Fully Automatic Method for Reliable Spinal Cord Compartment Segmentation in Multiple Sclerosis.

BACKGROUND AND PURPOSE: Fully automatic quantification methods of spinal cord compartments are needed to study pathologic changes of the spinal cord GM and WM in MS in vivo. We propose a novel method for automatic spinal cord compartment segmentation (SCORE) in patients with MS. MATERIALS AND METHODS: The cervical spinal cords of 24 patients with MS and 24 sex- and age-matched healthy controls were scanned on a 3T MR imaging system, including an averaged magnetization inversion recovery acquisition sequence. Three experienced raters manually segmented the spinal cord GM and WM, anterior and posterior horns, gray commissure, and MS lesions. Subsequently, manual segmentations were used to train neural segmentation networks of spinal cord compartments with multidimensional gated recurrent units in a 3-fold cross-validation fashion. Total intracranial volumes were quantified using FreeSurfer. RESULTS: The intra- and intersession reproducibility of SCORE was high in all spinal cord compartments (eg, mean relative SD of GM and WM: ≤ 3.50% and ≤1.47%, respectively) and was better than manual segmentations (all P < .001). The accuracy of SCORE compared with manual segmentations was excellent, both in healthy controls and in patients with MS (Dice similarity coefficients of GM and WM: ≥ 0.84 and ≥0.92, respectively). Patients with MS had lower total WM areas (P < .05), and total anterior horn areas (P < .01 respectively), as measured with SCORE. CONCLUSIONS: We demonstrate a novel, reliable quantification method for spinal cord tissue segmentation in healthy controls and patients with MS and other neurologic disorders affecting the spinal cord. Patients with MS have reduced areas in specific spinal cord tissue compartments, which may be used as MS biomarkers.

Combining NSAM and CPC concentrations to determine airborne nanoparticle count median diameter: Application to various laboratory and workplace aerosols.

Because nanomaterials have been increasingly developed and used in many technology and industry sectors over the last 20 years, an increasing number of workers is likely to be exposed to airborne nanoparticles. In addition, the question of the nanomaterial characteristics that should be assessed in epidemiological studies remains open. Thus, assessing occupational exposure to airborne nanoparticles will not only rely on mass concentration and chemical composition. Rather, key parameters, such as particle size, have to be included in measurement strategies. We previously proposed a methodology to estimate the Count Median Diameter (CMD) of an aerosol based on the simultaneous size-integrated measurement of two particle concentrations, lung-deposited surface area, and number, thanks to field-portable, commercially available aerosol instruments (Nanoparticle Surface Area Monitor/Condensation Particle Counter combination). In addition to previous work, this study investigates the case of various polydisperse metal oxides, organic oil, and salt particles with CMDs ranging from 16-410 nm. Once corrected, the CMDs derived from the NSAM/CPC agree within ±20% with regard to the reference electrical mobility equivalent diameter, regardless of aerosol composition, morphology, or geometric standard deviation (GSD). Furthermore, the field-applicability of the method was tested through 6 sets of experimental data stemming from workplace measurement campaigns where different materials were produced and handled (TiO2, SiO2, Ag, Multi-Walled Carbon Nanotubes-MWCNT), covering a range of CMDs between 40 and 190 nm. All situations considered, the approach based on the combination of a NSAM and a CPC leads to a satisfying estimation of particle CMD, within ±20% compared to reference CMD.