Radar-based sensing emerges as a promising alternative to cameras and wearable devices for indoor human activity recognition. Unlike wearables, radar sensors offer non-contact and unobtrusive monitoring, while being i...
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ISBN:
(纸本)9781510673915;9781510673908
Radar-based sensing emerges as a promising alternative to cameras and wearable devices for indoor human activity recognition. Unlike wearables, radar sensors offer non-contact and unobtrusive monitoring, while being insensitive to lighting conditions and preserving privacy as compared to cameras. This paper addresses the task of continuous and sequential classification of daily life activities, unlike the problem to isolate distinct motions in isolation. Upon acquiring raw radar data containing sequences of motions, an event detection algorithm, the Short-Time-Average/Long-Time-Average (STA/LTA) algorithm, is utilized to detect individual motion segments. By recognizing breaks between transitions from one motion type to another, the STA/LTA detector isolates individual activity segments. To ensure consistent input shapes for activities of varying durations, image resizing and cropping techniques are employed. Furthermore, data augmentation techniques are applied to modify micro-Doppler signatures, enhancing the classification system's robustness and providing additional data for training.
National traditional culture and traditional technology are the products of historical precipitation and indispensable precious resources. The establishment of national cultural database is of great significance for t...
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In order to improve the segmentation accuracy of 3D point cloud model in feature ambiguous region, the unsupervised clustering algorithm based on surface fusion features combines the depth residuals with the normal de...
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Garbage classification can be seen everywhere in today's society, but due to the changes of the times, the awareness that different types of waste should go into different types of bins is not widespread among mos...
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In order to overcome defects resulted from replacing the photo background color, an improved method is proposed for background replacement. The α-values in the alpha matte are transformed to enhance the details in th...
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Accurately identifying field weeds is crucial for selecting appropriate agricultural machinery and herbicides. This paper focuses on nine common weed species during the seedling stage in natural backgrounds of field v...
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Digital subtraction angiography (DSA) is routinely used for measuring the dimensions and characteristics of cerebral aneurysms as a step in planning of interventional treatments. Incorrect sizing of the aneurysm sac p...
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ISBN:
(纸本)9781510649446;9781510649439
Digital subtraction angiography (DSA) is routinely used for measuring the dimensions and characteristics of cerebral aneurysms as a step in planning of interventional treatments. Incorrect sizing of the aneurysm sac puts the patient at the risk of incomplete treatment due to the use of an intrasaccular implant that is too small or too large. In this work, we propose an automatic method to segment the aneurysm sac in 2D DSA images to enable fast and accurate measurements. We use a UNet-like architecture. However, we replace the encoder arm of this network with an EfficientNet architecture, pre-trained on 300 million natural images. We show that this architecture delivers very accurate segmentation of the aneurysm sac on a dataset of 144 DSA images obtained from patients prior to implantation of an intrasaccular device to treat wide-neck bifurcation aneurysms. We report a Dice coefficient of 0.9.
Primary angle closure disease (PACD) is a leading cause of permanent vision loss worldwide, so early treatment of patients suffering from symptoms of PACD is crucial to prevent vision loss. Gonioscopy is the current c...
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ISBN:
(数字)9781510649507
ISBN:
(纸本)9781510649507;9781510649491
Primary angle closure disease (PACD) is a leading cause of permanent vision loss worldwide, so early treatment of patients suffering from symptoms of PACD is crucial to prevent vision loss. Gonioscopy is the current clinical standard for diagnosing PACD. However, gonioscopy is a qualitative subjective assessment method. Thus, there is a need for a quantitative method to diagnose PACD. Anterior Segment Optical Coherence Tomography (AS-OCT) is an imaging modality which produces images of anterior structures such as the anterior chamber angle. Adoption of AS-OCT has been slow due to AS-OCT analysis not being standardized and inefficient. Currently, users must annotate each image by hand using proprietary software and use expert knowledge to diagnose PACD based on the key features annotated. Using an imaging-informatics based approach on a dataset of over 900 images we have developed a system to streamline and standardize AS-OCT analysis. This system will be DICOM compatible to promote standardization of AS-OCT images. This system will be attached to a HIPAA compliant database and will require a secure login to protect patient privacy. We have developed a streamlined approach towards annotating key features in AS-OCT images which will be used to validate the results of an automatic segmentation method. The automatic segmentation method will be integrated into the system to increase the efficiency of analyzing AS-OCT images and eliminate the need to annotate images for clinical diagnosis. These features will be used in the future to classify PACD based on the severity of the angle closure.
Deep Neural Networks (DNNs) have the potential for making various clinical procedures more time-efficient by automating medical image segmentation. Due to their strong, in some cases human-level, performance, they hav...
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ISBN:
(数字)9781510649408
ISBN:
(纸本)9781510649408;9781510649392
Deep Neural Networks (DNNs) have the potential for making various clinical procedures more time-efficient by automating medical image segmentation. Due to their strong, in some cases human-level, performance, they have become the standard approach in this field. The design of the best possible medical image segmentation DNNs, however, is task-specific. Neural Architecture Search (NAS), i.e., the automation of neural network design, has been shown to have the capability to outperform manually designed networks for various tasks. However, the existing NAS methods for medical image segmentation have explored a quite limited range of types of DNN architectures that can be discovered. In this work, we propose a novel NAS search space for medical image segmentation networks. This search space combines the strength of a generalised encoder-decoder structure, well known from U-Net, with network blocks that have proven to have a strong performance in image classification tasks. The search is performed by looking for the best topology of multiple cells simultaneously with the configuration of each cell within, allowing for interactions between topology and cell-level attributes. From experiments on two publicly available datasets, we find that the networks discovered by our proposed NAS method have better performance than well-known handcrafted segmentation networks, and outperform networks found with other NAS approaches that perform only topology search, and topology-level search followed by cell-level search.
Optical coherence tomography angiography (OCTA) is a novel noninvasive imaging modality for visualization of retinal blood flow in the human retina. Using specific OCTA imaging biomarkers for the identification of pat...
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ISBN:
(数字)9781510649422
ISBN:
(纸本)9781510649422;9781510649415
Optical coherence tomography angiography (OCTA) is a novel noninvasive imaging modality for visualization of retinal blood flow in the human retina. Using specific OCTA imaging biomarkers for the identification of pathologies, automated image segmentations of the blood vessels can improve subsequent analysis and diagnosis. We present a novel segmentation method for vessel density identification based on frequency representations of the image, in particular, using so-called Gabor filter banks. The algorithm is evaluated qualitatively and quantitatively on an OCTA image in-house data set from 10 eyes acquired by a Cirrus HD-OCT device. Qualitatively, the segmentation outcomes received very good visual evaluation feedback by experts. Quantitatively, we compared the resulting vessel density values with automated in-built values provided by the device. The results underline the visual evaluation. For the evaluation of the FAZ identification substep, manual annotations of 2 expert graders were used, showing that our results coincide well in visual and quantitative manners. Lastly, we suggest the computation of adaptive local vessel density maps that allow straightforward analysis of retinal blood flow in a local manner.
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