Browsing by Subject "three-dimensional displays"

Now showing 1 - 4 of 4
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    Four Dimensional Image Registration For Intravital Microscopy
    (IEEE, 2016-06) Fu, Chichen; Gadgil, Neeraj; Tahboub, Khalid K.; Salama, Paul; Dunn, Kenneth W.; Delp, Edward J.; Department of Biochemistry and Molecular Biology, School of Medicine
    Increasingly the behavior of living systems is being evaluated using intravital microscopy since it provides subcellular resolution of biological processes in an intact living organism. Intravital microscopy images are frequently confounded by motion resulting from animal respiration and heartbeat. In this paper we describe an image registration method capable of correcting motion artifacts in three dimensional fluorescence microscopy images collected over time. Our method uses 3D B-Spline non-rigid registration using a coarse-to-fine strategy to register stacks of images collected at different time intervals and 4D rigid registration to register 3D volumes over time. The results show that our proposed method has the ability of correcting global motion artifacts of sample tissues in four dimensional space, thereby revealing the motility of individual cells in the tissue.
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    Multimodal Sequence Classification of force-based instrumented hand manipulation motions using LSTM-RNN deep learning models
    (IEEE, 2023-10) Bhattacharjee, Abhinaba; Anwar, Sohel; Whitinger, Lexi; Loghmani, M. Terry; Health Sciences, School of Health and Human Sciences
    The advent of mobile ubiquitous computing enabled sensor informatics of human movements to be used in modeling and building deep learning classifiers for cognitive AI. Expanding deep learning approaches for classifying instrumented hand manipulation tasks, especially the art of manual therapy and soft tissue manipulation, can potentially augment practitioner’s performance and enhance fidelity with computer assisted guidelines. This paper introduces a dataset of 3D force profiles and manipulation motion sequences of controlled soft tissue manipulation stroke pattern applications in thoracolumbar, upper thigh and calf regions of a single human subject performed by five experienced manual therapists. The multimodal 3D force, 3D accelerometer and resultant gyro raw data were preprocessed and experimentally fed into a multilayered Long Short-Term Memory (LSTM) based Recurrent Neural Network (RNN) deep learning model to observe sequence classifications of two manipulation motion techniques (Linear "Strumming" motion and curvilinear "J-Stroke" arched motion) of manual therapy performed using a handheld, localizing Quantifiable Soft Tissue Manipulation (QSTM) medical tool. Each of these motion sequences were further labeled with corresponding best practice technique from validated video tapes and reclassified into "Correct" and "Incorrect" practice based on defined criteria. The deep learning model resulted in 90-95% classification accuracy for individual intra-therapist reduced dataset. The classification accuracy varied between 78%-93% range, when trained with multivariate characteristic feature set combinations for the complete spectrum of inter-therapist dataset.Clinical Relevance — AI informed online therapeutic guidelines can be leveraged to minimize practice inconsistencies, optimize educational training of therapy using data informed protocols, and study progression of pain and healing towards advancing manual therapy.
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    Nuclei Segmentation of Fluorescence Microscopy Images Using Three Dimensional Convolutional Neural Networks
    (IEEE, 2017-07) Ho, David Joon; Fu, Chichen; Salama, Paul; Dunn, Kenneth W.; Delp, Edward J.; Electrical and Computer Engineering, School of Engineering and Technology
    Fluorescence microscopy enables one to visualize subcellular structures of living tissue or cells in three dimensions. This is especially true for two-photon microscopy using near-infrared light which can image deeper into tissue. To characterize and analyze biological structures, nuclei segmentation is a prerequisite step. Due to the complexity and size of the image data sets, manual segmentation is prohibitive. This paper describes a fully 3D nuclei segmentation method using three dimensional convolutional neural networks. To train the network, synthetic volumes with corresponding labeled volumes are automatically generated. Our results from multiple data sets demonstrate that our method can successfully segment nuclei in 3D.
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    Three Dimensional Fluorescence Microscopy Image Synthesis and Segmentation
    (IEEE, 2018-06) Fu, Chichen; Lee, Soonam; Ho, David Joon; Han, Shuo; Salama, Paul; Dunn, Kenneth W.; Delp, Edward J.; Electrical and Computer Engineering, School of Engineering and Technology
    Advances in fluorescence microscopy enable acquisition of 3D image volumes with better image quality and deeper penetration into tissue. Segmentation is a required step to characterize and analyze biological structures in the images and recent 3D segmentation using deep learning has achieved promising results. One issue is that deep learning techniques require a large set of groundtruth data which is impractical to annotate manually for large 3D microscopy volumes. This paper describes a 3D deep learning nuclei segmentation method using synthetic 3D volumes for training. A set of synthetic volumes and the corresponding groundtruth are generated using spatially constrained cycle-consistent adversarial networks. Segmentation results demonstrate that our proposed method is capable of segmenting nuclei successfully for various data sets.