PurposeThe adoption of emerging imaging technologies in the medical community is often hampered when they provide a new unfamiliar contrast that requires experience to be interpreted. Dynamic full-field optical coherence tomography (D-FF-OCT) microscopy is such an emerging technique. It provides fast, high-resolution images of excised tissues with a contrast comparable to H&E histology but without any tissue preparation and alteration.ApproachWe designed and compared two machine learning approaches to support interpretation of D-FF-OCT images of breast surgical specimens and thus provide tools to facilitate medical adoption. We conducted a pilot study on 51 breast lumpectomy and mastectomy surgical specimens and more than 1000 individual 1.3 × 1.3 mm2 images and compared with standard H&E histology diagnosis.ResultsUsing our automatic diagnosis algorithms, we obtained an accuracy above 88% at the image level (1.3 × 1.3 mm2) and above 96% at the specimen level (above cm2).ConclusionsAltogether, these results demonstrate the high potential of D-FF-OCT coupled to machine learning to provide a rapid, automatic, and accurate histopathology diagnosis with minimal sample alteration.
Optical coherence tomography (OCT) has emerged as a promising image modality to characterize biological tissues. With axio-lateral resolutions at the micron-level, OCT images provide detailed morphological information and enable applications such as optical biopsy and virtual histology for clinical needs. Image enhancement is typically required for morphological segmentation, to improve boundary localization, rather than enrich detailed tissue information. We propose to formulate image enhancement as an image simplification task such that tissue layers are smoothed while contours are enhanced. For this purpose, we exploit a Total Variation sparsity-based image reconstruction, inspired by the Compressed Sensing (CS) theory, but specialized for images with structures arranged in layers. We demonstrate the potential of our approach on OCT human heart and retinal images for layers segmentation. We also compare our image enhancement capabilities to the state-of-the-art denoising techniques.
In this work, Compressed Sensing (CS) is investigated as a denoising tool in bioimaging. The denoising algorithm exploits multiple CS reconstructions, taking advantage of the robustness of CS in the presence of noise via regularized reconstructions and the properties of the Fourier transform of bioimages. Multiple reconstructions at low sampling rates are combined to generate high quality denoised images using several sparsity constraints. We present different combination methods for the CS reconstructions and quantitatively compare the performance of our denoising methods to state-of-the-art ones.
In this paper, we investigate how the CS framework can be adapted to biological video microscopy acquisition problems. We first consider a frame-by-frame linear acquisition model in the Fourier domain of the signal, and discuss the relevance of several sparsity models that can be used to drive the reconstruction of the whole video sequence. Then, we switch to a non-linear acquisition model – therefore beyond the “pure” CS framework – in which only the modulus of the Fourier transform of the signal is acquired: by exploiting sparsity properties similar to the one used in the linear acquisition case, we demonstrate the feasibility of a phase retrieval reconstruction procedure applied to video signals.
When undergoing reconstruction through a compressed sensing scheme, real microscopic images are affected
by various artifacts that lead to detail loss. Here, we discuss how the sampling strategy and the subsampling
rate affect the compressed sensing reconstruction, and how they should be determined according to a targeted
accuracy level of the reconstruction. We investigate the relevance and limits of theoretical results through
several numerical reconstructions of test images. We discuss the quantitative and qualitative artifacts that affect
the reconstructed signal when reducing the number of measurements in the Fourier domain. We conclude by
extending our results to real microscopic images.
KEYWORDS: Signal to noise ratio, Denoising, Microscopy, Luminescence, Interference (communication), Image restoration, Image acquisition, Compressed sensing, Molecules, Process control
Noise level and photobleaching are cross-dependent problems in biological fluorescence microscopy. Indeed,
observation of fluorescent molecules is challenged by photobleaching, a phenomenon whereby the fluorophores
are degraded by the excitation light. One way to control this process is by reducing the intensity of the light or the
time exposure, but it comes at the price of decreasing the signal-to-noise ratio (SNR). Although a host of denoising
methods have been developed to increase the SNR, most are post-processing techniques and require full data
acquisition. In this paper we propose a novel technique, based on Compressed Sensing (CS) that simultaneously
enables reduction of exposure time or excitation light level and improvement of image SNR. Our CS-based
method can simultaneously acquire and denoise data, based on statistical properties of the CS optimality, noise
reconstruction characteristics and signal modeling applied to microscopy images with low SNR. The proposed
approach is an experimental optimization combining sequential CS reconstructions in a multiscale framework
to perform image denoising. Simulated and practical experiments on fluorescence image data demonstrate that
thanks to CS denoising we obtain images with similar or increased SNR while still being able to reduce exposure
times. Such results open the gate to new mathematical imaging protocols, offering the opportunity to reduce
photobleaching and help biological applications based on fluorescence microscopy.
This paper presents a new method for computing the Feature-adapted Radon and Beamlet transforms [1] in a
fast and accurate way. These two transforms can be used for detecting features running along lines or piecewise
constant curves. The main contribution of this paper is to unify the Fast Slant Stack method, introduced in
[2], with linear filtering technique in order to define what we call the Feature-adapted Fast Slant Stack. If
the desired feature detector is chosen to belong to the class of steerable filters, our method can be achieved in
O(N log(N)), where N = n2 is the number of pixels. This new method leads to an efficient implementation of
both Feature-adapted Radon and Beamlet transforms, that outperforms our previous works [1] both in terms
of accuracy and speed. Our method has been developed in the context of biological imaging to detect DNA
filaments in fluorescent microscopy.
KEYWORDS: Point spread functions, Microscopy, Luminescence, Confocal microscopy, 3D modeling, Data modeling, Objectives, Data processing, Expectation maximization algorithms, Deconvolution
Despite the availability of rigorous physical models of microscopy point spread functions (PSFs), approximative PSFs, particularly separable Gaussian approximations are widely used in practical microscopic data processing. In fact, compared with a physical PSF model, which usually involves non-trivial terms such as integrals and infinite series, a Gaussian function has the advantage that it is much simpler and can be computed much faster. Moreover, due to its special analytical form, a Gaussian PSF is often preferred to facilitate the analysis of theoretical models such as Fluorescence Recovery After Photobleaching (FRAP) process and of processing algorithms such as EM deconvolution. However, in these works, the selection of Gaussian parameters and the approximation accuracy were rarely investigated. In this paper, we present a comprehensive study of Gaussian approximations for diffraction-limited 2D/3D paraxial/non-paraxial PSFs of Wide Field Fluorescence Microscopy (WFFM), Laser Scanning Confocal Microscopy (LSCM) and Disk Scanning Confocal Microscopy (DSCM) described using the Debye integral. Besides providing an optimal Gaussian parameter for the 2D paraxial WFFM PSF case, we further derive nearly optimal parameters in explicit forms for each of the other cases, based on Maclaurin series matching. Numerical results show that the accuracy of the 2D approximations is very high (Relative Squared Error (RSE) < 2% in WFFM, < 0.3% in LSCM and < 4% in DSCM). For the 3D PSFs, the approximations are average in WFFM (RSE ≃ 16-20%), accurate in DSCM (RSE≃ 3-6%) and nearly perfect in LSCM (RSE ≃ 0.3-0.5%).
Implicit active contour method are a powerful technique for segmentation and tracking of mobile objects such as biological cells observed in videomicroscopy. However, the lack of control on the topology changes in this approach often leads to undesirable contour fusions when previously distinct objects enter in close contact. To overcome this limitation, we propose to modulate the current image by a "ridge" which discourages contour motion towards neighboring objects, thus inhibiting contour fusions. We show applications of this method on both synthetic images and real images from cellular imaging.
The analysis of cell and pathogen movement and motility is a major topic in cell biology for which computerized methods are most needed. This study proposes a method to detect and track multiple moving biological objects in image sequences acquired through fluorescence video microscopy. The method enables the analysis of video microscopy image sequences in order to obtain reliable quantitative data such as number, position, speed and movement phases. The method consists of three stages. A stage of detection is performed through a multi-scale analysis of images using an undecimated wavelet transform. The next stage is the prediction of the state of each detected spot in the next frame using a Kalman filter and an adapted model. Then comes a stage of data association which constructs the tracks and refines the filters. Once all moving objects have been assigned with unique spatio-temporal paths, trajectories are analyzed in terms of different parameters relevant to the motility analysis of biological objects.
We present a method based on active contours to track and segment biological cells in large image sequences obtained by video-microscopy. This task is facilitated by good time resolution and global contrast, but important obstacles are the low contrast boundary deformations known as pseudopods, as well as cell aggregations and divisions. In order to allow better detection of local boundary deformations, we adopted the gradient vector flow (GVF) model of Xu and Prince, which is defined as the steady-state solution of a reaction-diffusion problem. We discuss an undesirable effect of boundary competition in the GVF that can lead to incorrect segmentations for grey-level images. We propose to replace the steady-state solution by a transient solution of the diffusion equation to alleviate this effect, which also allows significant gains in computation time. To enhance pseudopods over texture features, we use a binary edge map obtained from a Canny-Deriche filter followed by hysteresis thresholding. We use topological operators to efficiently detect intersections and maintain contour separation between aggregating cells. Cell divisions are automatically handled by this method. We discuss limits and possible improvements of this work.
We present a number of methods to detect and track multiple moving biological objects in image sequences acquired by different imaging techniques coupled to video microscopy. Movement and motility analysis is an important topic in biology and it is of major importance to be able to analyze the image sequences in order to get reliable and reproducible quantitative data such as number, position, movement phases and speed of the biological objects, as this information helps to characterize the biological assays. The detection is automatic and, in the case of phase contrast microscopy, is based upon the correlation of the image with a filter which varies adaptively to represent an object as it moves and deforms; in fluorescent imaging, the automatic detection is based on thresholding and mathematical morphology to determine and select the objects. The tracking is performed using a Kalman filter and a cost function which enable the position of the moving objects to be predicted, refined and updated. Once all moving objects have been assigned with unique spatio-temporal paths, trajectories are analyzed in terms of different parameters relevant for the motion analysis of biological objects.
We present a method to automatically register images presenting both global and local deformations. The image registration process is performed by exploiting a multi-level/multi- image approach whereby after having wavelet transformed the images, the subband images at different levels are used in a non-feature-based way to determine the motion vectors between the reference and the target images. The crude motion field determined by block matching at the coarsest level of the pyramid is successively refined by taking advantage of both the orientation sensitivity of the different subbands and the contribution of the adjacent levels.
In order to automatically analyze electron immunomicroscopy images, we have developed a computerized scheme for the detection and characterization of nanometer-size metal particles. The method is based upon selectively reconstructing an orthogonal wavelet decomposition of the image through the use of wavelet coefficient thresholding. The method is designed to work irrespective of the background and of the structural content of the image. Results are presented for the analysis of immunogold labelling of muscle tissues.
A segmentation method using a peak analysis algorithm for threshold selection is presented. It is based on the detection of the zero-crossings and the local extrema of a wavelet transform which give a complete characterization of the peaks in the histogram. These values are used for the unsupervised selection of a sequence of thresholds describing a coarse-to-fine analysis of histogram variation. The results of using the proposed technique are presented in the case of different images.
In order to automatically analyze electron immunomicroscopy images, we have developed a computerized scheme for the detection and characterization of nanometer-size metal particles. This scheme consists of a preprocessing step aimed at enhancing image features and of an extraction step which performs the analysis of the particles. The method is designed to work irrespective of background and scene illumination. Results are presented for the analysis of immunogold labelling of muscle tissues.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.