Segmentation-based tracking of macrophages in 2D＋time microscopy movies inside a living animal

Park, Seol Ah; Sipka, Tamara; Krivá, Zuzana; Lutfalla, Georges; Nguyen-Chi, Maï; Mikula, Karol

doi:10.1016/j.compbiomed.2022.106499

Cited by 4 publications

(6 citation statements)

References 57 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Traditional segmentation algorithms still have a place in the era of rapid development of deep learning algorithms. Part et al [22]proposed a segmentation method employing space-time ltering, local Otsu's thresholding, and the SUBSURF (subjective surface segmentation) method, macrophages can be distinguished from the background under relatively weak background noise interference. Yang et al [23] proposed a LIOtsu thresholding method for segmenting macrophages, which is predicated on the outcomes of a CNN-based classi cation.…”

Section: Macrophage Segmentationmentioning

confidence: 99%

Multi-task Deep Learning Pipeline for Irregular Macrophage Segmentation fusing Enhanced Microscopy Images and Auxiliary Boundary Maps

Zheng,

Lei,

Zhao

et al. 2024

Preprint

View full text Add to dashboard Cite

Background:Accurate segmentation of macrophages from microscope images can be beneficial for understanding viral infection and immune development stages. There are two particularly challenging aspects in this task: (1) over-segmentation of macrophage with pseudopodia; (2)under-segmentation for clustering, overlapping macrophages and unclear boundary macrophages. Method: This paper proposes a microscope image enhancement multi-task deep learning framework to achieve segmentation of macrophages with complex boundary conditions. The network initially utilizes PENet to enhance the quality of microscope image data, improving the overall image quality. Then, a multi-task U-Net architecture is employed to extract crucial feature information from masks, distance transforms, and heatmaps. By utilizing the cell segmentation achieved through masks, the distance transforms and heatmaps are used to further refine and capture the intricate boundary details of macrophages, including pseudopodia and other irregularities. Results: Despite the challenges posed by partially or entirely obscured cells, the network demonstrates robust segmentation capabilities for surface-visible cells, achieving an accuracy of 61.24%, a precision of 78.79%, and a recall of 87.93%, outperforming some other segmentation networks, including SOTA Cellpose. Through experiments, it is possible to achieve precise segmentation of irregular boundaries and narrow pseudopodia of macrophages in low-quality microscope images. Conclusions: Compared to current macrophage segmentation techniques, this network has two significant advantages: (1) supplementing rich boundary detail information to capture the microscopic features of macrophageswith elongated pseudopods; (2) enhancing underexposed cells due to limitations of microscopic imaging techniques and capturing their potential information.

show abstract

Section: Macrophage Segmentationmentioning

confidence: 99%

Multi-task Deep Learning Pipeline for Irregular Macrophage Segmentation fusing Enhanced Microscopy Images and Auxiliary Boundary Maps

Zheng,

Lei,

Zhao

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…TrackMate is an ImageJ plugin developed by Tinevez et al for particle tracking based on the particle pixel size [42]. Although it was built for particle tracking, in application, it has also been used for tracking blood cells [43,44], lysosomes [45,46], and even Drosophila movement [47,48].…”

Section: Tools Description Advantage References Ebird and Citizen Sci...mentioning

confidence: 99%

Performance Comparison of Five Methods Available in ImageJ for Bird Counting and Detection from Video Datasets

Kurnia,

Saputra,

Luong

et al. 2024

Inventions

View full text Add to dashboard Cite

Bird monitoring is an important approach to studying the diversity and abundance of birds, especially during migration, as it can provide core data for bird conservation purposes. The previous methods for bird number estimation are largely based on manual counting, which suffers from low throughput and a high error rate. In this study, we aimed to provide an alternative bird-counting method from video datasets by using five available ImageJ methods: Particle Analyzer, Find Maxima, Watershed segmentation, TrackMate, and trainable WEKA segmentation. The numbers of birds and their XY coordinates were extracted from videos to conduct a side-by-side comparison with the manual counting results, and the three important criteria of the sensitivity, precision, and F1 score were calculated for the performance evaluation. From the tests, which we conducted for four different cases with different bird numbers or flying patterns, TrackMate had the best overall performance for counting birds and pinpointing their locations, followed by Particle Analyzer, Find Maxima, WEKA, and lastly, Watershed, which showed low precision in most of the cases. In summary, five ImageJ-based counting methods were compared in this study, and we validated that TrackMate obtains the best performance for bird counting and detection.

show abstract

“…Equation ( 1) can be helpful in different applications where one needs to smooth a curve and remove the noise while keeping the curve similar to the original. In [2,11,12], models have been proposed for segmentation and tracking of immune system cells. Once these trajectories are obtained, one may be interested in finding the actual velocity of the cells (see for example [4]).…”

Section: Mathematical Modelmentioning

confidence: 99%

“…We applied the proposed model for smoothing macrophage trajectories. Our goal was to derive the actual velocity of the cell starting from the curve found connecting the cell center in every frame of a 2D video [12]. Consequently, we needed to remove the random motion of the cell and correct possible imperfections in cell centers detection while keeping the final trajectory as close to the original as possible.…”

Section: Mathematical Modelmentioning

confidence: 99%

Macrophages Trajectories Smoothing by Evolving Curves

Lupi,

Mikula,

Park

2023

Tatra Mountains Mathematical Publications

Self Cite

View full text Add to dashboard Cite

When analyzing cell trajectories, we often have to deal with noisy data due to the random motion of the cells and possible imperfections in cell center detection. To smooth these trajectories, we present a mathematical model and numerical method based on evolving open-plane curve approach in the Lagrangian formulation. The model contains two terms: the first is the smoothing term given by the influence of local curvature, while the other attracts the curve to the original trajectory. We use the flowing finite volume method to discretize the advection-diffusion partial differential equation. The PDE includes the asymptotically uniform tangential redistribution of curve grid points. We present results for macrophage trajectory smoothing and define a method to compute the cell velocity for the discrete points on the smoothed curve.

show abstract

Segmentation-based tracking of macrophages in 2D＋time microscopy movies inside a living animal

Cited by 4 publications

References 57 publications

Multi-task Deep Learning Pipeline for Irregular Macrophage Segmentation fusing Enhanced Microscopy Images and Auxiliary Boundary Maps

Multi-task Deep Learning Pipeline for Irregular Macrophage Segmentation fusing Enhanced Microscopy Images and Auxiliary Boundary Maps

Performance Comparison of Five Methods Available in ImageJ for Bird Counting and Detection from Video Datasets

Macrophages Trajectories Smoothing by Evolving Curves

Contact Info

Product

Resources

About