Blood cell detection and counting in holographic lens-free imaging by convolutional sparse dictionary learning and coding

Yellin, Florence; Haeffele, Benjamin D.; Vidal, René

doi:10.1109/isbi.2017.7950604

Cited by 14 publications

(8 citation statements)

References 9 publications

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“…In addition to the optimal thresholding baseline, we also consider the template matching (TM) baseline algorithm described in [11]. Here, we first convert the complex valued reconstruction to an absolute valued image and then use platelet templates which consist of symmetric 2D-Gaussians.…”

Section: Baseline Methods and Resultsmentioning

confidence: 99%

“…These advantages have led to LFI being explored as a potential solution for various applications in biomedical microscopy in resource-limited settings [4,5], such as semen [6] and hematological analysis [7][8][9][10][11][12]. Likewise, in this work we are interested in exploiting these advantages of LFI systems to develop a compact and low-cost system that is capable of measuring the concentration of platelets in human blood.…”

Section: Introductionmentioning

confidence: 99%

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Generative optical modeling of whole blood for detecting platelets in lens-free images

Haeffele

Pick

Lin

et al. 2020

Biomed. Opt. Express

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In this paper, we consider the task of detecting platelets in images of diluted whole blood taken with a lens-free microscope. Despite having several advantages over traditional microscopes, lens-free imaging systems have the significant challenge that the resolution of the system is typically limited by the pixel dimensions of the image sensor. As a result of this limited resolution, detecting platelets is very difficult even by manual inspection of the images due to the fact that platelets occupy just a few pixels of the reconstructed image. To address this challenge, we develop an optical model of diluted whole blood to generate physically realistic simulated holograms suitable for training machine learning models in a supervised manner. We then use this model to train a convolutional neural network (CNN) for platelet detection and validate our approach by developing a novel optical configuration which allows collecting both lens-free and fluorescent microscopy images of the same field of view of diluted whole blood samples with fluorescently labeled platelets.

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Section: Baseline Methods and Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Generative optical modeling of whole blood for detecting platelets in lens-free images

Haeffele

Pick

Lin

et al. 2020

Biomed. Opt. Express

Self Cite

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“…However, ADMM algorithms can be slow to reach a convergence, yielding suboptimal reconstruction performances as the feasible point is not directly optimized but results from a consensus. Yellin et al [35] proposed to adapt the K-SVD dictionary update method [1] to the convolutional setting but this technique is adapted to an 0 penalty for the activation Z. It also requires to compute costly singular value decompositions (SVD).…”

Section: Related Solversmentioning

confidence: 99%

“…Beyond denoising or inpainting, recent applications used this shiftinvariant model as a way to learn and localize relevant patterns in signals or images. Yellin et al [35] used it to count the number of blood-cells present in holographic lens-free images. Jas et al [15] and Dupré la Tour et al [10] learned recurring patterns in univariate and multivariate signals from neuroscience.…”

Section: Introductionmentioning

confidence: 99%

DiCoDiLe: Distributed Convolutional Dictionary Learning

Moreau

Gramfort

2022

IEEE Trans. Pattern Anal. Mach. Intell.

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Convolutional dictionary learning (CDL) estimates shift invariant basis adapted to represent signals or images. CDL has proven useful for image denoising or inpainting, as well as for pattern discovery on multivariate signals. Contrarily to standard patch-based dictionary learning, patterns estimated by CDL can be positioned anywhere in signals or images. Optimization techniques consequently face the difficulty of working with extremely large inputs with millions of pixels or time samples. To address this optimization problem, we propose a distributed and asynchronous algorithm, employing locally greedy coordinate descent and a soft-locking mechanism that does not require a central server. Computation can be distributed on a number of workers which scales linearly with the size of the data. The parallel computation accelerates the parameter estimation and the distributed setting allows our algorithm to be used with data that do not fit into a single computer's RAM. Experiments confirm the theoretical scaling properties of the algorithm. This allows to demonstrate an improved pattern recovery as images grow in size, and to learn patterns on images from the Hubble Space Telescope containing tens of millions of pixels.

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“…Classification of red blood cell images using spectral angle mapping (SAM) [36] is proposed; SAM leverages support vector machine (SVM) and establishes a standard red blood cell model for matching the red blood cells. The approach proposed by [37] takes convolution lexical learning and coding for red blood cell detection and counting in holographic lens imaging. Two types of single-frame processing based on machine learning [38] are proposed for lensless red blood cell counting, i.e., based on extreme learning machines and convolutional neural networks.…”

Section: Related Workmentioning

confidence: 99%

Toward Automatic Cardiomyocyte Clustering and Counting through Hesitant Fuzzy Sets

et al. 2019

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The isolation and observation of cardiomyocytes serve as the fundamental approach to cardiovascular research. The state-of-the-practice for the isolation and observation relies on manual operation of the entire culture process. Such a manual approach not only incurs high human errors, but also takes a long period of time. This paper proposes a new computer-aided paradigm to automatically, accurately, and efficiently perform the clustering and counting of cardiomyocytes, one of the key procedures for evaluating the success rate of cardiomyocytes isolation and the quality of culture medium. The key challenge of the proposed method lies in the unique, rod-like shape of cardiomyocytes, which has been hardly addressed in literature. Our proposed method employs a novel algorithm inspired by hesitant fuzzy sets and integrates an efficient implementation into the whole process of analyzing cardiomyocytes. The system, along with the data extracted from adult rats’ cardiomyocytes, has been experimentally evaluated with Matlab, showing promising results. The false accept rate (FAR) and the false reject rate (FRR) are as low as 1.46% and 1.97%, respectively. The accuracy rate is up to 98.7%—20% higher than the manual approach—and the processing time is reduced from tens of seconds to 3–5 s—an order of magnitude performance improvement.

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Blood cell detection and counting in holographic lens-free imaging by convolutional sparse dictionary learning and coding

Cited by 14 publications

References 9 publications

Generative optical modeling of whole blood for detecting platelets in lens-free images

Generative optical modeling of whole blood for detecting platelets in lens-free images

DiCoDiLe: Distributed Convolutional Dictionary Learning

Toward Automatic Cardiomyocyte Clustering and Counting through Hesitant Fuzzy Sets

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