Cells detection using segmentation competition

Poulain, Emmanuelle; Prigent, Sylvain; Soubies, Emmanuel; Descombes, Xavier

doi:10.1109/isbi.2015.7164090

Cited by 5 publications

(4 citation statements)

References 5 publications

(5 reference statements)

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“…In order to improve the resulting poor approximation of the cell shapes, the object space may be defined as a dictionary of precomputed shapes. Such a dictionary can be obtained from previous segmentation methods (Poulain et al, 2015) or by constructing an exhaustive description of convex shapes inside a small region (e.g. bounded by 5 x 5 pixels; Cedilnik et al, 2018).…”

Section: Related Workmentioning

confidence: 99%

Using Redundant Information From Multiple Aerial Images for the Detection of Bomb Craters Based on Marked Point Processes

Kruse

Rottensteiner

Heipke

2020

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Many countries were the target of air strikes during World War II. Numerous unexploded bombs still exist in the ground. These duds can be tracked down with the help of bomb craters, indicating areas where unexploded bombs may be located. Such areas are documented in so-called impact maps based on detected bomb craters. In this paper, a stochastic approach based on marked point processes (MPPs) for the automatic detection of bomb craters in aerial images taken during World War II is presented. As most areas are covered by multiple images, the influence of redundant image information on the object detection result is investigated: we compare the results generated based on single images with those obtained by our new approach that combines the individual detection results of multiple images covering the same location. The object model for the bomb craters is represented by circles. Our MPP approach determines the most likely configuration of objects within the scene. The goal is reached by minimizing an energy function that describes the conformity with a predefined model by Reversible Jump Markov Chain Monte Carlo sampling in combination with simulated annealing. Afterwards, a probability map is generated from the automatic detections via kernel density estimation. By setting a threshold, areas around the detections are classified as contaminated or uncontaminated sites, respectively, which results in an impact map. Our results show a significant improvement with respect to its quality when redundant image information is used.

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Section: Related Workmentioning

confidence: 99%

Using Redundant Information From Multiple Aerial Images for the Detection of Bomb Craters Based on Marked Point Processes

Kruse

Rottensteiner

Heipke

2020

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…The bigger the value of the F-score is, the better performance the method reaches. This validation method was widely used in previously published works (Al-Kofahi et al, 2010;Shu et al, 2013;Zhang et al, 2013;He et al, 2015;Poulain et al, 2015;Molnar et al, 2016).…”

Section: Comparison Of Automated and Manual Neuron Countingmentioning

confidence: 99%

Automated Individualization of Size-Varying and Touching Neurons in Macaque Cerebral Microscopic Images

et al. 2019

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In biomedical research, cell analysis is important to assess physiological and pathophysiological information. Virtual microscopy offers the unique possibility to study the compositions of tissues at a cellular scale. However, images acquired at such high spatial resolution are massive, contain complex information, and are therefore difficult to analyze automatically. In this article, we address the problem of individualization of size-varying and touching neurons in optical microscopy two-dimensional (2-D) images. Our approach is based on a series of processing steps that incorporate increasingly more information. (1) After a step of segmentation of neuron class using a Random Forest classifier, a novel min-max filter is used to enhance neurons' centroids and boundaries, enabling the use of region growing process based on a contour-based model to drive it to neuron boundary and achieve individualization of touching neurons. (2) Taking into account size-varying neurons, an adaptive multiscale procedure aiming at individualizing touching neurons is proposed. This protocol was evaluated in 17 major anatomical regions from three NeuN-stained macaque brain sections presenting diverse and comprehensive neuron densities. Qualitative and quantitative analyses demonstrate that the proposed method provides satisfactory results in most regions (e.g., caudate, cortex, subiculum, and putamen) and outperforms a baseline Watershed algorithm. Neuron counts obtained with our method show high correlation with an adapted stereology technique performed by two experts (respectively, 0.983 and 0.975 for the two experts). Neuron diameters obtained with our method ranged between 2 and 28.6 µm, matching values reported in the literature. Further works will aim to evaluate the impact of staining and interindividual variability on our protocol.

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“…To overcome this limit, it has been proposed to define the object space as a dictionary of precomputed shapes. Such a dictionary can be obtained from previous segmentation maps as in [11] (see the result on figure 8) or by constructing an exhaustive description of shapes included in a small bounding box. On figure 8 we can see that the use of a dictionary combining shapes obtained by the particle analyzer algorithm and by an active contour approach allows to select the most relevant ones from each method and for different parameters, thus improving the global result.…”

Section: More General Shapesmentioning

confidence: 99%

Multiple objects detection in biological images using a marked point process framework

Descombes

2017

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International audienceThe marked point process framework has been successfully developed in the field of image analysis to detect a configuration of predefined objects. The goal of this paper is to show how it can be particularly applied to biological imagery. We present a simple model that shows how some of the challenges specific to biological data are well addressed by the methodology. We further describe an extension to this first model to address other challenges due, for example, to the shape variability in biological material. We finally show results that illustrate the MPP framework using the " simcep " algorithm for simulating populations of cells

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Cells detection using segmentation competition

Cited by 5 publications

References 5 publications

Using Redundant Information From Multiple Aerial Images for the Detection of Bomb Craters Based on Marked Point Processes

Using Redundant Information From Multiple Aerial Images for the Detection of Bomb Craters Based on Marked Point Processes

Automated Individualization of Size-Varying and Touching Neurons in Macaque Cerebral Microscopic Images

Multiple objects detection in biological images using a marked point process framework

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