Gait, balance, and coordination are important in the development of chronic disease, but the ability to accurately assess these in the daily lives of patients may be limited by traditional biased ...assessment tools. Wearable sensors offer the possibility of minimizing the main limitations of traditional assessment tools by generating quantitative data on a regular basis, which can greatly improve the home monitoring of patients. However, these commercial sensors must be validated in this context with rigorous validation methods. This scoping review summarizes the state-of-the-art between 2010 and 2020 in terms of the use of commercial wearable devices for gait monitoring in patients. For this specific period, 10 databases were searched and 564 records were retrieved from the associated search. This scoping review included 70 studies investigating one or more wearable sensors used to automatically track patient gait in the field. The majority of studies (95%) utilized accelerometers either by itself (N = 17 of 70) or embedded into a device (N = 57 of 70) and/or gyroscopes (51%) to automatically monitor gait via wearable sensors. All of the studies (N = 70) used one or more validation methods in which “ground truth” data were reported. Regarding the validation of wearable sensors, studies using machine learning have become more numerous since 2010, at 17% of included studies. This scoping review highlights the current state of the ability of commercial sensors to enhance traditional methods of gait assessment by passively monitoring gait in daily life, over long periods of time, and with minimal user interaction. Considering our review of the last 10 years in this field, machine learning approaches are algorithms to be considered for the future. These are in fact data-based approaches which, as long as the data collected are numerous, annotated, and representative, allow for the training of an effective model. In this context, commercial wearable sensors allowing for increased data collection and good patient adherence through efforts of miniaturization, energy consumption, and comfort will contribute to its future success.
The spinal cord (SC) is a dense network of billions of fibers in a small volume surrounded by bones that makes tractography difficult to perform. We aim to provide a review collecting all technical ...settings of SC tractography and propose the optimal set of parameters to perform a good SC tractography rendering. The MEDLINE database was searched for articles reporting “spinal cord” “tractography” in “humans”. Studies were selected only when tractography rendering was displayed and MRI acquisition and tracking parameters detailed. From each study, clinical context, imaging acquisition settings, fiber tracking parameters, region of interest (ROI) design, and quality of the tractography rendering were extracted. Quality of tractography rendering was evaluated by several objective criteria proposed herein. According to the reported studies, to obtain a good tractography rendering, diffusion tensor imaging acquisition should be performed with 1.5 or 3 Tesla MRI, in the axial plane, with > 20 directions;
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value: 1000 s mm
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; right-left phase-encoding direction for cervical SC; isotropic voxel size; and no slice gap. Concerning the tracking process, it should be performed with determinist approach, fractional anisotropy threshold between 0.15 and 0.2, and curvature threshold of 40°. ROI design is an essential step for providing good tractography rendering, and their placement has to consider partial volume effects, magnetic susceptibility effects, and motion artifacts. The review reported herein highlights that successful SC tractography depends on many factors (imaging acquisition settings, fiber tracking parameters, and ROI design) to obtain a good SC tractography rendering.
Thanks to the recent development of sensors and Internet of Things (IoT), it is now common to use mobile application to monitor health status. These applications rely on sensors embedded in the ...smartphones that measure several physical quantities such as acceleration or angular velocity. However, these data are private information that can be used to infer sensitive attributes. This paper presents a new approach to anonymize the motion sensor data, preventing the re-identification of the user based on a selection of handcrafted features extracted from the distribution of zeros of the Shot-Time Fourier Transform (STFT). This work is motivated by recent works which highlight the importance of the zeros of the STFT Flandrin (IEEE Processing Letters 22:2137-2141,
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) and link them in the case of white noise to Gaussian Analytical Functions (GAF) Bardenet et al. (Applied and Computational Harmonic Analysis 48:682-705,
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) where the distribution of their zeros is formally described. The proposed approach is compared with an extension of an earlier work based on filtering in the time-frequency plane and doing the classification task based on convolutional neural networks, for which we improved the evaluation method and investigated the benefits of gyroscopic sensor’s data. An extensive comparison is performed on a first public dataset to assess the accuracy of activity recognition and user re-identification. We showed not only that the proposed method gives better results in term of activity/identity recognition trade-off compared with the state of the art but also that it can be generalized to other datasets.
Gliomas are infiltrative brain tumors with a margin difficult to identify. 5-ALA induced PpIX fluorescence measurements are a clinical standard, but expert-based classification models still lack ...sensitivity and specificity. Here a fully automatic clustering method is proposed to discriminate glioma margin. This is obtained from spectroscopic fluorescent measurements acquired with a recently introduced intraoperative set up. We describe a data-driven selection of best spectral features and show how this improves results of margin prediction from healthy tissue by comparison with the standard biomarker-based prediction. This pilot study based on 10 patients and 50 samples shows promising results with a best performance of 77% of accuracy in healthy tissue prediction from margin tissue.
One of the most challenging computer vision problems in the plant sciences is the segmentation of roots and soil in X-ray tomography. So far, this has been addressed using classical image analysis ...methods. In this paper, we address this soil–root segmentation problem in X-ray tomography using a variant of supervised deep learning-based classification called transfer learning where the learning stage is based on simulated data. The robustness of this technique, tested for the first time with this plant science problem, is established using soil–roots with very low contrast in X-ray tomography. We also demonstrate the possibility of efficiently segmenting the root from the soil while learning using purely synthetic soil and roots.
The possible depth of imaging of laser-scanning microscopy is limited not only by the working distances of objective lenses but also by image degradation caused by attenuation and diffraction of ...light passing through the specimen. To tackle this problem, one can either flip the sample to record images from both sides of the specimen or consecutively cut off shallow parts of the sample after taking serial images of certain thickness. Multiple image substacks acquired in these ways should be combined afterwards to generate a single stack. However, subtle movements of samples during image acquisition cause mismatch not only in the translation along x-, y-, and z-axes and rotation around z-axis but also tilting around x- and y-axes, making it difficult to register the substacks precisely. In this work, we developed a novel approach called 2D-SIFT-in-3D-Space using Scale Invariant Feature Transform (SIFT) to achieve robust three-dimensional matching of image substacks. Our method registers the substacks by separately fixing translation and rotation along x-, y-, and z-axes, through extraction and matching of stable features across two-dimensional sections of the 3D stacks. To validate the quality of registration, we developed a simulator of laser-scanning microscopy images to generate a virtual stack in which noise levels and rotation angles are controlled with known parameters. We illustrate quantitatively the performance of our approach by registering an entire brain of Drosophila melanogaster consisting of 800 sections. Our approach is also demonstrated to be extendable to other types of data that share large dimensions and need of fine registration of multiple image substacks. This method is implemented in Java and distributed as ImageJ/Fiji plugin. The source code is available via Github (http://www.creatis.insa-lyon.fr/site7/fr/MicroTools).
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•A novel approach using SIFT to achieve robust three-dimensional matching of image substacks is proposed.•We illustrate quantitatively the performance of our approach by registering an entire brain of Drosophila melanogaster.•Our approach is extendable to other data that share large dimensions and need of fine registration of multiple image.•This method is implemented in Java and freely distributed as ImageJ/Fiji plugin.
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•Deep learning predictive models of the final stroke infarct may have better performances when trained on reperfusion status specific subsets.•The proposed predictive models ...outperformed the standards threshold-based perfusion-diffusion mismatch models.•Comparing the predicted infarct in case of a successful vs failed reperfusion may help in estimating the treatment effect and guiding therapeutic decisions in selected patients.
Predictive maps of the final infarct may help therapeutic decisions in acute ischemic stroke patients. Our objectives were to assess whether integrating the reperfusion status into deep learning models would improve their performance, and to compare them to current clinical prediction methods.
We trained and tested convolutional neural networks (CNNs) to predict the final infarct in acute ischemic stroke patients treated by thrombectomy in our center. When training the CNNs, non-reperfused patients from a non-thrombectomized cohort were added to the training set to increase the size of this group. Baseline diffusion and perfusion-weighted magnetic resonance imaging (MRI) were used as inputs, and the lesion segmented on day-6 MRI served as the ground truth for the final infarct. The cohort was dichotomized into two subsets, reperfused and non-reperfused patients, from which reperfusion status specific CNNs were developed and compared to one another, and to the clinically-used perfusion-diffusion mismatch model. Evaluation metrics included the Dice similarity coefficient (DSC), precision, recall, volumetric similarity, Hausdorff distance and area-under-the-curve (AUC).
We analyzed 109 patients, including 35 without reperfusion. The highest DSC were achieved in both reperfused and non-reperfused patients (DSC = 0.44 ± 0.25 and 0.47 ± 0.17, respectively) when using the corresponding reperfusion status-specific CNN. CNN-based models achieved higher DSC and AUC values compared to those of perfusion-diffusion mismatch models (reperfused patients: AUC = 0.87 ± 0.13 vs 0.79 ± 0.17, P < 0.001; non-reperfused patients: AUC = 0.81 ± 0.13 vs 0.73 ± 0.14, P < 0.01, in CNN vs perfusion-diffusion mismatch models, respectively).
The performance of deep learning models improved when the reperfusion status was incorporated in their training. CNN-based models outperformed the clinically-used perfusion-diffusion mismatch model. Comparing the predicted infarct in case of successful vs failed reperfusion may help in estimating the treatment effect and guiding therapeutic decisions in selected patients.
We consider the detection of change in spatial distribution of fluorescent markers inside cells imaged by single cell microscopy. Such problems are important in bioimaging since the density of these ...markers can reflect the healthy or pathological state of cells, the spatial organization of DNA, or cell cycle stage. With the new super-resolved microscopes and associated microfluidic devices, bio-markers can be detected in single cells individually or collectively as a texture depending on the quality of the microscope impulse response. In this work, we propose, via numerical simulations, to address detection of changes in spatial density or in spatial clustering with an individual (pointillist) or collective (textural) approach by comparing their performances according to the size of the impulse response of the microscope. Pointillist approaches show good performances for small impulse response sizes only, while all textural approaches are found to overcome pointillist approaches with small as well as with large impulse response sizes. These results are validated with real fluorescence microscopy images with conventional resolution. This, a priori non-intuitive result in the perspective of the quest of super-resolution, demonstrates that, for difference detection tasks in single cell microscopy, super-resolved microscopes may not be mandatory and that lower cost, sub-resolved, microscopes can be sufficient.
Despite recent improvements in diffusion-weighted imaging, spinal cord tractography is not used in routine clinical practice because of difficulties in reconstructing tractograms, with a pertinent ...tri-dimensional-rendering, in a long post-processing time. We propose a new full tractography approach to the cervical spinal cord without extensive manual filtering or multiple regions of interest seeding that could help neurosurgeons manage various spinal cord disorders. Four healthy volunteers and two patients with either cervical intramedullary tumors or spinal cord injuries were included. Diffusion-weighted images of the cervical spinal cord were acquired using a Philips 3 Tesla machine, 32 diffusion directions, 1,000 s/mm
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-value, 2 × 2 × 2 mm voxel size, reduced field-of-view (ZOOM), with two opposing phase-encoding directions. Distortion corrections were then achieved using the FSL software package, and tracking of the full cervical spinal cord was performed using the DSI Studio software (quantitative anisotropy-based deterministic algorithm). A unique region of avoidance was used to exclude everything that is not of the nervous system. Fiber tracking parameters used adaptative fractional anisotropy from 0.015 to 0.045, fiber length from 10 to 1,000 mm, and angular threshold of 90°. In all participants, a full cervical cord tractography was performed from the medulla to the C7 spine level. On a ventral view, the junction between the medulla and spinal cord was identified with its pyramidal bulging, and by an invagination corresponding to the median ventral sulcus. On a dorsal view, the fourth ventricle—superior, middle, and inferior cerebellar peduncles—was seen, as well as its floor and the obex; and gracile and cuneate tracts were recognized on each side of the dorsal median sulcus. In the case of the intramedullary tumor or spinal cord injury, the spinal tracts were seen to be displaced, and this helped to adjust the neurosurgical strategy. This new full tractography approach simplifies the tractography pipeline and provides a reliable 3D-rendering of the spinal cord that could help to adjust the neurosurgical strategy.
MicroVIP is an open source software that assembles, in a unified web-application running on distributed computing resources, simulators of the main fluorescent microscopy imaging modalities (with ...existing codes or newly developed). MicroVIP provides realistic simulated images including several sources of noise (microfluidic blur effect, diffraction, Poisson noise, camera read out noise). MicroVIP also includes a module which simulates single cells with fluorescent markers and a module to analyze the simulated images with textural and pointillist feature spaces. MicroVIP is shown to be of value for supervised machine learning. It allow to automatically generate large sets of training images and virtual instrumentation to optimize the optical parameters before realizing real experiments.
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