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Deconvolution

Deconvolution

Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...

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Updated: Jun 11, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

在OCT使用深度学习进行概率学体积光斑抑制.

Bhaskara Rao Chintada^1,2, Sebastián Ruiz-Lopera^1,3, René Restrepo⁴

¹Wellman Center for Photomedicine, Massachusetts General Hospital, Boston, MA 02114, USA.

Biomedical optics express

|September 30, 2024

概括

此摘要是机器生成的。

我们开发了一种快速的深度学习方法,用于减少光学一致性断层扫描 (OCT) 卷中的斑点. 这种AI框架有效地消除噪音,同时保留图像细节,改善各种组织的可视化.

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Longitudinal Morphological and Physiological Monitoring of Three-dimensional Tumor Spheroids Using Optical Coherence Tomography

Longitudinal Morphological and Physiological Monitoring of Three-dimensional Tumor Spheroids Using Optical Coherence Tomography

Published on: February 9, 2019

Automated 3D Optical Coherence Tomography to Elucidate Biofilm Morphogenesis Over Large Spatial Scales

Automated 3D Optical Coherence Tomography to Elucidate Biofilm Morphogenesis Over Large Spatial Scales

Published on: August 21, 2019

相关实验视频

Last Updated: Jun 11, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Longitudinal Morphological and Physiological Monitoring of Three-dimensional Tumor Spheroids Using Optical Coherence Tomography

Longitudinal Morphological and Physiological Monitoring of Three-dimensional Tumor Spheroids Using Optical Coherence Tomography

Published on: February 9, 2019

Automated 3D Optical Coherence Tomography to Elucidate Biofilm Morphogenesis Over Large Spatial Scales

Automated 3D Optical Coherence Tomography to Elucidate Biofilm Morphogenesis Over Large Spatial Scales

Published on: August 21, 2019

科学领域:

医疗成像医学成像
人工智能的人工智能
生物医学工程生物医学工程

背景情况:

光学连贯断层扫描 (OCT) 中的斑点噪声降低了图像质量,并阻碍了准确的分析.
开发有效的斑点减少技术对于推进OCT在各种生物医学领域的应用至关重要.

研究的目的:

引入一个新的深度学习框架,以减少海外国家和地区数据中的体积斑点.
为了利用海上国家和地区数据的体积性质,提高斑点抑制和分辨率保存.

主要方法:

一个有条件的生成对抗网络 (cGAN) 被设计用于处理部分的OCT卷.
卷度非局部手段 (TNode) 用于为cGAN生成高质量的训练数据.
该框架是以最小的数据 (三个OCT卷) 进行培训的,并在不同的OCT系统和组织类型中进行了验证.

主要成果:

在所有三个维度中,cGAN实现了显著的斑点减少和保存的组织结构.
与TNode.相比,拟议的方法证明了与TNode.相比提高了两倍的速度.
该框架在未见的组织类型上表现出有效的性能,突出显示了其可通用性.

结论:

开发的深度学习框架提供了一个快速有效的解决方案,以减少OCT的体积斑点.
开源,全软件的实现促进了广泛采用和适应多样化的OCT系统.
这项工作解决了培训数据生成的挑战,使得在没有广泛的基础真相数据集的情况下能够实现高质量的除.