Comparative study of techniques for brain tumor segmentation

Tulsani, Hemant; Saxena, Saransh; Bharadwaj, Mamta

doi:10.1109/mspct.2013.6782100

Cited by 5 publications

(2 citation statements)

References 5 publications

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“…A frequent question in digital cell counting is how a protocol rationalizes occluded cells or groups of cells with indistinguishable borders. Implementations can be found where researchers have used more advanced computational methods and counting strategies such as youonly-look-once (YOLO) convolutional networks [25,26,63], outlier rejection based on cell size or fluorescence intensity and watershed segmentation to define cell borders [64][65][66] which can yield improvements and high accuracy in terms of resolving individual cells. In our case, making use of the high parallelization of our imaging, we only require an estimation of cell count over a population and not an absolute value on a per fish basis and so further counting methods have not been found necessary.…”

Section: Discussionmentioning

confidence: 99%

Automated, high-throughput quantification of EGFP-expressing neutrophils in zebrafish by machine learning and a highly-parallelized microscope

Efromson,

Ferrero,

Bègue

et al. 2023

PLoS ONE

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Normal development of the immune system is essential for overall health and disease resistance. Bony fish, such as the zebrafish (Danio rerio), possess all the major immune cell lineages as mammals and can be employed to model human host response to immune challenge. Zebrafish neutrophils, for example, are present in the transparent larvae as early as 48 hours post fertilization and have been examined in numerous infection and immunotoxicology reports. One significant advantage of the zebrafish model is the ability to affordably generate high numbers of individual larvae that can be arrayed in multi-well plates for high throughput genetic and chemical exposure screens. However, traditional workflows for imaging individual larvae have been limited to low-throughput studies using traditional microscopes and manual analyses. Using a newly developed, parallelized microscope, the Multi-Camera Array Microscope (MCAM™), we have optimized a rapid, high-resolution algorithmic method to count fluorescently labeled cells in zebrafish larvae in vivo. Using transgenic zebrafish larvae, in which neutrophils express EGFP, we captured 18 gigapixels of images across a full 96-well plate, in 75 seconds, and processed the resulting datastream, counting individual fluorescent neutrophils in all individual larvae in 5 minutes. This automation is facilitated by a machine learning segmentation algorithm that defines the most in-focus view of each larva in each well after which pixel intensity thresholding and blob detection are employed to locate and count fluorescent cells. We validated this method by comparing algorithmic neutrophil counts to manual counts in larvae subjected to changes in neutrophil numbers, demonstrating the utility of this approach for high-throughput genetic and chemical screens where a change in neutrophil number is an endpoint metric. Using the MCAM™ we have been able to, within minutes, acquire both enough data to create an automated algorithm and execute a biological experiment with statistical significance. Finally, we present this open-source software package which allows the user to train and evaluate a custom machine learning segmentation model and use it to localize zebrafish and analyze cell counts within the segmented region of interest. This software can be modified as needed for studies involving other zebrafish cell lineages using different transgenic reporter lines and can also be adapted for studies using other amenable model species.

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Section: Discussionmentioning

confidence: 99%

Automated, high-throughput quantification of EGFP-expressing neutrophils in zebrafish by machine learning and a highly-parallelized microscope

Efromson,

Ferrero,

Bègue

et al. 2023

PLoS ONE

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“…Однако в большинстве работ, посвященных исследования форменных элементов крови методами статической цифровой микроскопии, в качестве препарата выступает не раствор нативной крови, а окрашенные мазки [9][10][11][12][13][14]. Такой подход позволяет определять лейкоцитарную формулу, а также количественное соотношение между форменными элементами различных типов, но не позволяет определять их концентрацию.…”

Section: автоматизация статической цифровой микроскопииunclassified

Идентификация И Счет Эритроцитов Нативной Донорской Крови Человека Методом Цифровой Оптической Микроскопии С Использованием Спектрально Фильтрованного Освещения

Dubrovskii¹,

Забенков²,

Karpocheva³

et al. 2021

Оптика И Спектроскопия

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The possibility of identification and counting of native donor blood erythrocytes based by static, non-flow digital optical microscopy was studied. The object of study was the whole donor blood diluted by saline and placed into Goryaev's counting chamber. The sample was examined in transmitted light by a Lumam P-8 digital optical microscope equipped by a Basler acA920-40um camera. In order to identify erythrocytes by spectral characteristics two sets of micrographs of 20 pieces in each were obtained. In the first set there was no optical filter in the illumination channel of the microscope and in the second set an interference filter for a 420 nm wavelength with a 10 nm bandwidth was used. The characteristics of the interference filter were chosen to come more close to the Soret band of the RBC hemoglobin to obtain the best contrast for photographic images. The technique for automated analysis of micrographs using the OpenCV software was developed to recognize erythrocytes and to carry out their counting. The results of the computer RBC counting were compared with the manually counted red blood cells. It was found that for computer approach accepted the proportion of the erythrocytes identified and counted in averages was 97-98%.

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Nonlinear Tensor Diffusion Filter Based Marker-Controlled Watershed Segmentation for CT/MR Images

Kumar

Fred²,

Kumar³

et al. 2017

Proceedings of International Conference on Computational Intelligence and Data Engineering

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Comparative study of techniques for brain tumor segmentation

Cited by 5 publications

References 5 publications

Automated, high-throughput quantification of EGFP-expressing neutrophils in zebrafish by machine learning and a highly-parallelized microscope

Automated, high-throughput quantification of EGFP-expressing neutrophils in zebrafish by machine learning and a highly-parallelized microscope

Nonlinear Tensor Diffusion Filter Based Marker-Controlled Watershed Segmentation for CT/MR Images

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