Computer methods and programs in biomedicine
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Comput Methods Programs Biomed · Jul 2018
Development of an adaptive pulmonary simulator for in vitro analysis of patient populations and patient-specific data.
Patient-specific modeling (PSM) is gaining more attention from researchers due to its ability to potentially improve diagnostic capabilities, guide the design of intervention procedures, and optimize clinical management by predicting the outcome of a particular treatment and/or surgical intervention. Due to the hemodynamic diversity of specific patients, an adaptive pulmonary simulator (PS) would be essential for analyzing the possible impact of external factors on the safety, performance, and reliability of a cardiac assist device within a mock circulatory system (MCS). In order to accurately and precisely replicate the conditions within the pulmonary system, a PS should not only account for the ability of the pulmonary system to supply blood flow at specific pressures, but similarly consider systemic outflow dynamics. This would provide an accurate pressure and flow rate return supply back into the left ventricular section of the MCS (i.e. the initial conditions of the left heart). ⋯ The adaptability of this modelling approach allows for the simulation of pulmonary conditions without the limitations of a dedicated hardware platform for use in in vitro investigations.
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Medical image analysis and computer-assisted intervention problems are increasingly being addressed with deep-learning-based solutions. Established deep-learning platforms are flexible but do not provide specific functionality for medical image analysis and adapting them for this domain of application requires substantial implementation effort. Consequently, there has been substantial duplication of effort and incompatible infrastructure developed across many research groups. This work presents the open-source NiftyNet platform for deep learning in medical imaging. The ambition of NiftyNet is to accelerate and simplify the development of these solutions, and to provide a common mechanism for disseminating research outputs for the community to use, adapt and build upon. ⋯ The NiftyNet infrastructure enables researchers to rapidly develop and distribute deep learning solutions for segmentation, regression, image generation and representation learning applications, or extend the platform to new applications.
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Comput Methods Programs Biomed · May 2018
A novel biomedical image indexing and retrieval system via deep preference learning.
The traditional biomedical image retrieval methods as well as content-based image retrieval (CBIR) methods originally designed for non-biomedical images either only consider using pixel and low-level features to describe an image or use deep features to describe images but still leave a lot of room for improving both accuracy and efficiency. In this work, we propose a new approach, which exploits deep learning technology to extract the high-level and compact features from biomedical images. The deep feature extraction process leverages multiple hidden layers to capture substantial feature structures of high-resolution images and represent them at different levels of abstraction, leading to an improved performance for indexing and retrieval of biomedical images. ⋯ We propose a novel and automated indexing system based on deep preference learning to characterize biomedical images for developing computer aided diagnosis (CAD) systems in healthcare. Our proposed system shows an outstanding indexing ability and high efficiency for biomedical image retrieval applications and it can be used to collect and annotate the high-resolution images in a biomedical database for further biomedical image research and applications.
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Comput Methods Programs Biomed · Apr 2018
Assessing mechanical ventilation asynchrony through iterative airway pressure reconstruction.
Respiratory mechanics estimation can be used to guide mechanical ventilation (MV) but is severely compromised when asynchronous breathing occurs. In addition, asynchrony during MV is often not monitored and little is known about the impact or magnitude of asynchronous breathing towards recovery. Thus, it is important to monitor and quantify asynchronous breathing over every breath in an automated fashion, enabling the ability to overcome the limitations of model-based respiratory mechanics estimation during asynchronous breathing ventilation. ⋯ The iterative pressure reconstruction method is capable of identifying asynchronous breaths and improving respiratory mechanics estimation consistency compared to conventional model-based methods. It provides an opportunity to automate real-time quantification of asynchronous breathing frequency and magnitude that was previously limited to invasively method only.