Topic
Image Quantification
About: Image Quantification is a research topic. Over the lifetime, 91 publications have been published within this topic receiving 1251 citations.
Papers published on a yearly basis
Papers
TL;DR: A new framework for efficient recovery of image quality from sparse optoacoustic data based on a deep convolutional neural network is proposed and its performance with whole body mouse imaging in vivo is demonstrated.
Abstract: The rapidly evolving field of optoacoustic (photoacoustic) imaging and tomography is driven by a constant need for better imaging performance in terms of resolution, speed, sensitivity, depth and contrast. In practice, data acquisition strategies commonly involve sub-optimal sampling of the tomographic data, resulting in inevitable performance trade-offs and diminished image quality. We propose a new framework for efficient recovery of image quality from sparse optoacoustic data based on a deep convolutional neural network and demonstrate its performance with whole body mouse imaging in vivo. To generate accurate high-resolution reference images for optimal training, a full-view tomographic scanner capable of attaining superior cross-sectional image quality from living mice was devised. When provided with images reconstructed from substantially undersampled data or limited-view scans, the trained network was capable of enhancing the visibility of arbitrarily oriented structures and restoring the expected image quality. Notably, the network also eliminated some reconstruction artefacts present in reference images rendered from densely sampled data. No comparable gains were achieved when the training was performed with synthetic or phantom data, underlining the importance of training with high-quality in vivo images acquired by full-view scanners. The new method can benefit numerous optoacoustic imaging applications by mitigating common image artefacts, enhancing anatomical contrast and image quantification capacities, accelerating data acquisition and image reconstruction approaches, while also facilitating the development of practical and affordable imaging systems. The suggested approach operates solely on image-domain data and thus can be seamlessly applied to artefactual images reconstructed with other modalities. Optoacoustic imaging can achieve high spatial and temporal resolution but image quality is often compromised by suboptimal data acquisition. A new method employing deep learning to recover high-quality images from sparse or limited-view optoacoustic scans has been developed and demonstrated for whole-body mouse imaging in vivo.
260 citations
TL;DR: The currently available optoacoustic image reconstruction and quantification approaches are assessed, including back-projection and model-based inversion algorithms, sparse signal representation, wavelet-based approaches, methods for reduction of acoustic artifacts as well as multi-spectral methods for visualization of tissue bio-markers.
Abstract: This paper comprehensively reviews the emerging topic of optoacoustic imaging from the image reconstruction and quantification perspective. Optoacoustic imaging combines highly attractive features, including rich contrast and high versatility in sensing diverse biological targets, excellent spatial resolution not compromised by light scattering, and relatively low cost of implementation. Yet, living objects present a complex target for optoacoustic imaging due to the presence of a highly heterogeneous tissue background in the form of strong spatial variations of scattering and absorption. Extracting quantified information on the actual distribution of tissue chromophores and other biomarkers constitutes therefore a challenging problem. Image quantification is further compromised by some frequently-used approximated inversion formulae. In this review, the currently available optoacoustic image reconstruction and quantification approaches are assessed, including back-projection and model-based inversion algorithms, sparse signal representation, wavelet-based approaches, methods for reduction of acoustic artifacts as well as multi-spectral methods for visualization of tissue bio-markers. Applicability of the different methodologies is further analyzed in the context of real-life performance in small animal and clinical in-vivo imaging scenarios.
209 citations
TL;DR: This work outlines and gives entry points to the literature on approaches to quantification such as segmentation, tracking, automated classification and data visualization, and pays particular attention to the distinction between and application of concrete quantification measures such as number of objects in a cell, and abstract measuressuch as texture.
Abstract: Fluorescent microscope imaging technologies have developed at a rapid pace in recent years. High-throughput 2D fluorescent imaging platforms are now in wide use and are being applied on a proteome wide scale. Multiple fluorophore 3D imaging of live cells is being used to give detailed localization and subcellular structure information. Further, 2D and 3D video microscopy are giving important insights into the dynamics of protein localization and transport. In parallel with these developments, significant research has gone into developing new methodologies for quantifying and extracting meaning from the imaging data. Here we outline and give entry points to the literature on approaches to quantification such as segmentation, tracking, automated classification and data visualization. Particular attention is paid to the distinction between and application of concrete quantification measures such as number of objects in a cell, and abstract measures such as texture.
148 citations
TL;DR: The vast majority of microscopic data in biology of the cell nucleus is currently collected using fluorescence microscopy, and most of these data are subsequently subjected to quantitative analysis.
Abstract: The vast majority of microscopic data in biology of the cell nucleus is currently collected using fluorescence microscopy, and most of these data are subsequently subjected to quantitative analysis. The analysis process unites a number of steps, from image acquisition to statistics, and at each of these steps decisions must be made that may crucially affect the conclusions of the whole study. This often presents a really serious problem because the researcher is typically a biologist, while the decisions to be taken require expertise in the fields of physics, computer image analysis, and statistics. The researcher has to choose between multiple options for data collection, numerous programs for preprocessing and processing of images, and a number of statistical approaches. Written for biologists, this article discusses some of the typical problems and errors that should be avoided. The article was prepared by a team uniting expertise in biology, microscopy, image analysis, and statistics. It considers the options a researcher has at the stages of data acquisition (choice of the microscope and acquisition settings), preprocessing (filtering, intensity normalization, deconvolution), image processing (radial distribution, clustering, co-localization, shape and orientation of objects), and statistical analysis.
103 citations
TL;DR: This paper presents their CT-based collagen quantification software platform with a focus on new features and also giving a detailed description of curvelet-based fiber representation, and presents a validation of the tracking of individual fibers and fiber orientations by using synthesized fibers generated by the synthetic fiber generator.
Abstract: Quantification of fibrillar collagen organization has given new insight into the possible role of collagen topology in many diseases and has also identified candidate image-based bio-markers in breast cancer and pancreatic cancer. We have been developing collagen quantification tools based on the curvelet transform (CT) algorithm and have demonstrated this to be a powerful multiscale image representation method due to its unique features in collagen image denoising and fiber edge enhancement. In this paper, we present our CT-based collagen quantification software platform with a focus on new features and also giving a detailed description of curvelet-based fiber representation. These new features include C++-based code optimization for fast individual fiber tracking, Java-based synthetic fiber generator module for method validation, automatic tumor boundary generation for fiber relative quantification, parallel computing for large-scale batch mode processing, region-of-interest analysis for user-specified quantification, and pre- and post-processing modules for individual fiber visualization. We present a validation of the tracking of individual fibers and fiber orientations by using synthesized fibers generated by the synthetic fiber generator. In addition, we provide a comparison of the fiber orientation calculation on pancreatic tissue images between our tool and three other quantitative approaches. Lastly, we demonstrate the use of our software tool for the automatic tumor boundary creation and the relative alignment quantification of collagen fibers in human breast cancer pathology images, as well as the alignment quantification of in vivo mouse xenograft breast cancer images.
59 citations
Related Topics (5)
Performance Metrics
| Year | Papers |
|---|---|
| 2021 | 5 |
| 2020 | 6 |
| 2019 | 12 |
| 2018 | 5 |
| 2017 | 3 |
| 2016 | 3 |