A pulse coupled neural network segmentation algorithm for reflectance confocal images of epithelial tissue.
Automatic segmentation of nuclei in reflectance confocal microscopy images is critical for visualization and rapid quantification of nuclear-to-cytoplasmic ratio, a useful indicator of epithelial precancer. Reflectance confocal microscopy can provide three-dimensional imaging of epithelial tissue in...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2015-01-01
|
| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0122368 |
| _version_ | 1819123312045850624 |
|---|---|
| author | Meagan A Harris Andrew N Van Bilal H Malik Joey M Jabbour Kristen C Maitland |
| author_facet | Meagan A Harris Andrew N Van Bilal H Malik Joey M Jabbour Kristen C Maitland |
| author_sort | Meagan A Harris |
| collection | DOAJ |
| description | Automatic segmentation of nuclei in reflectance confocal microscopy images is critical for visualization and rapid quantification of nuclear-to-cytoplasmic ratio, a useful indicator of epithelial precancer. Reflectance confocal microscopy can provide three-dimensional imaging of epithelial tissue in vivo with sub-cellular resolution. Changes in nuclear density or nuclear-to-cytoplasmic ratio as a function of depth obtained from confocal images can be used to determine the presence or stage of epithelial cancers. However, low nuclear to background contrast, low resolution at greater imaging depths, and significant variation in reflectance signal of nuclei complicate segmentation required for quantification of nuclear-to-cytoplasmic ratio. Here, we present an automated segmentation method to segment nuclei in reflectance confocal images using a pulse coupled neural network algorithm, specifically a spiking cortical model, and an artificial neural network classifier. The segmentation algorithm was applied to an image model of nuclei with varying nuclear to background contrast. Greater than 90% of simulated nuclei were detected for contrast of 2.0 or greater. Confocal images of porcine and human oral mucosa were used to evaluate application to epithelial tissue. Segmentation accuracy was assessed using manual segmentation of nuclei as the gold standard. |
| first_indexed | 2024-12-22T07:06:21Z |
| format | Article |
| id | doaj.art-d0c6089d90da452eb4160f884d26b67d |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-22T07:06:21Z |
| publishDate | 2015-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-d0c6089d90da452eb4160f884d26b67d2022-12-21T18:34:40ZengPublic Library of Science (PLoS)PLoS ONE1932-62032015-01-01103e012236810.1371/journal.pone.0122368A pulse coupled neural network segmentation algorithm for reflectance confocal images of epithelial tissue.Meagan A HarrisAndrew N VanBilal H MalikJoey M JabbourKristen C MaitlandAutomatic segmentation of nuclei in reflectance confocal microscopy images is critical for visualization and rapid quantification of nuclear-to-cytoplasmic ratio, a useful indicator of epithelial precancer. Reflectance confocal microscopy can provide three-dimensional imaging of epithelial tissue in vivo with sub-cellular resolution. Changes in nuclear density or nuclear-to-cytoplasmic ratio as a function of depth obtained from confocal images can be used to determine the presence or stage of epithelial cancers. However, low nuclear to background contrast, low resolution at greater imaging depths, and significant variation in reflectance signal of nuclei complicate segmentation required for quantification of nuclear-to-cytoplasmic ratio. Here, we present an automated segmentation method to segment nuclei in reflectance confocal images using a pulse coupled neural network algorithm, specifically a spiking cortical model, and an artificial neural network classifier. The segmentation algorithm was applied to an image model of nuclei with varying nuclear to background contrast. Greater than 90% of simulated nuclei were detected for contrast of 2.0 or greater. Confocal images of porcine and human oral mucosa were used to evaluate application to epithelial tissue. Segmentation accuracy was assessed using manual segmentation of nuclei as the gold standard.https://doi.org/10.1371/journal.pone.0122368 |
| spellingShingle | Meagan A Harris Andrew N Van Bilal H Malik Joey M Jabbour Kristen C Maitland A pulse coupled neural network segmentation algorithm for reflectance confocal images of epithelial tissue. PLoS ONE |
| title | A pulse coupled neural network segmentation algorithm for reflectance confocal images of epithelial tissue. |
| title_full | A pulse coupled neural network segmentation algorithm for reflectance confocal images of epithelial tissue. |
| title_fullStr | A pulse coupled neural network segmentation algorithm for reflectance confocal images of epithelial tissue. |
| title_full_unstemmed | A pulse coupled neural network segmentation algorithm for reflectance confocal images of epithelial tissue. |
| title_short | A pulse coupled neural network segmentation algorithm for reflectance confocal images of epithelial tissue. |
| title_sort | pulse coupled neural network segmentation algorithm for reflectance confocal images of epithelial tissue |
| url | https://doi.org/10.1371/journal.pone.0122368 |
| work_keys_str_mv | AT meaganaharris apulsecoupledneuralnetworksegmentationalgorithmforreflectanceconfocalimagesofepithelialtissue AT andrewnvan apulsecoupledneuralnetworksegmentationalgorithmforreflectanceconfocalimagesofepithelialtissue AT bilalhmalik apulsecoupledneuralnetworksegmentationalgorithmforreflectanceconfocalimagesofepithelialtissue AT joeymjabbour apulsecoupledneuralnetworksegmentationalgorithmforreflectanceconfocalimagesofepithelialtissue AT kristencmaitland apulsecoupledneuralnetworksegmentationalgorithmforreflectanceconfocalimagesofepithelialtissue AT meaganaharris pulsecoupledneuralnetworksegmentationalgorithmforreflectanceconfocalimagesofepithelialtissue AT andrewnvan pulsecoupledneuralnetworksegmentationalgorithmforreflectanceconfocalimagesofepithelialtissue AT bilalhmalik pulsecoupledneuralnetworksegmentationalgorithmforreflectanceconfocalimagesofepithelialtissue AT joeymjabbour pulsecoupledneuralnetworksegmentationalgorithmforreflectanceconfocalimagesofepithelialtissue AT kristencmaitland pulsecoupledneuralnetworksegmentationalgorithmforreflectanceconfocalimagesofepithelialtissue |