Browsing by Subject "Convolutional neural network"

Now showing 1 - 6 of 6
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    A spatial map: a propitious choice for constraining the binding problem
    (Frontiers Media, 2024-07-02) Han, Zhixian; Sereno, Anne B.; Medicine, School of Medicine
    Many studies have shown that the human visual system has two major functionally distinct cortical visual pathways: a ventral pathway, thought to be important for object recognition, and a dorsal pathway, thought to be important for spatial cognition. According to our and others previous studies, artificial neural networks with two segregated pathways can determine objects' identities and locations more accurately and efficiently than one-pathway artificial neural networks. In addition, we showed that these two segregated artificial cortical visual pathways can each process identity and spatial information of visual objects independently and differently. However, when using such networks to process multiple objects' identities and locations, a binding problem arises because the networks may not associate each object's identity with its location correctly. In a previous study, we constrained the binding problem by training the artificial identity pathway to retain relative location information of objects. This design uses a location map to constrain the binding problem. One limitation of that study was that we only considered two attributes of our objects (identity and location) and only one possible map (location) for binding. However, typically the brain needs to process and bind many attributes of an object, and any of these attributes could be used to constrain the binding problem. In our current study, using visual objects with multiple attributes (identity, luminance, orientation, and location) that need to be recognized, we tried to find the best map (among an identity map, a luminance map, an orientation map, or a location map) to constrain the binding problem. We found that in our experimental simulations, when visual attributes are independent of each other, a location map is always a better choice than the other kinds of maps examined for constraining the binding problem. Our findings agree with previous neurophysiological findings that show that the organization or map in many visual cortical areas is primarily retinotopic or spatial.
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    Deep-Learning-Based High-Intensity Focused Ultrasound Lesion Segmentation in Multi-Wavelength Photoacoustic Imaging
    (MDPI, 2023-09-08) Wu, Xun; Sanders, Jean L.; Dundar, M. Murat; Oralkan, Ömer; Computer and Information Science, School of Science
    Photoacoustic (PA) imaging can be used to monitor high-intensity focused ultrasound (HIFU) therapies because ablation changes the optical absorption spectrum of the tissue, and this change can be detected with PA imaging. Multi-wavelength photoacoustic (MWPA) imaging makes this change easier to detect by repeating PA imaging at multiple optical wavelengths and sampling the optical absorption spectrum more thoroughly. Real-time pixel-wise classification in MWPA imaging can assist clinicians in monitoring HIFU lesion formation and will be a crucial milestone towards full HIFU therapy automation based on artificial intelligence. In this paper, we present a deep-learning-based approach to segment HIFU lesions in MWPA images. Ex vivo bovine tissue is ablated with HIFU and imaged via MWPA imaging. The acquired MWPA images are then used to train and test a convolutional neural network (CNN) for lesion segmentation. Traditional machine learning algorithms are also trained and tested to compare with the CNN, and the results show that the performance of the CNN significantly exceeds traditional machine learning algorithms. Feature selection is conducted to reduce the number of wavelengths to facilitate real-time implementation while retaining good segmentation performance. This study demonstrates the feasibility and high performance of the deep-learning-based lesion segmentation method in MWPA imaging to monitor HIFU lesion formation and the potential to implement this method in real time.
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    Determination of Internal Elevation Fluctuation from CCTV Footage of Sanitary Sewers Using Deep Learning
    (MDPI, 2021) Ji, Hyon Wook; Yoo, Sung Soo; Koo, Dan Daehyun; Kang, Jeong-Hee; Engineering Technology, School of Engineering and Technology
    The slope of sewer pipes is a major factor for transporting sewage at designed flow rates. However, the gradient inside the sewer pipe changes locally for various reasons after construction. This causes flow disturbances requiring investigation and appropriate maintenance. This study extracted the internal elevation fluctuation from closed-circuit television investigation footage, which is required for sanitary sewers. The principle that a change in water level in sewer pipes indirectly indicates a change in elevation was applied. The sewage area was detected using a convolutional neural network, a type of deep learning technique, and the water level was calculated using the geometric principles of circles and proportions. The training accuracy was 98%, and the water level accuracy compared to random sampling was 90.4%. Lateral connections, joints, and outliers were removed, and a smoothing method was applied to reduce data fluctuations. Because the target sewer pipes are 2.5 m concrete reinforced pipes, the joint elevation was determined every 2.5 m so that the internal slope of the sewer pipe would consist of 2.5 m linear slopes. The investigative method proposed in this study is effective with high economic feasibility and sufficient accuracy compared to the existing sensor-based methods of internal gradient investigation.
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    Estimation of Defocus Blur in Virtual Environments Comparing Graph Cuts and Convolutional Neural Network
    (2018-12) Chowdhury, Prodipto; Christopher, Lauren; King, Brian; Ben-Miled, Zina
    Depth estimation is one of the most important problems in computer vision. It has attracted a lot of attention because it has applications in many areas, such as robotics, VR and AR, self-driving cars etc. Using the defocus blur of a camera lens is one of the methods of depth estimation. In this thesis, we have researched this technique in virtual environments. Virtual datasets have been created for this purpose. In this research, we have applied graph cuts and convolutional neural network (DfD-net) to estimate depth from defocus blur using a natural (Middlebury) and a virtual (Maya) dataset. Graph Cuts showed similar performance for both natural and virtual datasets in terms of NMAE and NRMSE. However, with regard to SSIM, the performance of graph cuts is 4% better for Middlebury compared to Maya. We have trained the DfD-net using the natural and the virtual dataset and then combining both datasets. The network trained by the virtual dataset performed best for both datasets. The performance of graph-cuts and DfD-net have been compared. Graph-Cuts performance is 7% better than DfD-Net in terms of SSIM for Middlebury images. For Maya images, DfD-Net outperforms Graph-Cuts by 2%. With regard to NRMSE, Graph-Cuts and DfD-net shows similar performance for Maya images. For Middlebury images, Graph-cuts is 1.8% better. The algorithms show no difference in performance in terms of NMAE. The time DfD-net takes to generate depth maps compared to graph cuts is 500 times less for Maya images and 200 times less for Middlebury images.
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    Image Segmentation of Operative Neuroanatomy Into Tissue Categories Using a Machine Learning Construct and Its Role in Neurosurgical Training
    (Wolters Kluwer, 2022-10) Witten , Andrew J.; Patel , Neal; Cohen-Gadol, Aaron; Neurological Surgery, School of Medicine
    BACKGROUND: The complexity of the relationships among the structures within the brain makes efficient mastery of neuroanatomy difficult for medical students and neurosurgical residents. Therefore, there is a need to provide real-time segmentation of neuroanatomic images taken from various perspectives to assist with training. OBJECTIVE: To develop the initial foundation of a neuroanatomic image segmentation algorithm using artificial intelligence for education. METHODS: A pyramidal scene-parsing network with a convolutional residual neural network backbone was assessed for its ability to accurately segment neuroanatomy images. A data set of 879 images derived from The Neurosurgical Atlas was used to train, validate, and test the network. Quantitative assessment of the segmentation was performed using pixel accuracy, intersection-over-union, the Dice similarity coefficient, precision, recall, and the boundary F1 score. RESULTS: The network was trained, and performance was assessed class wise. Compared with the ground truth annotations, the ensembled results for our artificial intelligence framework for the pyramidal scene-parsing network during testing generated a total pixel accuracy of 91.8%. CONCLUSION: Using the presented methods, we show that a convolutional neural network can accurately segment gross neuroanatomy images, which represents an initial foundation in artificial intelligence gross neuroanatomy that will aid future neurosurgical training. These results also suggest that our network is sufficiently robust, to an unprecedented level, for performing anatomic category recognition in a clinical setting.
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    Image Segmentation of Operative Neuroanatomy Into Tissue Categories Using a Machine Learning Construct and Its Role in Neurosurgical Training
    (Wolters Kluwer, 2022) Witten, Andrew J.; Patel, Neal; Cohen-Gadol, Aaron; Neurological Surgery, School of Medicine
    Background: The complexity of the relationships among the structures within the brain makes efficient mastery of neuroanatomy difficult for medical students and neurosurgical residents. Therefore, there is a need to provide real-time segmentation of neuroanatomic images taken from various perspectives to assist with training. Objective: To develop the initial foundation of a neuroanatomic image segmentation algorithm using artificial intelligence for education. Methods: A pyramidal scene-parsing network with a convolutional residual neural network backbone was assessed for its ability to accurately segment neuroanatomy images. A data set of 879 images derived from The Neurosurgical Atlas was used to train, validate, and test the network. Quantitative assessment of the segmentation was performed using pixel accuracy, intersection-over-union, the Dice similarity coefficient, precision, recall, and the boundary F1 score. Results: The network was trained, and performance was assessed class wise. Compared with the ground truth annotations, the ensembled results for our artificial intelligence framework for the pyramidal scene-parsing network during testing generated a total pixel accuracy of 91.8%. Conclusion: Using the presented methods, we show that a convolutional neural network can accurately segment gross neuroanatomy images, which represents an initial foundation in artificial intelligence gross neuroanatomy that will aid future neurosurgical training. These results also suggest that our network is sufficiently robust, to an unprecedented level, for performing anatomic category recognition in a clinical setting.