Enhancing Fluorescence Lifetime Parameter Estimation Accuracy with Differential Transformer Based Deep Learning Model Incorporating Pixelwise Instrument Response Function

• URL: http://arxiv.org/abs/2411.16896v2
• Date: Wed, 04 Dec 2024 19:02:23 GMT
• Title: Enhancing Fluorescence Lifetime Parameter Estimation Accuracy with Differential Transformer Based Deep Learning Model Incorporating Pixelwise Instrument Response Function
• Authors: Ismail Erbas, Vikas Pandey, Navid Ibtehaj Nizam, Nanxue Yuan, Amit Verma, Margarida Barosso, Xavier Intes,
• Abstract summary: Fluorescence Lifetime Imaging (FLI) provides unique information about the tissue microenvironment. Recent advancements in Deep Learning have enabled improved fluorescence lifetime parameter estimation. We present MFliNet, a novel DL architecture that integrates the Instrument Response Function (IRF) as an additional input alongside experimental photon time-of-arrival histograms.
• Score: 0.3441582801949978
• License:
• Abstract: Fluorescence Lifetime Imaging (FLI) is a critical molecular imaging modality that provides unique information about the tissue microenvironment, which is invaluable for biomedical applications. FLI operates by acquiring and analyzing photon time-of-arrival histograms to extract quantitative parameters associated with temporal fluorescence decay. These histograms are influenced by the intrinsic properties of the fluorophore, instrument parameters, time-of-flight distributions associated with pixel-wise variations in the topographic and optical characteristics of the sample. Recent advancements in Deep Learning (DL) have enabled improved fluorescence lifetime parameter estimation. However, existing models are primarily designed for planar surface samples, limiting their applicability in translational scenarios involving complex surface profiles, such as \textit{in-vivo} whole-animal or imaged guided surgical applications. To address this limitation, we present MFliNet (Macroscopic FLI Network), a novel DL architecture that integrates the Instrument Response Function (IRF) as an additional input alongside experimental photon time-of-arrival histograms. Leveraging the capabilities of a Differential Transformer encoder-decoder architecture, MFliNet effectively focuses on critical input features, such as variations in photon time-of-arrival distributions. We evaluate MFliNet using rigorously designed tissue-mimicking phantoms and preclinical in-vivo cancer xenograft models. Our results demonstrate the model's robustness and suitability for complex macroscopic FLI applications, offering new opportunities for advanced biomedical imaging in diverse and challenging settings.

Related papers

• FE-UNet: Frequency Domain Enhanced U-Net with Segment Anything Capability for Versatile Image Segmentation [50.9040167152168]
  We experimentally quantify the contrast sensitivity function of CNNs and compare it with that of the human visual system. We propose the Wavelet-Guided Spectral Pooling Module (WSPM) to enhance and balance image features across the frequency domain. To further emulate the human visual system, we introduce the Frequency Domain Enhanced Receptive Field Block (FE-RFB) We develop FE-UNet, a model that utilizes SAM2 as its backbone and incorporates Hiera-Large as a pre-trained block.
  arXiv  Detail & Related papers  (2025-02-06T07:24:34Z)
• Enhanced Confocal Laser Scanning Microscopy with Adaptive Physics Informed Deep Autoencoders [0.0]
  We present a physics-informed deep learning framework to address limitations in Confocal Laser Scanning Microscopy. The model reconstructs high fidelity images from heavily noisy inputs by using convolutional and transposed convolutional layers.
  arXiv  Detail & Related papers  (2025-01-24T18:32:34Z)
• OCR is All you need: Importing Multi-Modality into Image-based Defect Detection System [7.1083241462091165]
  We introduce an external modality-guided data mining framework, primarily rooted in optical character recognition (OCR), to extract statistical features from images. A key aspect of our approach is the alignment of external modality features, extracted using a single modality-aware model, with image features encoded by a convolutional neural network. Our methodology considerably boosts the recall rate of the defect detection model and maintains high robustness even in challenging scenarios.
  arXiv  Detail & Related papers  (2024-03-18T07:41:39Z)
• Optimization of array encoding for ultrasound imaging [2.357055571094446]
  We use machine learning (ML) to construct scanning sequences, parameterized by time delays and apodization weights, that produce high-quality B-mode images. We demonstrate these results experimentally on both wire targets and a tissue-mimicking phantom.
  arXiv  Detail & Related papers  (2024-03-01T05:19:59Z)
• Physics-Driven Turbulence Image Restoration with Stochastic Refinement [80.79900297089176]
  Image distortion by atmospheric turbulence is a critical problem in long-range optical imaging systems. Fast and physics-grounded simulation tools have been introduced to help the deep-learning models adapt to real-world turbulence conditions. This paper proposes the Physics-integrated Restoration Network (PiRN) to help the network to disentangle theity from the degradation and the underlying image.
  arXiv  Detail & Related papers  (2023-07-20T05:49:21Z)
• Optimizations of Autoencoders for Analysis and Classification of Microscopic In Situ Hybridization Images [68.8204255655161]
  We propose a deep-learning framework to detect and classify areas of microscopic images with similar levels of gene expression. The data we analyze requires an unsupervised learning model for which we employ a type of Artificial Neural Network - Deep Learning Autoencoders.
  arXiv  Detail & Related papers  (2023-04-19T13:45:28Z)
• OADAT: Experimental and Synthetic Clinical Optoacoustic Data for Standardized Image Processing [62.993663757843464]
  Optoacoustic (OA) imaging is based on excitation of biological tissues with nanosecond-duration laser pulses followed by detection of ultrasound waves generated via light-absorption-mediated thermoelastic expansion. OA imaging features a powerful combination between rich optical contrast and high resolution in deep tissues. No standardized datasets generated with different types of experimental set-up and associated processing methods are available to facilitate advances in broader applications of OA in clinical settings.
  arXiv  Detail & Related papers  (2022-06-17T08:11:26Z)
• A parameter refinement method for Ptychography based on Deep Learning concepts [55.41644538483948]
  coarse parametrisation in propagation distance, position errors and partial coherence frequently menaces the experiment viability. A modern Deep Learning framework is used to correct autonomously the setup incoherences, thus improving the quality of a ptychography reconstruction. We tested our system on both synthetic datasets and also on real data acquired at the TwinMic beamline of the Elettra synchrotron facility.
  arXiv  Detail & Related papers  (2021-05-18T10:15:17Z)
• Towards an Automatic Analysis of CHO-K1 Suspension Growth in Microfluidic Single-cell Cultivation [63.94623495501023]
  We propose a novel Machine Learning architecture, which allows us to infuse a neural deep network with human-powered abstraction on the level of data. Specifically, we train a generative model simultaneously on natural and synthetic data, so that it learns a shared representation, from which a target variable, such as the cell count, can be reliably estimated.
  arXiv  Detail & Related papers  (2020-10-20T08:36:51Z)
• Spatially-Variant CNN-based Point Spread Function Estimation for Blind Deconvolution and Depth Estimation in Optical Microscopy [6.09170287691728]
  We present a method that improves the resolution of light microscopy images of thin, yet non-flat objects. We estimate the parameters of a spatially-variant Point-Spread function (PSF) model using a Convolutional Neural Network (CNN) Our method recovers PSF parameters from the image itself with up to a squared Pearson correlation coefficient of 0.99 in ideal conditions.
  arXiv  Detail & Related papers  (2020-10-08T14:20:16Z)
• Learning to Model and Calibrate Optics via a Differentiable Wave Optics Simulator [27.913052825303097]
  We present a novel learning-based method to build a differentiable computational model of a real fluorescence microscope. Our model can be used to calibrate a real optical setup directly from data samples and to engineer point spread functions by specifying the desired input-output data.
  arXiv  Detail & Related papers  (2020-05-18T10:23:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.