IE-Vnet: Deep Learning-Based Segmentation of the Inner Ear's Total Fluid Space.

Background: In-vivo MR-based high-resolution volumetric quantification methods of the endolymphatic hydrops (ELH) are highly dependent on a reliable segmentation of the inner ear's total fluid space (TFS). This study aimed to develop a novel open-source inner ear TFS segmentation approach using a dedicated deep learning (DL) model. Methods: The model was based on a V-Net architecture (IE-Vnet) and a multivariate (MR scans: T1, T2, FLAIR, SPACE) training dataset (D1, 179 consecutive patients with peripheral vestibulocochlear syndromes). Ground-truth TFS masks were generated in a semi-manual, atlas-assisted approach. IE-Vnet model segmentation performance, generalizability, and robustness to domain shift were evaluated on four heterogenous test datasets (D2-D5, n = 4 × 20 ears). Results: The IE-Vnet model predicted TFS masks with consistently high congruence to the ground-truth in all test datasets (Dice overlap coefficient: 0.9 ± 0.02, Hausdorff maximum surface distance: 0.93 ± 0.71 mm, mean surface distance: 0.022 ± 0.005 mm) without significant difference concerning side (two-sided Wilcoxon signed-rank test, p>0.05), or dataset (Kruskal-Wallis test, p>0.05; post-hoc Mann-Whitney U, FDR-corrected, all p>0.2). Prediction took 0.2 s, and was 2,000 times faster than a state-of-the-art atlas-based segmentation method. Conclusion: IE-Vnet TFS segmentation demonstrated high accuracy, robustness toward domain shift, and rapid prediction times. Its output works seamlessly with a previously published open-source pipeline for automatic ELS segmentation. IE-Vnet could serve as a core tool for high-volume trans-institutional studies of the inner ear. Code and pre-trained models are available free and open-source under https://github.com/pydsgz/IEVNet.

Dosimetric impact of deep learning-based CT auto-segmentation on radiation therapy treatment planning for prostate cancer.

BACKGROUND: The evaluation of automatic segmentation algorithms is commonly performed using geometric metrics. An analysis based on dosimetric parameters might be more relevant in clinical practice but is often lacking in the literature. The aim of this study was to investigate the impact of state-of-the-art 3D U-Net-generated organ delineations on dose optimization in radiation therapy (RT) for prostate cancer patients. METHODS: A database of 69 computed tomography images with prostate, bladder, and rectum delineations was used for single-label 3D U-Net training with dice similarity coefficient (DSC)-based loss. Volumetric modulated arc therapy (VMAT) plans have been generated for both manual and automatic segmentations with the same optimization settings. These were chosen to give consistent plans when applying perturbations to the manual segmentations. Contours were evaluated in terms of DSC, average and 95% Hausdorff distance (HD). Dose distributions were evaluated with the manual segmentation as reference using dose volume histogram (DVH) parameters and a 3%/3 mm gamma-criterion with 10% dose cut-off. A Pearson correlation coefficient between DSC and dosimetric metrics, i.e. gamma index and DVH parameters, has been calculated. RESULTS: 3D U-Net-based segmentation achieved a DSC of 0.87 (0.03) for prostate, 0.97 (0.01) for bladder and 0.89 (0.04) for rectum. The mean and 95% HD were below 1.6 (0.4) and below 5 (4) mm, respectively. The DVH parameters, V[Formula: see text] for the bladder and V[Formula: see text] for the rectum, showed agreement between dose distributions within [Formula: see text] and [Formula: see text], respectively. The D[Formula: see text] and V[Formula: see text], for prostate and its 3 mm expansion (surrogate clinical target volume) showed agreement with the reference dose distribution within 2% and 3 Gy with the exception of one case. The average gamma pass-rate was 85%. The comparison between geometric and dosimetric metrics showed no strong statistically significant correlation. CONCLUSIONS: The 3D U-Net developed for this work achieved state-of-the-art geometrical performance. Analysis based on clinically relevant DVH parameters of VMAT plans demonstrated neither excessive dose increase to OARs nor substantial under/over-dosage of the target in all but one case. Yet the gamma analysis indicated several cases with low pass rates. The study highlighted the importance of adding dosimetric analysis to the standard geometric evaluation.

Using Base-ml to Learn Classification of Common Vestibular Disorders on DizzyReg Registry Data.

Background: Multivariable analyses (MVA) and machine learning (ML) applied on large datasets may have a high potential to provide clinical decision support in neuro-otology and reveal further avenues for vestibular research. To this end, we build base-ml, a comprehensive MVA/ML software tool, and applied it to three increasingly difficult clinical objectives in differentiation of common vestibular disorders, using data from a large prospective clinical patient registry (DizzyReg). Methods: Base-ml features a full MVA/ML pipeline for classification of multimodal patient data, comprising tools for data loading and pre-processing; a stringent scheme for nested and stratified cross-validation including hyper-parameter optimization; a set of 11 classifiers, ranging from commonly used algorithms like logistic regression and random forests, to artificial neural network models, including a graph-based deep learning model which we recently proposed; a multi-faceted evaluation of classification metrics; tools from the domain of "Explainable AI" that illustrate the input distribution and a statistical analysis of the most important features identified by multiple classifiers. Results: In the first clinical task, classification of the bilateral vestibular failure (N = 66) vs. functional dizziness (N = 346) was possible with a classification accuracy ranging up to 92.5% (Random Forest). In the second task, primary functional dizziness (N = 151) vs. secondary functional dizziness (following an organic vestibular syndrome) (N = 204), was classifiable with an accuracy ranging from 56.5 to 64.2% (k-nearest neighbors/logistic regression). The third task compared four episodic disorders, benign paroxysmal positional vertigo (N = 134), vestibular paroxysmia (N = 49), Menière disease (N = 142) and vestibular migraine (N = 215). Classification accuracy ranged between 25.9 and 50.4% (Naïve Bayes/Support Vector Machine). Recent (graph-) deep learning models classified well in all three tasks, but not significantly better than more traditional ML methods. Classifiers reliably identified clinically relevant features as most important toward classification. Conclusion: The three clinical tasks yielded classification results that correlate with the clinical intuition regarding the difficulty of diagnosis. It is favorable to apply an array of MVA/ML algorithms rather than a single one, to avoid under-estimation of classification accuracy. Base-ml provides a systematic benchmarking of classifiers, with a standardized output of MVA/ML performance on clinical tasks. To alleviate re-implementation efforts, we provide base-ml as an open-source tool for the community.

Simultaneous imputation and classification using Multigraph Geometric Matrix Completion (MGMC): Application to neurodegenerative disease classification.

Large-scale population-based studies in medicine are a key resource towards better diagnosis, monitoring, and treatment of diseases. They also serve as enablers of clinical decision support systems, in particular computer-aided diagnosis (CADx) using machine learning (ML). Numerous ML approaches for CADx have been proposed in literature. However, these approaches assume feature-complete data, which is often not the case in clinical data. To account for missing data, incomplete data samples are either removed or imputed, which could lead to data bias and may negatively affect classification performance. As a solution, we propose an end-to-end learning of imputation and disease prediction of incomplete medical datasets via Multi-graph Geometric Matrix Completion (MGMC). MGMC uses multiple recurrent graph convolutional networks, where each graph represents an independent population model based on a key clinical meta-feature like age, sex, or cognitive function. Graph signal aggregation from local patient neighborhoods, combined with multi-graph signal fusion via self-attention, has a regularizing effect on both matrix reconstruction and classification performance. Our proposed approach is able to impute class relevant features as well as perform accurate and robust classification on two publicly available medical datasets. We empirically show the superiority of our proposed approach in terms of classification and imputation performance when compared with state-of-the-art approaches. MGMC enables disease prediction in multimodal and incomplete medical datasets. These findings could serve as baseline for future CADx approaches which utilize incomplete datasets.

Modern machine-learning can support diagnostic differentiation of central and peripheral acute vestibular disorders.

BACKGROUND: Diagnostic classification of central vs. peripheral etiologies in acute vestibular disorders remains a challenge in the emergency setting. Novel machine-learning methods may help to support diagnostic decisions. In the current study, we tested the performance of standard and machine-learning approaches in the classification of consecutive patients with acute central or peripheral vestibular disorders. METHODS: 40 Patients with vestibular stroke (19 with and 21 without acute vestibular syndrome (AVS), defined by the presence of spontaneous nystagmus) and 68 patients with peripheral AVS due to vestibular neuritis were recruited in the emergency department, in the context of the prospective EMVERT trial (EMergency VERTigo). All patients received a standardized neuro-otological examination including videooculography and posturography in the acute symptomatic stage and an MRI within 7 days after symptom onset. Diagnostic performance of state-of-the-art scores, such as HINTS (Head Impulse, gaze-evoked Nystagmus, Test of Skew) and ABCD2 (Age, Blood, Clinical features, Duration, Diabetes), for the differentiation of vestibular stroke vs. peripheral AVS was compared to various machine-learning approaches: (i) linear logistic regression (LR), (ii) non-linear random forest (RF), (iii) artificial neural network, and (iv) geometric deep learning (Single/MultiGMC). A prospective classification was simulated by ten-fold cross-validation. We analyzed whether machine-estimated feature importances correlate with clinical experience. RESULTS: Machine-learning methods (e.g., MultiGMC) outperform univariate scores, such as HINTS or ABCD2, for differentiation of all vestibular strokes vs. peripheral AVS (MultiGMC area-under-the-curve (AUC): 0.96 vs. HINTS/ABCD2 AUC: 0.71/0.58). HINTS performed similarly to MultiGMC for vestibular stroke with AVS (AUC: 0.86), but more poorly for vestibular stroke without AVS (AUC: 0.54). Machine-learning models learn to put different weights on particular features, each of which is relevant from a clinical viewpoint. Established non-linear machine-learning methods like RF and linear methods like LR are less powerful classification models (AUC: 0.89 vs. 0.62). CONCLUSIONS: Established clinical scores (such as HINTS) provide a valuable baseline assessment for stroke detection in acute vestibular syndromes. In addition, machine-learning methods may have the potential to increase sensitivity and selectivity in the establishment of a correct diagnosis.

Towards computerized diagnosis of neurological stance disorders: data mining and machine learning of posturography and sway.

We perform classification, ranking and mapping of body sway parameters from static posturography data of patients using recent machine-learning and data-mining techniques. Body sway is measured in 293 individuals with the clinical diagnoses of acute unilateral vestibulopathy (AVS, n = 49), distal sensory polyneuropathy (PNP, n = 12), anterior lobe cerebellar atrophy (CA, n = 48), downbeat nystagmus syndrome (DN, n = 16), primary orthostatic tremor (OT, n = 25), Parkinson's disease (PD, n = 27), phobic postural vertigo (PPV n = 59) and healthy controls (HC, n = 57). We classify disorders and rank sway features using supervised machine learning. We compute a continuous, human-interpretable 2D map of stance disorders using t-stochastic neighborhood embedding (t-SNE). Classification of eight diagnoses yielded 82.7% accuracy [95% CI (80.9%, 84.5%)]. Five (CA, PPV, AVS, HC, OT) were classified with a mean sensitivity and specificity of 88.4% and 97.1%, while three (PD, PNP, and DN) achieved a mean sensitivity of 53.7%. The most discriminative stance condition was ranked as "standing on foam-rubber, eyes closed". Mapping of sway path features into 2D space revealed clear clusters among CA, PPV, AVS, HC and OT subjects. We confirm previous claims that machine learning can aid in classification of clinical sway patterns measured with static posturography. Given a standardized, long-term acquisition of quantitative patient databases, modern machine learning and data analysis techniques help in visualizing, understanding and utilizing high-dimensional sensor data from clinical routine.

Cloud Deployment of High-Resolution Medical Image Analysis With TOMAAT.

BACKGROUND: Deep learning has been recently applied to a multitude of computer vision and medical image analysis problems. Although recent research efforts have improved the state of the art, most of the methods cannot be easily accessed, compared or used by other researchers or clinicians. Even if developers publish their code and pre-trained models on the internet, integration in stand-alone applications and existing workflows is often not straightforward, especially for clinical research partners. In this paper, we propose an open-source framework to provide AI-enabled medical image analysis through the network. METHODS: TOMAAT provides a cloud environment for general medical image analysis, composed of three basic components: (i) an announcement service, maintaining a public registry of (ii) multiple distributed server nodes offering various medical image analysis solutions, and (iii) client software offering simple interfaces for users. Deployment is realized through HTTP-based communication, along with an API and wrappers for common image manipulations during pre- and post-processing. RESULTS: We demonstrate the utility and versatility of TOMAAT on several hallmark medical image analysis tasks: segmentation, diffeomorphic deformable atlas registration, landmark localization, and workflow integration. Through TOMAAT, the high hardware demands, setup and model complexity of demonstrated approaches are transparent to users, who are provided with simple client interfaces. We present example clients in three-dimensional Slicer, in the web browser, on iOS devices and in a commercially available, certified medical image analysis suite. CONCLUSION: TOMAAT enables deployment of state-of-the-art image segmentation in the cloud, fostering interaction among deep learning researchers and medical collaborators in the clinic. Currently, a public announcement service is hosted by the authors, and several ready-to-use services are registered and enlisted at http://tomaat.cloud.