Automatic segmentation and 3D feature extraction of protein aggregates in Caenorhabditis Elegans

Detalhes bibliográficos
Autor(a) principal: Rodrigues, Pedro L.
Data de Publicação: 2012
Outros Autores: Moreira, António H. J., Teixeira-Castro, Andreia, Oliveira, João, Dias, Nuno, F. Rodrigues, Nuno, Vilaça, João L.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/11110/508
Resumo: In the last years, it has become increasingly clear that neurodegenerative diseases involve protein aggregation, a process often used as disease progression readout and to develop therapeutic strategies. This work presents an image processing tool to automatic segment, classify and quantify these aggregates and the whole 3D body of the nematode Caenorhabditis Elegans. A total of 150 data set images, containing different slices, were captured with a confocal microscope from animals of distinct genetic conditions. Because of the animals’ transparency, most of the slices pixels appeared dark, hampering their body volume direct reconstruction. Therefore, for each data set, all slices were stacked in one single 2D image in order to determine a volume approximation. The gradient of this image was input to an anisotropic diffusion algorithm that uses the Tukey’s biweight as edge-stopping function. The image histogram median of this outcome was used to dynamically determine a thresholding level, which allows the determination of a smoothed exterior contour of the worm and the medial axis of the worm body from thinning its skeleton. Based on this exterior contour diameter and the medial animal axis, random 3D points were then calculated to produce a volume mesh approximation. The protein aggregations were subsequently segmented based on an iso-value and blended with the resulting volume mesh. The results obtained were consistent with qualitative observations in literature, allowing non-biased, reliable and high throughput protein aggregates quantification. This may lead to a significant improvement on neurodegenerative diseases treatment planning and interventions prevention
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spelling Automatic segmentation and 3D feature extraction of protein aggregates in Caenorhabditis ElegansImage processingQuantificationCaenorhabditis EleganssegmentationIn the last years, it has become increasingly clear that neurodegenerative diseases involve protein aggregation, a process often used as disease progression readout and to develop therapeutic strategies. This work presents an image processing tool to automatic segment, classify and quantify these aggregates and the whole 3D body of the nematode Caenorhabditis Elegans. A total of 150 data set images, containing different slices, were captured with a confocal microscope from animals of distinct genetic conditions. Because of the animals’ transparency, most of the slices pixels appeared dark, hampering their body volume direct reconstruction. Therefore, for each data set, all slices were stacked in one single 2D image in order to determine a volume approximation. The gradient of this image was input to an anisotropic diffusion algorithm that uses the Tukey’s biweight as edge-stopping function. The image histogram median of this outcome was used to dynamically determine a thresholding level, which allows the determination of a smoothed exterior contour of the worm and the medial axis of the worm body from thinning its skeleton. Based on this exterior contour diameter and the medial animal axis, random 3D points were then calculated to produce a volume mesh approximation. The protein aggregations were subsequently segmented based on an iso-value and blended with the resulting volume mesh. The results obtained were consistent with qualitative observations in literature, allowing non-biased, reliable and high throughput protein aggregates quantification. This may lead to a significant improvement on neurodegenerative diseases treatment planning and interventions prevention2013-12-19T17:20:25Z2012-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/11110/508oai:ciencipca.ipca.pt:11110/508eng9780819489661http://hdl.handle.net/11110/508metadata only accessinfo:eu-repo/semantics/openAccessRodrigues, Pedro L.Moreira, António H. J.Teixeira-Castro, AndreiaOliveira, JoãoDias, NunoF. Rodrigues, NunoVilaça, João L.reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2022-09-05T12:52:05Zoai:ciencipca.ipca.pt:11110/508Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T15:00:58.205907Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Automatic segmentation and 3D feature extraction of protein aggregates in Caenorhabditis Elegans
title Automatic segmentation and 3D feature extraction of protein aggregates in Caenorhabditis Elegans
spellingShingle Automatic segmentation and 3D feature extraction of protein aggregates in Caenorhabditis Elegans
Rodrigues, Pedro L.
Image processing
Quantification
Caenorhabditis Elegans
segmentation
title_short Automatic segmentation and 3D feature extraction of protein aggregates in Caenorhabditis Elegans
title_full Automatic segmentation and 3D feature extraction of protein aggregates in Caenorhabditis Elegans
title_fullStr Automatic segmentation and 3D feature extraction of protein aggregates in Caenorhabditis Elegans
title_full_unstemmed Automatic segmentation and 3D feature extraction of protein aggregates in Caenorhabditis Elegans
title_sort Automatic segmentation and 3D feature extraction of protein aggregates in Caenorhabditis Elegans
author Rodrigues, Pedro L.
author_facet Rodrigues, Pedro L.
Moreira, António H. J.
Teixeira-Castro, Andreia
Oliveira, João
Dias, Nuno
F. Rodrigues, Nuno
Vilaça, João L.
author_role author
author2 Moreira, António H. J.
Teixeira-Castro, Andreia
Oliveira, João
Dias, Nuno
F. Rodrigues, Nuno
Vilaça, João L.
author2_role author
author
author
author
author
author
dc.contributor.author.fl_str_mv Rodrigues, Pedro L.
Moreira, António H. J.
Teixeira-Castro, Andreia
Oliveira, João
Dias, Nuno
F. Rodrigues, Nuno
Vilaça, João L.
dc.subject.por.fl_str_mv Image processing
Quantification
Caenorhabditis Elegans
segmentation
topic Image processing
Quantification
Caenorhabditis Elegans
segmentation
description In the last years, it has become increasingly clear that neurodegenerative diseases involve protein aggregation, a process often used as disease progression readout and to develop therapeutic strategies. This work presents an image processing tool to automatic segment, classify and quantify these aggregates and the whole 3D body of the nematode Caenorhabditis Elegans. A total of 150 data set images, containing different slices, were captured with a confocal microscope from animals of distinct genetic conditions. Because of the animals’ transparency, most of the slices pixels appeared dark, hampering their body volume direct reconstruction. Therefore, for each data set, all slices were stacked in one single 2D image in order to determine a volume approximation. The gradient of this image was input to an anisotropic diffusion algorithm that uses the Tukey’s biweight as edge-stopping function. The image histogram median of this outcome was used to dynamically determine a thresholding level, which allows the determination of a smoothed exterior contour of the worm and the medial axis of the worm body from thinning its skeleton. Based on this exterior contour diameter and the medial animal axis, random 3D points were then calculated to produce a volume mesh approximation. The protein aggregations were subsequently segmented based on an iso-value and blended with the resulting volume mesh. The results obtained were consistent with qualitative observations in literature, allowing non-biased, reliable and high throughput protein aggregates quantification. This may lead to a significant improvement on neurodegenerative diseases treatment planning and interventions prevention
publishDate 2012
dc.date.none.fl_str_mv 2012-01-01T00:00:00Z
2013-12-19T17:20:25Z
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