A novel technique for olfactory bulb measurements

Lateralisation of nasal cycle is not reflected in the olfactory bulb volumes and cerebral activations

Nasal cycle (NC) is a rhythmic change of lateralised nasal airflow mediated by the autonomous nervous system. Previous studies reported the dependence of NC dominance or more patent side on handedness and hemispheric cerebral activity. We aimed to investigate firstly the possible lateralised effect of NC on olfactory bulb volume and secondly the association of NC with the lateralised cerebral dominance in terms of olfactory processing. Thirty-five subjects (22 women and 13 men, mean age 26 ± 3 years) participated in the study. NC was ascertained using a portable rhino-flowmeter. Structural and functional brain measurements were assessed using a 3T MR scanner. Vanillin odorant was presented during functional scans using a computer-controlled olfactometer. NC was found to be independent of the olfactory bulb volumes. Also, cerebral activations were found independent of the NC during odorant perception. NC potency is not associated with lateralised structural or functional differences in the cerebral olfactory system.

The potential for clinical application of automatic quantification of olfactory bulb volume in MRI scans using convolutional neural networks

The olfactory bulbs (OBs) play a key role in olfactory processing; their volume is important for diagnosis, prognosis and treatment of patients with olfactory loss. Until now, measurements of OB volumes have been limited to quantification of manually segmented OBs, which is a cumbersome task and makes evaluation of OB volumes in large scale clinical studies infeasible. Hence, the aim of this study was to evaluate the potential of our previously developed automatic OB segmentation method for application in clinical practice and to relate the results to clinical outcome measures. To evaluate utilization potential of the automatic segmentation method, three data sets containing MR scans of patients with olfactory loss were included. Dataset 1 (N = 66) and 3 (N = 181) were collected at the Smell and Taste Center in Ede (NL) on a 3 T scanner; dataset 2 (N = 42) was collected at the Smell and Taste Clinic in Dresden (DE) on a 1.5 T scanner. To define the reference standard, manual annotation of the OBs was performed in Dataset 1 and 2. OBs were segmented with a method that employs two consecutive convolutional neural networks (CNNs) that the first localize the OBs in an MRI scan and subsequently segment them. In Dataset 1 and 2, the method accurately segmented the OBs, resulting in a Dice coefficient above 0.7 and average symmetrical surface distance below 0.3 mm. Volumes determined from manual and automatic segmentations showed a strong correlation (Dataset 1: r = 0.79, p < 0.001; Dataset 2: r = 0.72, p = 0.004). In addition, the method was able to recognize the absence of an OB. In Dataset 3, OB volumes computed from automatic segmentations obtained with our method were related to clinical outcome measures, i.e. duration and etiology of olfactory loss, and olfactory ability. We found that OB volume was significantly related to age of the patient, duration and etiology of olfactory loss, and olfactory ability (F(5, 172) = 11.348, p < 0.001, R2 = 0.248). In conclusion, the results demonstrate that automatic segmentation of the OBs and subsequent computation of their volumes in MRI scans can be performed accurately and can be applied in clinical and research population studies. Automatic evaluation may lead to more insight in the role of OB volume in diagnosis, prognosis and treatment of olfactory loss.

DTI of the Olfactory Bulb in COVID-19-Related Anosmia: A Pilot Study

This study aimed to assess the utility of DTI in the detection of olfactory bulb dysfunction in COVID-19-related anosmia. It was performed in 62 patients with COVID-19-related anosmia and 23 controls. The mean diffusivity and fractional anisotropy were calculated by 2 readers. The difference between the fractional anisotropy and mean diffusivity values of anosmic and control olfactory bulbs was statistically significant (P = .001). The threshold of fractional anisotropy and mean diffusivity to differentiate a diseased from normal olfactory bulb were 0.22 and 1.5, with sensitivities of 84.4% and 96.8%, respectively, and a specificity of 100%.

Automated olfactory bulb segmentation on high resolutional T2-weighted MRI

The neuroimage analysis community has neglected the automated segmentation of the olfactory bulb (OB) despite its crucial role in olfactory function. The lack of an automatic processing method for the OB can be explained by its challenging properties (small size, location, and poor visibility on traditional MRI scans). Nonetheless, recent advances in MRI acquisition techniques and resolution have allowed raters to generate more reliable manual annotations. Furthermore, the high accuracy of deep learning methods for solving semantic segmentation problems provides us with an option to reliably assess even small structures. In this work, we introduce a novel, fast, and fully automated deep learning pipeline to accurately segment OB tissue on sub-millimeter T2-weighted (T2w) whole-brain MR images. To this end, we designed a three-stage pipeline: (1) Localization of a region containing both OBs using FastSurferCNN, (2) Segmentation of OB tissue within the localized region through four independent AttFastSurferCNN - a novel deep learning architecture with a self-attention mechanism to improve modeling of contextual information, and (3) Ensemble of the predicted label maps. For this work, both OBs were manually annotated in a total of 620 T2w images for training (n=357) and testing. The OB pipeline exhibits high performance in terms of boundary delineation, OB localization, and volume estimation across a wide range of ages in 203 participants of the Rhineland Study (Dice Score (Dice): 0.852, Volume Similarity (VS): 0.910, and Average Hausdorff Distance (AVD): 0.215 mm). Moreover, it also generalizes to scans of an independent dataset never encountered during training, the Human Connectome Project (HCP), with different acquisition parameters and demographics, evaluated in 30 cases at the native 0.7 mm HCP resolution (Dice: 0.738, VS: 0.790, and AVD: 0.340 mm), and the default 0.8 mm pipeline resolution (Dice: 0.782, VS: 0.858, and AVD: 0.268 mm). We extensively validated our pipeline not only with respect to segmentation accuracy but also to known OB volume effects, where it can sensitively replicate age effects (β=-0.232, p<.01). Furthermore, our method can analyze a 3D volume in less than a minute (GPU) in an end-to-end fashion, providing a validated, efficient, and scalable solution for automatically assessing OB volumes.

Automatic Segmentation of the Olfactory Bulb

The olfactory bulb (OB) has an essential role in the human olfactory pathway. A change in olfactory function is associated with a change of OB volume. It has been shown to predict the prognosis of olfactory loss and its volume is a biomarker for various neurodegenerative diseases, such as Alzheimer’s disease. Thus far, obtaining an OB volume for research purposes has been performed by manual segmentation alone; a very time-consuming and highly rater-biased process. As such, this process dramatically reduces the ability to produce fair and reliable comparisons between studies, as well as the processing of large datasets. Our study aims to solve this by proposing a novel methodological framework for the unbiased measurement of OB volume. In this paper, we present a fully automated tool that successfully performs such a task, accurately and quickly. In order to develop a stable and versatile algorithm and to train the neural network, we used four datasets consisting of whole-brain T1 and high-resolution T2 MRI scans, as well as the corresponding clinical information of the subject’s smelling ability. One dataset contained data of patients suffering from anosmia or hyposmia (N = 79), and the other three datasets contained data of healthy controls (N = 91). First, the manual segmentation labels of the OBs were created by two experienced raters, independently and blinded. The algorithm consisted of the following four different steps: (1) multimodal data co-registration of whole-brain T1 images and T2 images, (2) template-based localization of OBs, (3) bounding box construction, and lastly, (4) segmentation of the OB using a 3D-U-Net. The results from the automated segmentation algorithm were tested on previously unseen data, achieving a mean dice coefficient (DC) of 0.77 ± 0.05, which is remarkably convergent with the inter-rater DC of 0.79 ± 0.08 estimated for the same cohort. Additionally, the symmetric surface distance (ASSD) was 0.43 ± 0.10. Furthermore, the segmentations produced using our algorithm were manually rated by an independent blinded rater and have reached an equivalent rating score of 5.95 ± 0.87 compared to a rating score of 6.23 ± 0.87 for the first rater’s segmentation and 5.92 ± 0.81 for the second rater’s manual segmentation. Taken together, these results support the success of our tool in producing automatic fast (3–5 min per subject) and reliable segmentations of the OB, with virtually matching accuracy with the current gold standard technique for OB segmentation. In conclusion, we present a newly developed ready-to-use tool that can perform the segmentation of OBs based on multimodal data consisting of T1 whole-brain images and T2 coronal high-resolution images. The accuracy of the segmentations predicted by the algorithm matches the manual segmentations made by two well-experienced raters. This method holds potential for immediate implementation in clinical practice. Furthermore, its ability to perform quick and accurate processing of large datasets may provide a valuable contribution to advancing our knowledge of the olfactory system, in health and disease. Specifically, our framework may integrate the use of olfactory bulb volume (OBV) measurements for the diagnosis and treatment of olfactory loss and improve the prognosis and treatment options of olfactory dysfunctions.