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In-situ examination regarding natural terrestrial-radioactivity via Uranium-238 (238U), Thorium-232 (232Th) as well as Potassium-40 (40K) within seaside urban-environment and its particular feasible well being ramifications.
Deep learning has successfully been leveraged for medical image segmentation. It employs convolutional neural networks (CNN) to learn distinctive image features from a defined pixel-wise objective function. However, this approach can lead to less output pixel interdependence producing incomplete and unrealistic segmentation results. In this paper, we present a fully automatic deep learning method for robust medical image segmentation by formulating the segmentation problem as a recurrent framework using two systems. The first one is a forward system of an encoder-decoder CNN that predicts the segmentation result from the input image. The predicted probabilistic output of the forward system is then encoded by a fully convolutional network (FCN)-based context feedback system. The encoded feature space of the FCN is then integrated back into the forward system's feed-forward learning process. Using the FCN-based context feedback loop allows the forward system to learn and extract more high-level image features and fix previous mistakes, thereby improving prediction accuracy over time. Experimental results, performed on four different clinical datasets, demonstrate our method's potential application for single and multi-structure medical image segmentation by outperforming the state of the art methods. With the feedback loop, deep learning methods can now produce results that are both anatomically plausible and robust to low contrast images. learn more Therefore, formulating image segmentation as a recurrent framework of two interconnected networks via context feedback loop can be a potential method for robust and efficient medical image analysis.Kidney volume is an essential biomarker for a number of kidney disease diagnoses, for example, chronic kidney disease. Existing total kidney volume estimation methods often rely on an intermediate kidney segmentation step. On the other hand, automatic kidney localization in volumetric medical images is a critical step that often precedes subsequent data processing and analysis. Most current approaches perform kidney localization via an intermediate classification or regression step. This paper proposes an integrated deep learning approach for (i) kidney localization in computed tomography scans and (ii) segmentation-free renal volume estimation. Our localization method uses a selection-convolutional neural network that approximates the kidney inferior-superior span along the axial direction. Cross-sectional (2D) slices from the estimated span are subsequently used in a combined sagittal-axial Mask-RCNN that detects the organ bounding boxes on the axial and sagittal slices, the combination of which produces a final 3D organ bounding box. Furthermore, we use a fully convolutional network to estimate the kidney volume that skips the segmentation procedure. We also present a mathematical expression to approximate the 'volume error' metric from the 'Sørensen-Dice coefficient.' We accessed 100 patients' CT scans from the Vancouver General Hospital records and obtained 210 patients' CT scans from the 2019 Kidney Tumor Segmentation Challenge database to validate our method. Our method produces a kidney boundary wall localization error of ~2.4mm and a mean volume estimation error of ~5%.
LVADs are surgically implanted mechanical pumps that improve survival rates of individuals with advanced heart failure. LVAD therapy is associated with high morbidity, which can be partially attributed to challenges with detecting LVAD complications before adverse events occur. Current methods used to monitor for complications with LVAD support require frequent clinical assessments at specialized LVAD centers. Analysis of recorded precordial sounds may enable real-time, remote monitoring of device and cardiac function for early detection of LVAD complications. The dominance of LVAD sounds in the precordium limits the utility of routine cardiac auscultation of LVAD recipients. In this work, we develop a signal processing pipeline to mitigate sounds generated by the LVAD.

We collected in vivo precordial sounds from 17 LVAD recipients, and contemporaneous echocardiograms from 12 of these individuals, to validate heart valve closure timings.

We characterized various acoustic signatures of heart sounds extracted from in vivo recordings, and report preliminary findings linking fundamental heart sound characteristics and level of LVAD support.

Mitigation of LVAD sounds from precordial sound recordings of LVAD recipients enables analysis of intrinsic heart sounds.

These findings provide proof-of-concept evidence of the clinical utility of heart sound analysis for bedside and remote monitoring of LVAD recipients.
These findings provide proof-of-concept evidence of the clinical utility of heart sound analysis for bedside and remote monitoring of LVAD recipients.
Biopsies are the gold standard for clinical diagnosis. However, a discrepancy between the biopsy sample and target tissue because of misplacement of the biopsy spoon can lead to errors in the diagnosis and subsequent treatment. Thus, correctly determining whether the needle tip is in the tumor is crucial for accurate biopsy results.

A biopsy needle system was designed with a steerable, flexible, and superelastic concentric tube; electrodes to monitor the electrical resistivity; and load cells to monitor the insertion force. The degrees of freedom were analyzed for two working modes straight-line and deflection.

Experimental results showed that the system could perceive the tissue type in online based on the electrical resistivity. In addition, changes in the insertion force indicated transitions between the interfaces of adjacent tissue layers.

The two monitoring methods guarantee that the biopsy spoon is at the desired position inside the tumor during an operation.

The proposed biopsy needle system can be integrated into an autonomous robotic biopsy system.
The proposed biopsy needle system can be integrated into an autonomous robotic biopsy system.
A common problem in magnetoencephalographic (MEG) and electroencephalographic (EEG) experimental paradigms relying on the estimation of brain evoked responses is the lengthy time of the experiment, which stems from the need to acquire a large number of repeated recordings. Using a bootstrap approach, we aim at reliably reducing the number of these repeated trials.

To this end, we assessed five variants of non-parametric bootstrapping based on the classical signal-plus-noise model constituting the foundation of signal averaging in MEG/EEG. We explain which of these approaches should and which should not be used for the aforementioned purpose, and why.

We present results for two advocated bootstrap variants applied to auditory MEG data. The ensuing trial-averaged magnetic fields served as input to the estimation of cortical source generators, with spatio-temporal matching pursuit as an example of an inverse solution technique. We propose, for a wide range of trial numbers, a general framework to evaluate the statistical properties of the parameter estimates for source locations and related time courses.
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