Localization and Tracking in 4D Fluorescence Microscopy Imagery

Abousamra, Shahira; Adar, Shai; Elia, Natalie; Shilkrot, Roy

doi:10.1109/cvprw.2018.00306

Cited by 4 publications

(6 citation statements)

References 34 publications

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“…Any clustering method can be used to perform this subdivision. Another random number p sp c ∈ [0, 1] Registration approaches can be principally distinguished in feature-based and intensity-based schemes [2]:…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

“…This semantic segmentation approach was assessed on the segmentation problem of cell membrane and nucleus. Abousamra et al [2] proposed a CNN-based architecture for localization, classification, and tracking in 4D fluorescence microscopy imaging. In particular, this CNN-based approach was tested in the case of microtubule fibers' bridge formation during the cell division of zebrafish embryos.…”

Section: Fluorescence Microscopymentioning

confidence: 99%

“…2 Comparison among the developed brain lesion segmentation methods on the same 15 patients treated with Gamma Knife neuro-radiosurgery. The results are expressed as average value ± standard deviation.…”

mentioning

confidence: 99%

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MedGA: A novel evolutionary method for image enhancement in medical imaging systems

Rundo

Tangherloni

Nobile

et al. 2019

Expert Systems with Applications

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Medical imaging systems often require the application of image enhancement techniques to help physicians in anomaly/abnormality detection and diagnosis, as well as to improve the quality of images that undergo automated image processing. In this work we introduce MedGA, a novel image enhancement method based on Genetic Algorithms that is able to improve the appearance and the visual quality of images characterized by a bimodal gray level intensity histogram, by strengthening their two underlying sub-distributions. MedGA can be exploited as a pre-processing step for the enhancement of images with a nearly bimodal histogram distribution, to improve the results achieved by downstream image processing techniques. As a case study, we use MedGA as a clinical expert system for contrast-enhanced Magnetic Resonance image analysis, considering Magnetic Resonance guided Focused Ultrasound Surgery for uterine fibroids. The performances of MedGA are quantitatively evaluated by means of various image enhancement metrics, and compared against the conventional state-of-the-art image enhancement techniques, namely, histogram

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Section: Particle Swarm Optimizationmentioning

confidence: 99%

Section: Fluorescence Microscopymentioning

confidence: 99%

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MedGA: A novel evolutionary method for image enhancement in medical imaging systems

Rundo

Tangherloni

Nobile

et al. 2019

Expert Systems with Applications

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show abstract

“…A critical challenge in using fluorescence microscopy images is the presence of noise. Denoising filters, such as the Gaussian filter, average filter, etc., are widely employed to reduce such noises [10]- [12]. In the literature, diverse cell and nuclei segmentation methods have been studied, including active contour, watershed, and thresholding methods, as well as deep neural network methods.…”

Section: Related Workmentioning

confidence: 99%

“…Tracking is a complex processing, following object detection and segmentation. The popular cell or particle tracking methods can be divided into three categories: Nearest neighbor [29]- [33], Kalman filter [34]- [36] data association [34], [35], and deep learning [7], [10]. The nearest neighbor method is the simplest approach, which links every segmented object to the nearest object in the next frame [37].…”

Section: Related Workmentioning

confidence: 99%

Multi-Object Portion Tracking in 4D Fluorescence Microscopy Imagery With Deep Feature Maps

Jiao

Weng

Yang

2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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3D fluorescence microscopy of living organisms has increasingly become an essential and powerful tool in biomedical research and diagnosis. An exploding amount of imaging data has been collected, whereas efficient and effective computational tools to extract information from them are still lagging behind. This is largely due to the challenges in analyzing biological data. Interesting biological structures are not only small, but are often morphologically irregular and highly dynamic. Although tracking cells in live organisms has been studied for years, existing tracking methods for cells are not effective in tracking subcellular structures, such as protein complexes, which feature in continuous morphological changes including split and merge, in addition to fast migration and complex motion. In this paper, we first define the problem of multi-object portion tracking to model the protein object tracking process. A multi-object tracking method with portion matching is proposed based on 3D segmentation results. The proposed method distills deep feature maps from deep networks, then recognizes and matches objects' portions using an extended search. Experimental results confirm that the proposed method achieves 2.96% higher on consistent tracking accuracy and 35.48% higher on event identification accuracy than the state-of-art methods.

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Tracking leukocytes in intravital time lapse images using 3D cell association learning network

Moghadam

Chen

2021

Artificial Intelligence in Medicine

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Localization and Tracking in 4D Fluorescence Microscopy Imagery

Cited by 4 publications

References 34 publications

MedGA: A novel evolutionary method for image enhancement in medical imaging systems

MedGA: A novel evolutionary method for image enhancement in medical imaging systems

Multi-Object Portion Tracking in 4D Fluorescence Microscopy Imagery With Deep Feature Maps

Tracking leukocytes in intravital time lapse images using 3D cell association learning network

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