Semi-supervised COVID-19 volumetric pulmonary lesion estimation on CT images using probabilistic active contour and CNN segmentation.

Purpose: A semi-supervised two-step methodology is proposed to obtain a volumetric estimation of COVID-19-related lesions on Computed Tomography (CT) images. Methods: First, damaged tissue was segmented from CT images using a probabilistic active contours approach. Second, lung parenchyma was extracted using a previously trained U-Net. Finally, volumetric estimation of COVID-19 lesions was calculated considering the lung parenchyma masks.Our approach was validated using a publicly available dataset containing 20 CT COVID-19 images previously labeled and manually segmented. Then, it was applied to 295 COVID-19 patients CT scans admitted to an intensive care unit. We compared the lesion estimation between deceased and survived patients for high and low-resolution images. Results: A comparable median Dice similarity coefficient of 0.66 for the 20 validation images was achieved. For the 295 images dataset, results show a significant difference in lesion percentages between deceased and survived patients, with a p-value of 9.1 × 10-4 in low-resolution and 5.1 × 10-5 in high-resolution images. Furthermore, the difference in lesion percentages between high and low-resolution images was 10 % on average. Conclusion: The proposed approach could help estimate the lesion size caused by COVID-19 in CT images and may be considered an alternative to getting a volumetric segmentation for this novel disease without the requirement of large amounts of COVID-19 labeled data to train an artificial intelligence algorithm. The low variation between the estimated percentage of lesions in high and low-resolution CT images suggests that the proposed approach is robust, and it may provide valuable information to differentiate between survived and deceased patients.

COVID-19 Volumetric Pulmonary Lesion Estimation on CT Images using a U-NET and Probabilistic Active Contour Segmentation.

A two-step method for obtaining a volumetric estimation of COVID-19 related lesion from CT images is proposed. The first step consists in applying a U-NET convolutional neural network to provide a segmentation of the lung-parenchyma. This architecture is trained and validated using the Thoracic Volume and Pleural Effusion Segmentations in Diseased Lungs for Benchmarking Chest CT Processing Pipelines (PleThora) dataset, which is publicly available. The second step consists in obtaining the volumetric lesion estimation using an automatic algorithm based on a probabilistic active contour (PACO) region delimitation approach. Our pipeline successfully segmented COVID-19 related lesions in CT images, with exception of some mislabeled regions including lung airways and vasculature. Our workflow was applied to images in a cohort of 50 patients.

Proceedings of the Sixth Deep Brain Stimulation Think Tank Modulation of Brain Networks and Application of Advanced Neuroimaging, Neurophysiology, and Optogenetics.

The annual deep brain stimulation (DBS) Think Tank aims to create an opportunity for a multidisciplinary discussion in the field of neuromodulation to examine developments, opportunities and challenges in the field. The proceedings of the Sixth Annual Think Tank recapitulate progress in applications of neurotechnology, neurophysiology, and emerging techniques for the treatment of a range of psychiatric and neurological conditions including Parkinson's disease, essential tremor, Tourette syndrome, epilepsy, cognitive disorders, and addiction. Each section of this overview provides insight about the understanding of neuromodulation for specific disease and discusses current challenges and future directions. This year's report addresses key issues in implementing advanced neurophysiological techniques, evolving use of novel modulation techniques to deliver DBS, ans improved neuroimaging techniques. The proceedings also offer insights into the new era of brain network neuromodulation and connectomic DBS to define and target dysfunctional brain networks. The proceedings also focused on innovations in applications and understanding of adaptive DBS (closed-loop systems), the use and applications of optogenetics in the field of neurostimulation and the need to develop databases for DBS indications. Finally, updates on neuroethical, legal, social, and policy issues relevant to DBS research are discussed.

Parametric subtracted post-ictal diffusion tensor imaging for guiding direct neurostimulation therapy.

Parametric subtracted post-ictal diffusion tensor imaging (pspiDTI) is a novel imaging technique developed at our center to visualize transient, patient-specific, ictal-associated water diffusion abnormalities in hippocampal-associated axonal tissue. PspiDTI can elucidate putative connectivity patterns, tracing ictal propagation following a partial-onset seizure without generalization secondarily. PspiDTI compares two DTI volumes acquired during the early post-ictal period (<4 hr), and baseline inter-ictal interval (>24 hr post-seizure). This technique performs a voxel-wise parametric test to identify statistically significant transient ictal-associated changes in water diffusivity involving white matter (WM). Our technique was applied to six patients with refractory partial-onset epilepsy who were candidates for direct cortical responsive neurostimulation (RNS) therapy. Global and region-specific fractional anisotropy decreases, relative to baseline, were detected in all patients with a 17.01% (p < .01) relative mean decrement, while trace increases were found in 6/6 (100%) patients with a 13.30% (p < .01) relative global mean increment. Changes in diffusivity were anatomically compared with transient hyper-perfusion as detected by subtracted ictal SPECT co-registered to MRI (SISCOM). In 5/6 (83.33%) patients, alterations in WM diffusivity were detected adjacent to the SISCOM signal localized predominantly in gray matter. In 4/6 patients, post-implant RNS electrocorticography revealed early ictal propagation between implanted RNS depth leads guided by pspiDTI, hence validating concordant abnormal diffusivity regions detected by our technique. PspiDTI can complement the conventional pre-surgical evaluation to provide additional crucial information regarding WM ictal-propagation pathways between predominantly gray matter ictal-onset zones. When incorporated into a multi-modality pre-surgical workflow, pspiDTI can aid in defining critical nodes between ictogenic regions. This information can be used to strategically implant a limited set of two RNS depth leads for maximizing the extent to which direct cortical RNS can modulate a potentially extensive epileptogenic network.

Pre-implant modeling of depth lead placement in white matter for maximizing the extent of cortical activation during direct neurostimulation therapy.

INTRODUCTION: The objective of this work was to predict preoperatively the maximum extent to which direct stimulation therapy can propagate through an epileptic circuit for stabilizing refractory focal-onset epilepsy. A pre-surgical workflow is presented which comprises a computationally intensive process for calculating the volume of cortical activation (VOCA) surrounding cylindrical depth contacts virtually placed in white matter. The process employs an activation function (AF) derived from cable modeling of an axon. The AF was extrapolated to describe the three-dimensional activation of axon bundles facilitated by patient-specific diffusion tensor imaging (DTI). METHODS: The modeling process consisted of the following steps: (1) acquisition of structural MRI and DTI; (2) computation of the electric potential using the finite element method; (3) analysis of the effect of the modeled electric field on depolarizing axon bundles using the AF; (4) predicting distant cortical activation by strategically placing the AF seeds for creating a modulated circuit tractography (MCT) map; and finally, (5) post-implant in vivo validation using Subtracted Activated SPECT (SAS). RESULTS: The pre-implant simulation calculated non-spherical volumetric regions around the contacts representing areas of hyperpolarization and depolarization. Furthermore, the generated MCT map predicted the extent to which white matter connected epileptic sources were influenced during direct stimulation therapy. Validation of this map was demonstrated post-implantation employing RNS electrocorticography and SAS. The latter technique captured transient alterations in blood flow synched to neural metabolism potentially distant to the stimulated contacts. CONCLUSION: This pre-implant modeling system offers the potential for predicting optimal depth lead implant sites with a limited set of contacts for modulating the maximal extent of a refractory epileptogenic network.

Proceedings of the Third Annual Deep Brain Stimulation Think Tank: A Review of Emerging Issues and Technologies.

The proceedings of the 3rd Annual Deep Brain Stimulation Think Tank summarize the most contemporary clinical, electrophysiological, imaging, and computational work on DBS for the treatment of neurological and neuropsychiatric disease. Significant innovations of the past year are emphasized. The Think Tank's contributors represent a unique multidisciplinary ensemble of expert neurologists, neurosurgeons, neuropsychologists, psychiatrists, scientists, engineers, and members of industry. Presentations and discussions covered a broad range of topics, including policy and advocacy considerations for the future of DBS, connectomic approaches to DBS targeting, developments in electrophysiology and related strides toward responsive DBS systems, and recent developments in sensor and device technologies.