Quantitative Analysis of Spinal Canal Areas in the Lumbar Spine: An Imaging Informatics and Machine Learning Study.

BACKGROUND AND PURPOSE: Quantitative imaging biomarkers have not been established for the diagnosis of spinal canal stenosis. This work aimed to lay the groundwork to establish such biomarkers by leveraging the developments in machine learning and medical imaging informatics. MATERIALS AND METHODS: Machine learning algorithms were trained to segment lumbar spinal canal areas on axial views and intervertebral discs on sagittal views of lumbar MRIs. These were used to measure spinal canal areas at each lumbar level (L1 through L5). Machine-generated delineations were compared with 2 sets of human-generated delineations to validate the proposed techniques. Then, we use these machine learning methods to delineate and measure lumbar spinal canal areas in a normative cohort and to analyze their variation with respect to age, sex, and height using a variable-intercept mixed model. RESULTS: We established that machine-generated delineations are comparable with human-generated segmentations. Spinal canal areas as measured by machine are statistically significantly correlated with height (P < .05) but not with age or sex. CONCLUSIONS: Our machine learning methodology demonstrates that this important anatomic structure can be accurately detected and quantitatively measured without human input in a manner comparable with that of human raters. Anatomic deviations measured against the normative model established here could be used to flag spinal stenosis in the future.

Phase transition pathways for the production of 100 nm oil-in-water emulsions.

Oil/water emulsions can be produced through phase inversion, by adding water to a reverse water/oil microemulsion. According to small angle neutron scattering experiments and visual observations performed during phase inversion, the stages of this process are as follows: (i) upon water addition, the microemulsion gives way to a highly swollen lamellar phase; (ii) the transient lamellar phase breaks up to yield an array of droplets; (iii) the droplets loses the correlations of the lamellar phase. This emulsion is already present less than one minute after the initial addition of water, and it reaches the final size distribution in one hour. The final population of oil droplets is homogenous with a mean diameter below 100 nm.