Accurate and Robust Alignment of Differently Stained Histologic Images Based on Greedy Diffeomorphic Registration.

Histopathologic assessment routinely provides rich microscopic information about tissue structure and disease process. However, the sections used are very thin, and essentially capture only 2D representations of a certain tissue sample. Accurate and robust alignment of sequentially cut 2D slices should contribute to more comprehensive assessment accounting for surrounding 3D information. Towards this end, we here propose a two-step diffeomorphic registration approach that aligns differently stained histology slides to each other, starting with an initial affine step followed by estimating a deformation field. It was quantitatively evaluated on ample (n = 481) and diverse data from the automatic non-rigid histological image registration challenge, where it was awarded the second rank. The obtained results demonstrate the ability of the proposed approach to robustly (average robustness = 0.9898) and accurately (average relative target registration error = 0.2%) align differently stained histology slices of various anatomical sites while maintaining reasonable computational efficiency (<1 min per registration). The method was developed by adapting a general-purpose registration algorithm designed for 3D radiographic scans and achieved consistently accurate results for aligning high-resolution 2D histologic images. Accurate alignment of histologic images can contribute to a better understanding of the spatial arrangement and growth patterns of cells, vessels, matrix, nerves, and immune cell interactions.

Inter-subject registration and application of the SIGMA rat brain atlas for regional labeling in functional ultrasound imaging.

BACKGROUND: Recent advances using functional ultrasound (fUS) imaging have opened new avenues to evaluate brain activity through the regional monitoring of cerebral blood volume (CBV) dynamics. In particular, this technology paves the way for understanding physiological or pathological cerebral processes or exploring the pharmacological profiles of new drugs targeting brain disorders. One of the main difficulties of this technology is the lack of standardized and validated tools, in particular relevant brain atlases, to help improving the accuracy, automation and reproducibility of fUS data analysis. NEW METHOD: Here, we demonstrate the possibility to use the MRI-validated SIGMA brain atlas in rat to perform fast and precise analysis of CBV changes in numerous functionally relevant regions of interest using fUS imaging. We applied this atlas to a dataset obtained in anesthetized rats evaluating the cerebral effects of atomoxetine, a norepinephrine reuptake inhibitor currently marketed in attention-deficit/hyperactivity-disorder. RESULTS: This approach enabled to show the subregional effects of atomoxetine in the rat with very few inter-individual differences in some areas, such as the dentate gyrus. CONCLUSIONS: We show the feasibility of inter-individual registration of 2D pharmaco-fUS data and subsequent detailed analysis using the SIGMA atlas.

Reproducibility analysis of multi-institutional paired expert annotations and radiomic features of the Ivy Glioblastoma Atlas Project (Ivy GAP) dataset.

PURPOSE: The availability of radiographic magnetic resonance imaging (MRI) scans for the Ivy Glioblastoma Atlas Project (Ivy GAP) has opened up opportunities for development of radiomic markers for prognostic/predictive applications in glioblastoma (GBM). In this work, we address two critical challenges with regard to developing robust radiomic approaches: (a) the lack of availability of reliable segmentation labels for glioblastoma tumor sub-compartments (i.e., enhancing tumor, non-enhancing tumor core, peritumoral edematous/infiltrated tissue) and (b) identifying "reproducible" radiomic features that are robust to segmentation variability across readers/sites. ACQUISITION AND VALIDATION METHODS: From TCIA's Ivy GAP cohort, we obtained a paired set (n = 31) of expert annotations approved by two board-certified neuroradiologists at the Hospital of the University of Pennsylvania (UPenn) and at Case Western Reserve University (CWRU). For these studies, we performed a reproducibility study that assessed the variability in (a) segmentation labels and (b) radiomic features, between these paired annotations. The radiomic variability was assessed on a comprehensive panel of 11 700 radiomic features including intensity, volumetric, morphologic, histogram-based, and textural parameters, extracted for each of the paired sets of annotations. Our results demonstrated (a) a high level of inter-rater agreement (median value of DICE ≥0.8 for all sub-compartments), and (b) ≈24% of the extracted radiomic features being highly correlated (based on Spearman's rank correlation coefficient) to annotation variations. These robust features largely belonged to morphology (describing shape characteristics), intensity (capturing intensity profile statistics), and COLLAGE (capturing heterogeneity in gradient orientations) feature families. DATA FORMAT AND USAGE NOTES: We make publicly available on TCIA's Analysis Results Directory (https://doi.org/10.7937/9j41-7d44), the complete set of (a) multi-institutional expert annotations for the tumor sub-compartments, (b) 11 700 radiomic features, and (c) the associated reproducibility meta-analysis. POTENTIAL APPLICATIONS: The annotations and the associated meta-data for Ivy GAP are released with the purpose of enabling researchers toward developing image-based biomarkers for prognostic/predictive applications in GBM.

Functional ultrasound imaging to study brain dynamics: Application of pharmaco-fUS to atomoxetine.

Functional ultrasound (fUS) is a new tool enabling the imaging of brain activity through the regional monitoring of cerebral blood volume (CBV) dynamics. This innovative technique has not yet demonstrated its full potential in pharmacological applications and drug development. In the current proof-of-concept study, the impact of atomoxetine (ATX), a potent norepinephrine reuptake inhibitor and non-stimulant treatment marketed in attention-deficit/hyperactivity-disorder, was evaluated in anesthetized rat using pharmacological functional ultrasound (pharmaco-fUS) at increasing doses (0.3, 1 and 3 mg/kg). Using regions of interest (acute changes of CBV and functional connectivity) or pixel-based (general linear modeling and independent component analysis) analysis, we here demonstrated that ATX consistently displayed a hemodynamic effect in the visual cortex, the dentate gyrus and thalamus, especially visual areas such as lateral posterior thalamic nuclei and lateral geniculate nuclei (LGN). The time profile of ATX effects was dose-dependent, with fastest CBV increases at the highest dose, and longer CBV increases at the intermediate dose. Standardizing the use of pharmaco-fUS could improve our understanding of the mechanism of action of drugs active in the brain and might constitute a new step to move forward in drug development for neurological disorders.

ANHIR: Automatic Non-Rigid Histological Image Registration Challenge.

Automatic Non-rigid Histological Image Registration (ANHIR) challenge was organized to compare the performance of image registration algorithms on several kinds of microscopy histology images in a fair and independent manner. We have assembled 8 datasets, containing 355 images with 18 different stains, resulting in 481 image pairs to be registered. Registration accuracy was evaluated using manually placed landmarks. In total, 256 teams registered for the challenge, 10 submitted the results, and 6 participated in the workshop. Here, we present the results of 7 well-performing methods from the challenge together with 6 well-known existing methods. The best methods used coarse but robust initial alignment, followed by non-rigid registration, used multiresolution, and were carefully tuned for the data at hand. They outperformed off-the-shelf methods, mostly by being more robust. The best methods could successfully register over 98% of all landmarks and their mean landmark registration accuracy (TRE) was 0.44% of the image diagonal. The challenge remains open to submissions and all images are available for download.