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This work evaluates current 3-D image registration tools on clinically acquired abdominal computed tomography (CT) scans. Thirteen abdominal organs were manually labeled on a set of 100 CT images, and the 100 labeled images were pairwise registered based on intensity information with six registration tools (FSL, ANTS-CC, ANTS-QUICK-MI, IRTK, NIFTYREG, and DEEDS). The results suggest that DEEDS yielded the best registration performance.
All data and source code are available.
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This review describes the advantages of creating a device that interfaces with the LGN of the thalamus for patients who have lost sight through glaucoma, the leading cause of blindness and for which no satisfactory medical treatment exists. We cover the unique advantages the structure of the LGN provides for a visual prosthesis, how neural degeneration progresses during glaucoma, and the challenge of interfacing to a structure deep in the brain.
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We propose a dual sequential Monte Carlo adaptive point process method, which models and decodes the gradually changing modulation depth of individual neuron over the course of a movement. We use multi-channel neural spike trains from the primary motor cortex of a monkey performing a target pursuit task, and test our computational approach with successful tracking on the neural modulation depth over time and improvement on kinematic reconstruction.
We have been developing a closed-loop automated cardiovascular drug infusion system to automate the hemodynamic management in HF.
In dogs with HF, our newly developed system have succeeded in optimizing the hemodynamics automatically and stably in a minimally invasive and clinically feasible setting.
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We present the framework for wearable joint rehabilitation assessment following acute knee injury based on the measurement of acoustical emissions from the knee with miniature microphones. The research reported in this paper was highlighted in several major news pieces including Scientific American and BBC Radio.
We propose computer-based methods for segmenting 2D EBUS (Endobronchial ultrasound) frames and 3D EBUS sequences.
Both segmentation methods compare very favorably to ground-truth results for a human lung-cancer patient EBUS database. We also demonstrate the potential of the methods for EBUS localization in a multimodal image-guided bronchoscopy system.
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