Biomedical Image Segmentation: A Survey

Alzahrani, Yahya; Boufama, Boubakeur

doi:10.1007/s42979-021-00704-7

Cited by 27 publications

(12 citation statements)

References 133 publications

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“…Its origins lie in computer vision. It is a well-studied problem today, especially where segmentation is mandatory for decision or prediction, typically for medical or biology images (Fu & Mui 1981;Alzahrani & Boufama 2021). It has also been used for a long time in astrophysics, with recent applications on galaxies (Zhu et al 2019;Hausen & Robertson 2020;Bianco et al 2021;Bekki 2021).…”

Section: Segmentation With Unetsmentioning

confidence: 99%

Supervised machine learning on Galactic filaments

Zavagno

Dupé

Bensaid

et al. 2023

A&A

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Context. Filaments are ubiquitous in the Galaxy, and they host star formation. Detecting them in a reliable way is therefore key towards our understanding of the star formation process. Aims. We explore whether supervised machine learning can identify filamentary structures on the whole Galactic plane. Methods. We used two versions of UNet-based networks for image segmentation. We used H 2 column density images of the Galactic plane obtained with Herschel Hi-GAL data as input data. We trained the UNet-based networks with skeletons (spine plus branches) of filaments that were extracted from these images, together with background and missing data masks that we produced. We tested eight training scenarios to determine the best scenario for our astrophysical purpose of classifying pixels as filaments.Results. The training of the UNets allows us to create a new image of the Galactic plane by segmentation in which pixels belonging to filamentary structures are identified. With this new method, we classify more pixels (more by a factor of 2 to 7, depending on the classification threshold used) as belonging to filaments than the spine plus branches structures we used as input. New structures are revealed, which are mainly low-contrast filaments that were not detected before. We use standard metrics to evaluate the performances of the different training scenarios. This allows us to demonstrate the robustness of the method and to determine an optimal threshold value that maximizes the recovery of the input labelled pixel classification. Conclusions. This proof-of-concept study shows that supervised machine learning can reveal filamentary structures that are present throughout the Galactic plane. The detection of these structures, including low-density and low-contrast structures that have never been seen before, offers important perspectives for the study of these filaments.

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Section: Segmentation With Unetsmentioning

confidence: 99%

Supervised machine learning on Galactic filaments

Zavagno

Dupé

Bensaid

et al. 2023

A&A

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“…Recent advances in artificial intelligence methods applied to bioimage analysis remarkably improved the accuracy of cell detection and subsequently tracking [ 12 – 14 ]. Amongst these, end-to-end neuronal networks with convolutional layers such as the U-NET [ 15 ] and its variants that transform an input image into another image as output, improved the segmentation of complex structures with respect to single pixel classifiers [ 16 ], gaining application in both biomedical imaging for cell detection, counting, and morphological analysis [ 17 , 18 ]. The usage of U-NET was also demonstrated to improve cell and tracking due to the increased robustness of object detection on binary masks rather than on original images which may suffer from non-uniform illumination or poor signal to noise ratio [ 19 – 22 ].…”

Section: Introductionmentioning

confidence: 99%

Tracking unlabeled cancer cells imaged with low resolution in wide migration chambers via U-NET class-1 probability (pseudofluorescence)

et al. 2023

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Cell migration is a pivotal biological process, whose dysregulation is found in many diseases including inflammation and cancer. Advances in microscopy technologies allow now to study cell migration in vitro, within engineered microenvironments that resemble in vivo conditions. However, to capture an entire 3D migration chamber for extended periods of time and with high temporal resolution, images are generally acquired with low resolution, which poses a challenge for data analysis. Indeed, cell detection and tracking are hampered due to the large pixel size (i.e., cell diameter down to 2 pixels), the possible low signal-to-noise ratio, and distortions in the cell shape due to changes in the z-axis position. Although fluorescent staining can be used to facilitate cell detection, it may alter cell behavior and it may suffer from fluorescence loss over time (photobleaching).Here we describe a protocol that employs an established deep learning method (U-NET), to specifically convert transmitted light (TL) signal from unlabeled cells imaged with low resolution to a fluorescent-like signal (class 1 probability). We demonstrate its application to study cancer cell migration, obtaining a significant improvement in tracking accuracy, while not suffering from photobleaching. This is reflected in the possibility of tracking cells for three-fold longer periods of time. To facilitate the application of the protocol we provide WID-U, an open-source plugin for FIJI and Imaris imaging software, the training dataset used in this paper, and the code to train the network for custom experimental settings.

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“…Thermal imaging diagnostics is a promising method of examination in medicine. It is harmless and painless, highly informative and physiological 10,11 .…”

Section: Introductionmentioning

confidence: 99%

Biomedical image segmentation method based on contour preparation

Orazayeva

Wójcik

Pavlov

et al. 2022

Photonics Applications in Astronomy, Communications, Industry, and High Energy Physics Experiments 2022

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The methods of processing biomedical images, namely thermal images, are investigated. Algorithms for calculating the temperature and area of the zone of interest in the manual mode operator-computer, as well as in the automatic mode, are specified. Methods of thermal image processing are presented, namely recursive generalized contour preparation and preparation based on histograms of connections. An experimental study of these methods was performed, as well as a comparison of thermal image segmentation methods in manual segmentation modes, using contour preparation-based segmentation, multilevel segmentation based on recursive generalized contour preparation, and automatic segmentation based on connectivity histograms.

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Biomedical Image Segmentation: A Survey

Cited by 27 publications

References 133 publications

Supervised machine learning on Galactic filaments

Supervised machine learning on Galactic filaments

Tracking unlabeled cancer cells imaged with low resolution in wide migration chambers via U-NET class-1 probability (pseudofluorescence)

Biomedical image segmentation method based on contour preparation

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