Automatic deformable registration of histological slides to µCT volume data.

Localizing a histological section in the three-dimensional dataset of a different imaging modality is a challenging 2D-3D registration problem. In the literature, several approaches have been proposed to solve this problem; however, they cannot be considered as fully automatic. Recently, we developed an automatic algorithm that could successfully find the position of a histological section in a micro computed tomography (µCT) volume. For the majority of the datasets, the result of localization corresponded to the manual results. However, for some datasets, the matching µCT slice was off the ground-truth position. Furthermore, elastic distortions, due to histological preparation, could not be accounted for in this framework. In the current study, we introduce two optimization frameworks based on normalized mutual information, which enabled us to accurately register histology slides to volume data. The rigid approach allocated 81 % of histological sections with a median position error of 8.4 µm in jaw bone datasets, and the deformable approach improved registration by 33 µm with respect to the median distance error for four histological slides in the cerebellum dataset.

Accuracy of full guided vs. half-guided implant surgery.

OBJECTIVES: The benefit in terms of higher accuracy for full guided implant surgery (template based guided cavity preparation and guided implant insertion) compared with half-guided surgery (template based guided cavity preparation and free-handed, manual implant insertion) has not been proved till now. MATERIAL AND METHODS: A total of 38 identical implants were inserted into five human cadaver jaws, after virtual implant planning with the coDiagnostiX(™) device. All cavities were drilled using templates equipped with tubes for guidance. At random, 19 implants were inserted in a free handed way (half-guided), whereas 19 implants were inserted in a guided way through the templates tubes (full guided). Postoperative cone beam computer tomographies (CBCT) were performed, and based on image fusion the total deviations between the virtual implant positions at the implants base and tip were determined and compared between both implantation modi. RESULTS: The mean difference in accuracy between both implantation modalities at the implants bases was 0.72 mm (range: 0.16-1.17 mm, SD: 0.45). The mean difference in accuracy between both modalities at the implants tips was 0.46 mm (range: 0.16-1.23 mm. SD: 0.49). Although full guided implantation showed a generally higher accuracy (mean tip: 1.54 mm, range: 0.33-3.64 mm; mean base: 1.52 mm, range: 0.4-3.54 mm) than half-guided implantation (mean tip: 1.84 mm, range: 0.84-3.22 mm; mean base: 1.56 mm, range: 0.49-3.43 mm), the differences were not statistically significant. CONCLUSIONS: The accuracy of half-guided implant surgery is comparable with full guided implant surgery.

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.

Automatic Spinal Cord Gray Matter Quantification: A Novel Approach.

BACKGROUND AND PURPOSE: Currently, accurate and reproducible spinal cord GM segmentation remains challenging and a noninvasive broadly accepted reference standard for spinal cord GM measurements is still a matter of ongoing discussion. Our aim was to assess the reproducibility and accuracy of cervical spinal cord GM and WM cross-sectional area measurements using averaged magnetization inversion recovery acquisitions images and a fully-automatic postprocessing segmentation algorithm. MATERIALS AND METHODS: The cervical spinal cord of 24 healthy subjects (14 women; mean age, 40 ± 11 years) was scanned in a test-retest fashion on a 3T MR imaging system. Twelve axial averaged magnetization inversion recovery acquisitions slices were acquired over a 48-mm cord segment. GM and WM were both manually segmented by 2 experienced readers and compared with an automatic variational segmentation algorithm with a shape prior modified for 3D data with a slice similarity prior. Precision and accuracy of the automatic method were evaluated using coefficients of variation and Dice similarity coefficients. RESULTS: The mean GM area was 17.20 ± 2.28 mm2 and the mean WM area was 72.71 ± 7.55 mm2 using the automatic method. Reproducibility was high for both methods, while being better for the automatic approach (all mean automatic coefficients of variation, ≤4.77%; all differences, P < .001). The accuracy of the automatic method compared with the manual reference standard was excellent (mean Dice similarity coefficients: 0.86 ± 0.04 for GM and 0.90 ± 0.03 for WM). The automatic approach demonstrated similar coefficients of variation between intra- and intersession reproducibility as well as among all acquired spinal cord slices. CONCLUSIONS: Our novel approach including the averaged magnetization inversion recovery acquisitions sequence and a fully-automated postprocessing segmentation algorithm demonstrated an accurate and reproducible spinal cord GM and WM segmentation. This pipeline is promising for both the exploration of longitudinal structural GM changes and application in clinical settings in disorders affecting the spinal cord.