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Adaptive 3D descattering with a dynamic synthesis network

Light(Science & Applications) 2022年第5期11卷 766-786页

作者： waleed tahir Hao Wang Lei Tian Department of Electrical and Computer Engineering Boston UniversityBostonMA 02215USA Department of Biomedical Engineering Boston UniversityBostonMA 02215USA

Deep learning has been broadly applied to imaging in scattering applications.A common framework is to train a descattering network for image recovery by removing scattering *** achieve the best results on a broad spectrum of scattering conditions,individual“expert”networks need to be trained for each ***,the expert’s performance sharply degrades when the testing condition differs from the *** alternative brute-force approach is to train a“generalist”network using data from diverse scattering *** generally requires a larger network to encapsulate the diversity in the data and a sufficiently large training set to avoid ***,we propose an adaptive learning framework,termed dynamic synthesis network(DSN),which dynamically adjusts the model weights and adapts to different scattering *** adaptability is achieved by a novel“mixture of experts”architecture that enables dynamically synthesizing a network by blending multiple experts using a gating *** demonstrate the DSN in holographic 3D particle imaging for a variety of scattering *** show in simulation that our DSN provides generalization across a continuum of scattering *** addition,we show that by training the DSN entirely on simulated data,the network can generalize to experiments and achieve robust 3D *** expect the same concept can find many other applications,such as denoising and imaging in scattering ***,our dynamic synthesis framework opens up a new paradigm for designing highly adaptive deep learning and computational imaging techniques.

关键词： network scattering synthesis

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Holographic particle localization under multiple scattering

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Advanced Photonics 2019年第3期1卷 39-50页

作者： waleed tahir Ulugbek S.Kamilov Lei Tian Boston University Department of Electrical and Computer EngineeringBostonMassachusettsUnited States Washington University in St.Louis Department of Electrical and Systems EngineeringSt.LouisMissouriUnited States Washington University in St.Louis Department of Computer Science and EngineeringSt.LouisMissouriUnited States

We introduce a computational framework that incorporates multiple scattering for large-scale threedimensional(3-D)particle localization using single-shot in-line *** holographic techniques rely on single-scattering models that become inaccurate under high particle densities and large refractive index *** multiple scattering solvers become computationally prohibitive for large-scale problems,which comprise millions of voxels within the scattering *** approach overcomes the computational bottleneck by slicewise computation of multiple scattering under an efficient recursive *** the forward model,each recursion estimates the next higher-order multiple scattered field among the object *** the inverse model,each order of scattering is recursively estimated by a nonlinear optimization *** nonlinear inverse model is further supplemented by a sparsity promoting procedure that is particularly effective in localizing 3-D distributed *** show that our multiple-scattering model leads to significant improvement in the quality of 3-D localization compared to traditional methods based on single scattering *** experiments demonstrate robust inverse multiple scattering,allowing reconstruction of 100 million voxels from a single 1-megapixel hologram with a sparsity *** performance bound of our approach is quantified in simulation and validated *** work promises utilization of multiple scattering for versatile large-scale applications.

关键词： multiple scattering digital holography particle localization

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Anatomical Modeling of Brain Vasculature in Two-Photon Microscopy by Generalizable Deep Learning

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Biomedical Engineering Frontiers 2021年第1期2卷 103-114页

作者： waleed tahir Sreekanth Kura Jiabei Zhu Xiaojun Cheng Rafat Damseh Fetsum Tadesse Alex Seibel Blaire S.Lee Frédéric Lesage Sava Sakadžic David A.Boas Lei Tian Department of Electrical and Computer Engineering Boston UniversityBostonMAUSA Department of Biomedical Engineering Boston UniversityBostonMAUSA Biomedical Engineering Institute École Polytechnique de MontréalMontréalQCCanada Institute of Neurological Sciences and Psychiatry Hacettepe UniversityAnkaraTurkey Department of Radiology Massachusetts General HospitalHarvard Medical SchoolCharlestownUSA Neurophotonics Center Boston UniversityBostonMAUSA

Objective and Impact *** of blood vessels from two-photon microscopy(2PM)angiograms of brains has important applications in hemodynamic analysis and disease ***,we develop a generalizable deep learning technique for accurate 2PM vascular segmentation of sizable regions in mouse brains acquired from multiple 2PM *** technique is computationally efficient,thus ideal for large-scale neurovascular *** segmentation from 2PM angiograms is an important first step in hemodynamic modeling of brain *** segmentation methods based on deep learning either lack the ability to generalize to data from different imaging systems or are computationally infeasible for large-scale *** this work,we overcome both these limitations by a method that is generalizable to various imaging systems and is able to segment large-scale *** employ a computationally efficient deep learning framework with a loss function that incorporates a balanced binary-cross-entropy loss and total variation regularization on the network’s *** effectiveness is demonstrated on experimentally acquired in vivo angiograms from mouse brains of dimensions up to 808×808×702μ*** demonstrate the superior generalizability of our framework,we train on data from only one 2PM microscope and demonstrate high-quality segmentation on data from a different microscope without any network ***,our method demonstrates 10×faster computation in terms of voxels-segmented-per-second and 3×larger depth compared to the *** work provides a generalizable and computationally efficient anatomical modeling framework for brain vasculature,which consists of deep learning-based vascular segmentation followed by *** paves the way for future modeling and analysis of hemodynamic response at much greater scales that were inaccessible before.

关键词： Deep network overcome

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