulation, the model was found to have an explanatory R-squared for both ON and sheath radii greater than 0.95. The accuracy of the method was within the measurement error on the highest possible in vivo resolution. Comparing healthy controls and patients with MS, significant structural differences were found near the ON head and the chiasm, and structural trends agreed with the literature. CONCLUSION: This is a first demonstration that the ON can be exclusively, quantitatively measured and separated from the surrounding CSF using MRI.

Self-assessed performance improves statistical fusion of image labels.

PURPOSE: Expert manual labeling is the gold standard for image segmentation, but this process is difficult, time-consuming, and prone to inter-individual differences. While fully automated methods have successfully targeted many anatomies, automated methods have not yet been developed for numerous essential structures (e.g., the internal structure of the spinal cord as seen on magnetic resonance imaging). Collaborative labeling is a new paradigm that offers a robust alternative that may realize both the throughput of automation and the guidance of experts. Yet, distributing manual labeling expertise across individuals and sites introduces potential human factors concerns (e.g., training, software usability) and statistical considerations (e.g., fusion of information, assessment of confidence, bias) that must be further explored. During the labeling process, it is simple to ask raters to self-assess the confidence of their labels, but this is rarely done and has not been previously quantitatively studied. Herein, the authors explore the utility of self-assessment in relation to automated assessment of rater performance in the context of statistical fusion. METHODS: The authors conducted a study of 66 volumes manually labeled by 75 minimally trained human raters recruited from the university undergraduate population. Raters were given 15 min of training during which they were shown examples of correct segmentation, and the online segmentation tool was demonstrated. The volumes were labeled 2D slice-wise, and the slices were unordered. A self-assessed quality metric was produced by raters for each slice by marking a confidence bar superimposed on the slice. Volumes produced by both voting and statistical fusion algorithms were compared against a set of expert segmentations of the same volumes. RESULTS: Labels for 8825 distinct slices were obtained. Simple majority voting resulted in statistically poorer performance than voting weighted by self-assessed performance. Statistical fusion resulted in statistically indistinguishable performance from self-assessed weighted voting. The authors developed a new theoretical basis for using self-assessed performance in the framework of statistical fusion and demonstrated that the combined sources of information (both statistical assessment and self-assessment) yielded statistically significant improvement over the methods considered separately. CONCLUSIONS: The authors present the first systematic characterization of self-assessed performance in manual labeling. The authors demonstrate that self-assessment and statistical fusion yield similar, but complementary, benefits for label fusion. Finally, the authors present a new theoretical basis for combining self-assessments with statistical label fusion.

Groupwise multi-atlas segmentation of the spinal cord's internal structure.

The spinal cord is an essential and vulnerable component of the central nervous system. Differentiating and localizing the spinal cord internal structure (i.e., gray matter vs. white matter) is critical for assessment of therapeutic impacts and determining prognosis of relevant conditions. Fortunately, new magnetic resonance imaging (MRI) sequences enable clinical study of the in vivo spinal cord's internal structure. Yet, low contrast-to-noise ratio, artifacts, and imaging distortions have limited the applicability of tissue segmentation techniques pioneered elsewhere in the central nervous system. Additionally, due to the inter-subject variability exhibited on cervical MRI, typical deformable volumetric registrations perform poorly, limiting the applicability of a typical multi-atlas segmentation framework. Thus, to date, no automated algorithms have been presented for the spinal cord's internal structure. Herein, we present a novel slice-based groupwise registration framework for robustly segmenting cervical spinal cord MRI. Specifically, we provide a method for (1) pre-aligning the slice-based atlases into a groupwise-consistent space, (2) constructing a model of spinal cord variability, (3) projecting the target slice into the low-dimensional space using a model-specific registration cost function, and (4) estimating robust segmentation susing geodesically appropriate atlas information. Moreover, the proposed framework provides a natural mechanism for performing atlas selection and initializing the free model parameters in an informed manner. In a cross-validation experiment using 67 MR volumes of the cervical spinal cord, we demonstrate sub-millimetric accuracy, significant quantitative and qualitative improvement over comparable multi-atlas frameworks, and provide insight into the sensitivity of the associated model parameters.