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BACKGROUND: Although artificial intelligence systems that diagnosis among different conditions from medical images are long term aims, specific goals for automation of human-labor, time-consuming tasks are not only feasible but equally important. Acute conditions that require quantitative metrics, such as acute ischemic strokes, can greatly benefit by the consistency, objectiveness, and accessibility of automated radiological reports. METHODS: We used 1,878 annotated brain MRIs to generate a fully automated system that outputs radiological reports in addition to the infarct volume, 3D digital infarct mask, and the feature vector of anatomical regions affected by the acute infarct. This system is associated to a deep-learning algorithm for segmentation of the ischemic core and to parcellation schemes defining arterial territories and classically-identified anatomical brain structures. RESULTS: Here we show that the performance of our system to generate radiological reports was comparable to that of an expert evaluator. The weight of the components of the feature vectors that supported the prediction of the reports, as well as the prediction probabilities are outputted, making the pre-trained models behind our system interpretable. The system is publicly available, runs in real time, in local computers, with minimal computational requirements, and it is readily useful for non-expert users. It supports large-scale processing of new and legacy data, enabling clinical and translational research. CONCLUSION: The generation of reports indicates that our fully automated system is able to extract quantitative, objective, structured, and personalized information from stroke MRIs.

BACKGROUND: Accessible tools to efficiently detect and segment diffusion abnormalities in acute strokes are highly anticipated by the clinical and research communities. METHODS: We developed a tool with deep learning networks trained and tested on a large dataset of 2,348 clinical diffusion weighted MRIs of patients with acute and sub-acute ischemic strokes, and further tested for generalization on 280 MRIs of an external dataset (STIR). RESULTS: Our proposed model outperforms generic networks and DeepMedic, particularly in small lesions, with lower false positive rate, balanced precision and sensitivity, and robustness to data perturbs (e.g., artefacts, low resolution, technical heterogeneity). The agreement with human delineation rivals the inter-evaluator agreement; the automated lesion quantification of volume and contrast has virtually total agreement with human quantification. CONCLUSION: Our tool is fast, public, accessible to non-experts, with minimal computational requirements, to detect and segment lesions via a single command line. Therefore, it fulfills the conditions to perform large scale, reliable and reproducible clinical and translational research.

This study examines the atrophy rates of subjects with mild cognitive impairment (MCI) compared to controls in four regions within the medial temporal lobe: the transentorhinal cortex (TEC), entorhinal cortex (ERC), hippocampus, and amygdala. These regions were manually segmented and then corrected for undesirable longitudinal variability via Large Deformation Diffeomorphic Metric Mapping (LDDMM) based longitudinal diffeomorphometry. Diffeomorphometry techniques were used to compare thickness measurements in the TEC with the ERC. There were more significant changes in thickness atrophy rate in the TEC than medial regions of the entorhinal cortex. Volume measures were also calculated for all four regions. Classifiers were constructed using linear discriminant analysis to demonstrate that average thickness and atrophy rate of TEC together was the most discriminating measure compared to the thickness and volume measures in the areas examined, in differentiating MCI from controls. These findings are consistent with autopsy findings demonstrating that initial neuronal changes are found in TEC before spreading more medially in the ERC and to other regions in the medial temporal lobe. These findings suggest that the TEC thickness could serve as a biomarker for Alzheimer's disease in the prodromal phase of the disease.

Brain segmentation at different levels is generally represented as hierarchical trees. Brain regional atrophy at specific levels was found to be marginally associated with Alzheimer's disease outcomes. In this study, we propose an ℓ1‐type regularization for predictors that follow a hierarchical tree structure. Considering a tree as a directed acyclic graph, we interpret the model parameters from a path analysis perspective. Under this concept, the proposed penalty regulates the total effect of each predictor on the outcome. With regularity conditions, it is shown that under the proposed regularization, the estimator of the model coefficient is consistent in ℓ2‐norm and the model selection is also consistent. When applied to a brain sMRI dataset acquired from the Alzheimer's Disease Neuroimaging Initiative (ADNI), the proposed approach identifies brain regions where atrophy in these regions demonstrates the declination in memory. With regularization on the total effects, the findings suggest that the impact of atrophy on memory deficits is localized from small brain regions, but at various levels of brain segmentation. Data used in preparation of this paper were obtained from the ADNI database. [ABSTRACT FROM AUTHOR]

Background: Accessible tools to efficiently detect and segment diffusion abnormalities in acute strokes are highly anticipated by the clinical and research communities., Methods: We developed a tool with deep learning networks trained and tested on a large dataset of 2,348 clinical diffusion weighted MRIs of patients with acute and sub-acute ischemic strokes, and further tested for generalization on 280 MRIs of an external dataset (STIR)., Results: Our proposed model outperforms generic networks and DeepMedic, particularly in small lesions, with lower false positive rate, balanced precision and sensitivity, and robustness to data perturbs (e.g., artefacts, low resolution, technical heterogeneity). The agreement with human delineation rivals the inter-evaluator agreement; the automated lesion quantification of volume and contrast has virtually total agreement with human quantification., Conclusion: Our tool is fast, public, accessible to non-experts, with minimal computational requirements, to detect and segment lesions via a single command line. Therefore, it fulfills the conditions to perform large scale, reliable and reproducible clinical and translational research., Competing Interests: Competing interestsM.I.M. owns “AnatomyWorks”. This arrangement is managed by Johns Hopkins University in accordance with its conflict-of-interest policies. The remaining authors declare no competing interests., (© The Author(s) 2021.)

Small rodent cardiac magnetic resonance imaging (MRI) plays an important role in preclinical models of cardiac disease. Accurate myocardial boundaries delineation is crucial to most morphological and functional analysis in rodent cardiac MRIs. However, rodent cardiac MRIs, due to animal's small cardiac volume and high heart rate, are usually acquired with sub-optimal resolution and low signal-to-noise ratio (SNR). These rodent cardiac MRIs can also suffer from signal loss due to the intra-voxel dephasing. These factors make automatic myocardial segmentation challenging. Manual contouring could be applied to label myocardial boundaries but it is usually laborious, time consuming, and not systematically objective. In this study, we present a deep learning approach based on 3D attention M-net to perform automatic segmentation of left ventricular myocardium. In the deep learning architecture, we use dual spatial-channel attention gates between encoder and decoder along with multi-scale feature fusion path after decoder. Attention gates enable networks to focus on relevant spatial information and channel features to improve segmentation performance. A distance derived loss term, besides general dice loss and binary cross entropy loss, was also introduced to our hybrid loss functions to refine segmentation contours. The proposed model outperforms other generic models, like U-Net and FCN, in major segmentation metrics including the dice score (0.9072), Jaccard index (0.8307) and Hausdorff distance (3.1754 pixels), which are comparable to the results achieved by state-of-the-art models on human cardiac ACDC17 datasets.Clinical relevance Small rodent cardiac MRI is routinely used to probe the effect of individual genes or groups of genes on the etiology of a large number of cardiovascular diseases. An automatic myocardium segmentation algorithm specifically designed for these data can enhance accuracy and reproducibility of cardiac structure and function analysis.

Aquaporin-2 (AQP2) is a vasopressin-regulated water channel protein responsible for water reabsorption by the kidney collecting ducts. Under control conditions, most AQP2 resides in the recycling endosomes of principal cells, where it answers to vasopressin with trafficking to the apical plasma membrane to increase water reabsorption. Upon vasopressin withdrawal, apical AQP2 retreats to the early endosomes before joining the recycling endosomes for the next vasopressin stimulation. Prior studies have demonstrated a role of AQP2 S269 phosphorylation in reducing AQP2 endocytosis, thereby prolonging apical AQP2 retention. Here, we studied where in the cells S269 was phosphorylated and dephosphorylated in response to vasopressin versus withdrawal. In mpkCCD collecting cells, vacuolar protein sorting 35 knockdown slowed vasopressin-induced apical AQP2 trafficking, resulting in AQP2 accumulation in the recycling endosomes where S269 was phosphorylated. Rab7 knockdown, which impaired AQP2 trafficking from the early to recycling endosomes, reduced vasopressin-induced S269 phosphorylation. Rab5 knockdown, which impaired AQP2 endocytosis, did not affect vasopressin-induced S269 phosphorylation. Upon vasopressin withdrawal, S269 was not dephosphorylated in Rab5 knockdown cells. In contrast, S269 dephosphorylation upon vasopressin withdrawal was completed in Rab7 or vacuolar protein sorting 35 knockdown cells. We conclude that S269 is dephosphorylated during Rab5-mediated AQP2 endocytosis before AQP2 joins the recycling endosomes upon vasopressin withdrawal. While in the recycling endosomes, AQP2 can be phosphorylated at S269 in response to vasopressin before apical trafficking.

Aquaporin-2 (AQP2) is a vasopressin-regulated water channel protein responsible for osmotic water reabsorption by kidney collecting ducts. In response to vasopressin, AQP2 traffics from intracellular vesicles to the apical plasma membrane of collecting duct principal cells, where it increases water permeability and, hence, water reabsorption. Despite continuing efforts, gaps remain in our knowledge of vasopressin-regulated AQP2 trafficking. Here, we studied the functions of two retromer complex proteins, small GTPase Rab7 and vacuolar protein sorting 35 (Vps35), in vasopressin-induced AQP2 trafficking in a collecting duct cell model (mpkCCD cells). We showed that upon vasopressin removal, apical AQP2 returned to Rab5-positive early endosomes before joining Rab11-positive recycling endosomes. In response to vasopressin, Rab11-associated AQP2 trafficked to the apical plasma membrane before Rab5-associated AQP2 did so. Rab7 knockdown resulted in AQP2 accumulation in early endosomes and impaired vasopressin-induced apical AQP2 trafficking. In response to vasopressin, Rab7 transiently colocalized with Rab5, indicative of a role of Rab7 in AQP2 sorting in early endosomes before trafficking to the apical membrane. Rab7-mediated apical AQP2 trafficking in response to vasopressin required GTPase activity. When Vps35 was knocked down, AQP2 accumulated in recycling endosomes under vehicle conditions and did not traffic to the apical plasma membrane in response to vasopressin. We conclude that Rab7 and Vps35 participate in AQP2 sorting in early endosomes under vehicle conditions and apical membrane trafficking in response to vasopressin.