Multi-layer segmentation framework for cell nuclei using improved GVF Snake model, Watershed, and ellipse fitting

Jia, Dongfang; Zhang, Chuanwang; Wu, Nengkai; Guo, Zhigang; Ge, Hairui

doi:10.1016/j.bspc.2021.102516

Cited by 25 publications

(20 citation statements)

References 45 publications

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“…Further, we used the entire cell edge and not just the edge where cells connect or are in close proximity to nudge the optimizer in order to improve the separation between cell edges of neighboring cells. The architecture of our UNET model is similar to previous reports with a few variations in depth as well as implementation, as shown in Figure 5 [29][30][31][32][33] .…”

Section: Cell Segmentationmentioning

confidence: 60%

Towards a comprehensive approach for characterizing cell activity in low contrast microscopic images

Baar

Kuragano

Tokuraku

et al. 2022

Preprint

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When studying physical cellular response observed by light microscopy, variations in cell behavior are difficult to quantitatively measure and are often only discussed on a subjective level. Hence, cell properties are described qualitatively based on a researcher’s impressions. In this study, we aim to define a comprehensive approach to estimate the physical cell activity based on migration and morphology based on statistical analysis of a cell population within a predefined field of view and timespan. We present quantitative measurements of the influence of drugs such as cytochalasin D and taxol on human neuroblastoma, SH-SY5Y cell populations. Both chemicals are well known to interact with the cytoskeleton and affect the cell morphology and motility. Being able to compute the physical properties of each cell for a given observation time, requires precise localization of each cell even when in an adhesive state. Also, the risk of confusion through contaminants is desired to be minimized. In relation to the cell detection process, we have developed a customized encoder-decoder based deep learning cell detection and tracking procedure. Further, we discuss the accuracy of our approach to quantify cell activity and its viability in regard to the cell detection accuracy.

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Section: Cell Segmentationmentioning

confidence: 60%

Towards a comprehensive approach for characterizing cell activity in low contrast microscopic images

Baar

Kuragano

Tokuraku

et al. 2022

Preprint

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“…The convex hull algorithm [50] fails to achieve the desired ideal effect when inpainting the lung parenchyma template of large area lesions. The rolling ball method cannot accurately determine the size of the defect when repairing the edge of the lung parenchyma template, and it is difficult to accurately set the radius of the sphere.…”

Section: Refinement Segmentation Of Lung Parenchymamentioning

confidence: 99%

Two-dimensional reciprocal cross entropy multi-threshold combined with improved firefly algorithm for lung parenchyma segmentation of COVID-19 CT image

Guo-wei

Guo

Han

et al. 2022

Biomedical Signal Processing and Control

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“…The canny edge detection technique is used to detect the edges of the pupil and the iris. The circular Hough transform technique (19) is then used to detect the radius and diameter of the pupil. For the calculation of the radius of a circle, R is known.…”

Section: Feature Extraction Of Pupil Ratiomentioning

confidence: 99%

Real Time Glaucoma Prediction Using Y-UNet Classifier via Hardware Software Co-Design SMART System

Priya¹,

Sathiaseelan²

2022

IJST

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Objectives: To present a real-time algorithm that combines Yolov5 and UNetbased CNN predictions to classify small-sized images, particularly medical images. Methods: The proposed model combines the various phases of preprocessing, object detection, segmentation and classification through Kalman filter, Yolov5, U-net based on CNN. The model is derived from the three different datasets to create a novel classification algorithm for medical data. The dataset contains images of 136 glaucoma patients and 187 healthy images. Findings: The proposed Y-UNet classifier framework is used to classify glaucoma images based on their IOP and CCT from real-time dataset collection.The proposed framework accuracy of 98.75% is achieved in the run time performance at 0.18 seconds per image. The accuracy is higher by 1.66% and runtime performance reduced by 0.03 seconds when compared with standard classification methods. Additionally, threshold optimizer has minimized the overall losses and provides the most accurate result. Novelty: The Y-UNet classifier model proposed here enables the real-time glaucoma prediction tasks to be carried out through the use of a hardware-based system known as the Sensitive Mirror Analyzer and Retina Tracker. To the best of our knowledge, for the first time, the proposed algorithm has been implemented to improve the accuracy and run time performance.

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Multi-layer segmentation framework for cell nuclei using improved GVF Snake model, Watershed, and ellipse fitting

Cited by 25 publications

References 45 publications

Towards a comprehensive approach for characterizing cell activity in low contrast microscopic images

Towards a comprehensive approach for characterizing cell activity in low contrast microscopic images

Two-dimensional reciprocal cross entropy multi-threshold combined with improved firefly algorithm for lung parenchyma segmentation of COVID-19 CT image

Real Time Glaucoma Prediction Using Y-UNet Classifier via Hardware Software Co-Design SMART System

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