Visualization of the image segmentation and labeling pipeline for the first frame of an example movie.
<p>(A) Grayscale raw image. (B) Grayscale image showing the output of DeepCad. (C) Probability map generated by ilastik. (D) Binarized image obtained (from (C)) using thresholding via Li’s iterative Minimum Cross Entropy Method. (E) Smoothed binarized image. (F) Integrated movie over all time...
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2025
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| _version_ | 1852019881564700672 |
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| author | Miriam Schnitzerlein (21446136) |
| author2 | Eric Greto (21446139) Anja Wegner (10643774) Anna Möller (18076549) Oliver Aust (21446142) Oumaima Ben Brahim (21446145) David B. Blumenthal (11838270) Vasily Zaburdaev (504665) Stefan Uderhardt (21446148) |
| author2_role | author author author author author author author author |
| author_facet | Miriam Schnitzerlein (21446136) Eric Greto (21446139) Anja Wegner (10643774) Anna Möller (18076549) Oliver Aust (21446142) Oumaima Ben Brahim (21446145) David B. Blumenthal (11838270) Vasily Zaburdaev (504665) Stefan Uderhardt (21446148) |
| author_role | author |
| dc.creator.none.fl_str_mv | Miriam Schnitzerlein (21446136) Eric Greto (21446139) Anja Wegner (10643774) Anna Möller (18076549) Oliver Aust (21446142) Oumaima Ben Brahim (21446145) David B. Blumenthal (11838270) Vasily Zaburdaev (504665) Stefan Uderhardt (21446148) |
| dc.date.none.fl_str_mv | 2025-05-29T17:53:22Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pcbi.1011859.g008 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/Visualization_of_the_image_segmentation_and_labeling_pipeline_for_the_first_frame_of_an_example_movie_/29189004 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biochemistry Medicine Developmental Biology Space Science Biological Sciences not elsewhere classified physiologically relevant approach monitoring interstitial fluids employed intravital imaging div >< p characteristic morphological changes aged animals displayed interpretable cell size quantify rtm morphodynamics generate dynamic data clearing cellular debris bona fide </ pathological cell states functional tissue age various functional states resident tissue macrophages physiological native state functional states cellular morphodynamics tissue homeostasis cell populations various conditions state conditions functional status dynamic behavior vivo </ vitro </ young counterparts wide range upon challenge shape features qualitative differentiation predominantly within population level naïve morphospace morphodynamical phenotypes inflammatory activation including pro fundamentally different features allowed endosomal dysfunction diverse functions defining human current knowledge behavioral motifs >, enabling |
| dc.title.none.fl_str_mv | Visualization of the image segmentation and labeling pipeline for the first frame of an example movie. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <p>(A) Grayscale raw image. (B) Grayscale image showing the output of DeepCad. (C) Probability map generated by ilastik. (D) Binarized image obtained (from (C)) using thresholding via Li’s iterative Minimum Cross Entropy Method. (E) Smoothed binarized image. (F) Integrated movie over all time frames. (G) Fixed area of the integrated movie by using a threshold of 0.97 on (F). (H) Label seeds obtained from (G) by coloring all distinct objects. (I) Overlay of label seeds (H) and binary segmented cells (D). (J) Labeling all distinct objects in (I) using the watershed algorithm to be used as new seed labels. (K) Dilating the cells from (E) and using the labeled cells from (J) as seed labels for the watershed algorithm to achieve the labeling as shown in (L). (M) Using the labels from (L) to correctly label the pre-processed binary images from (E). (N) Final labelled image.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_24a9e6a7b99137299feab5de4e5b10aa |
| identifier_str_mv | 10.1371/journal.pcbi.1011859.g008 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/29189004 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Visualization of the image segmentation and labeling pipeline for the first frame of an example movie.Miriam Schnitzerlein (21446136)Eric Greto (21446139)Anja Wegner (10643774)Anna Möller (18076549)Oliver Aust (21446142)Oumaima Ben Brahim (21446145)David B. Blumenthal (11838270)Vasily Zaburdaev (504665)Stefan Uderhardt (21446148)BiochemistryMedicineDevelopmental BiologySpace ScienceBiological Sciences not elsewhere classifiedphysiologically relevant approachmonitoring interstitial fluidsemployed intravital imagingdiv >< pcharacteristic morphological changesaged animals displayedinterpretable cell sizequantify rtm morphodynamicsgenerate dynamic dataclearing cellular debrisbona fide </pathological cell statesfunctional tissue agevarious functional statesresident tissue macrophagesphysiological native statefunctional statescellular morphodynamicstissue homeostasiscell populationsvarious conditionsstate conditionsfunctional statusdynamic behaviorvivo </vitro </young counterpartswide rangeupon challengeshape featuresqualitative differentiationpredominantly withinpopulation levelnaïve morphospacemorphodynamical phenotypesinflammatory activationincluding profundamentally differentfeatures allowedendosomal dysfunctiondiverse functionsdefining humancurrent knowledgebehavioral motifs>, enabling<p>(A) Grayscale raw image. (B) Grayscale image showing the output of DeepCad. (C) Probability map generated by ilastik. (D) Binarized image obtained (from (C)) using thresholding via Li’s iterative Minimum Cross Entropy Method. (E) Smoothed binarized image. (F) Integrated movie over all time frames. (G) Fixed area of the integrated movie by using a threshold of 0.97 on (F). (H) Label seeds obtained from (G) by coloring all distinct objects. (I) Overlay of label seeds (H) and binary segmented cells (D). (J) Labeling all distinct objects in (I) using the watershed algorithm to be used as new seed labels. (K) Dilating the cells from (E) and using the labeled cells from (J) as seed labels for the watershed algorithm to achieve the labeling as shown in (L). (M) Using the labels from (L) to correctly label the pre-processed binary images from (E). (N) Final labelled image.</p>2025-05-29T17:53:22ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pcbi.1011859.g008https://figshare.com/articles/figure/Visualization_of_the_image_segmentation_and_labeling_pipeline_for_the_first_frame_of_an_example_movie_/29189004CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/291890042025-05-29T17:53:22Z |
| spellingShingle | Visualization of the image segmentation and labeling pipeline for the first frame of an example movie. Miriam Schnitzerlein (21446136) Biochemistry Medicine Developmental Biology Space Science Biological Sciences not elsewhere classified physiologically relevant approach monitoring interstitial fluids employed intravital imaging div >< p characteristic morphological changes aged animals displayed interpretable cell size quantify rtm morphodynamics generate dynamic data clearing cellular debris bona fide </ pathological cell states functional tissue age various functional states resident tissue macrophages physiological native state functional states cellular morphodynamics tissue homeostasis cell populations various conditions state conditions functional status dynamic behavior vivo </ vitro </ young counterparts wide range upon challenge shape features qualitative differentiation predominantly within population level naïve morphospace morphodynamical phenotypes inflammatory activation including pro fundamentally different features allowed endosomal dysfunction diverse functions defining human current knowledge behavioral motifs >, enabling |
| status_str | publishedVersion |
| title | Visualization of the image segmentation and labeling pipeline for the first frame of an example movie. |
| title_full | Visualization of the image segmentation and labeling pipeline for the first frame of an example movie. |
| title_fullStr | Visualization of the image segmentation and labeling pipeline for the first frame of an example movie. |
| title_full_unstemmed | Visualization of the image segmentation and labeling pipeline for the first frame of an example movie. |
| title_short | Visualization of the image segmentation and labeling pipeline for the first frame of an example movie. |
| title_sort | Visualization of the image segmentation and labeling pipeline for the first frame of an example movie. |
| topic | Biochemistry Medicine Developmental Biology Space Science Biological Sciences not elsewhere classified physiologically relevant approach monitoring interstitial fluids employed intravital imaging div >< p characteristic morphological changes aged animals displayed interpretable cell size quantify rtm morphodynamics generate dynamic data clearing cellular debris bona fide </ pathological cell states functional tissue age various functional states resident tissue macrophages physiological native state functional states cellular morphodynamics tissue homeostasis cell populations various conditions state conditions functional status dynamic behavior vivo </ vitro </ young counterparts wide range upon challenge shape features qualitative differentiation predominantly within population level naïve morphospace morphodynamical phenotypes inflammatory activation including pro fundamentally different features allowed endosomal dysfunction diverse functions defining human current knowledge behavioral motifs >, enabling |