A novel image segmentation method based on spatial autocorrelation identifies A-type potassium channel clusters in the thalamus
Unsupervised segmentation in biological and non-biological images is only partially resolved. Segmentation either requires arbitrary thresholds or large teaching datasets. Here, we propose a spatial autocorrelation method based on Local Moran’s I coefficient to differentiate signal, background, and...
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| Format: | Article |
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eLife Sciences Publications Ltd
2024-12-01
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| Online Access: | https://elifesciences.org/articles/89361 |
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| author | Csaba Dávid Kristóf Giber Katalin Kerti-Szigeti Mihály Köllő Zoltan Nusser Laszlo Acsady |
| author_facet | Csaba Dávid Kristóf Giber Katalin Kerti-Szigeti Mihály Köllő Zoltan Nusser Laszlo Acsady |
| author_sort | Csaba Dávid |
| collection | DOAJ |
| description | Unsupervised segmentation in biological and non-biological images is only partially resolved. Segmentation either requires arbitrary thresholds or large teaching datasets. Here, we propose a spatial autocorrelation method based on Local Moran’s I coefficient to differentiate signal, background, and noise in any type of image. The method, originally described for geoinformatics, does not require a predefined intensity threshold or teaching algorithm for image segmentation and allows quantitative comparison of samples obtained in different conditions. It utilizes relative intensity as well as spatial information of neighboring elements to select spatially contiguous groups of pixels. We demonstrate that Moran’s method outperforms threshold-based method in both artificially generated as well as in natural images especially when background noise is substantial. This superior performance can be attributed to the exclusion of false positive pixels resulting from isolated, high intensity pixels in high noise conditions. To test the method’s power in real situation, we used high power confocal images of the somatosensory thalamus immunostained for Kv4.2 and Kv4.3 (A-type) voltage-gated potassium channels in mice. Moran’s method identified high-intensity Kv4.2 and Kv4.3 ion channel clusters in the thalamic neuropil. Spatial distribution of these clusters displayed strong correlation with large sensory axon terminals of subcortical origin. The unique association of the special presynaptic terminals and a postsynaptic voltage-gated ion channel cluster was confirmed with electron microscopy. These data demonstrate that Moran’s method is a rapid, simple image segmentation method optimal for variable and high noise conditions. |
| format | Article |
| id | doaj-art-978959bdad9847b5b45c09435c9eb0ce |
| institution | Kabale University |
| issn | 2050-084X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | eLife Sciences Publications Ltd |
| record_format | Article |
| series | eLife |
| spelling | doaj-art-978959bdad9847b5b45c09435c9eb0ce2024-12-10T13:26:12ZengeLife Sciences Publications LtdeLife2050-084X2024-12-011210.7554/eLife.89361A novel image segmentation method based on spatial autocorrelation identifies A-type potassium channel clusters in the thalamusCsaba Dávid0https://orcid.org/0000-0003-4221-9468Kristóf Giber1Katalin Kerti-Szigeti2Mihály Köllő3Zoltan Nusser4Laszlo Acsady5https://orcid.org/0000-0002-0679-2980Lendület Laboratory of Thalamus Research, HUN-REN Institute of Experimental Medicine, Budapest, Hungary; Department of Anatomy, Histology and Embryology, Semmelweis University, Budapest, HungaryLendület Laboratory of Thalamus Research, HUN-REN Institute of Experimental Medicine, Budapest, HungaryLaboratory of Cellular Neurophysiology, HUN-REN Institute of Experimental Medicine, Budapest, Hungary; Novarino Group, Institute of Science and Technology, Klosterneuburg, AustriaLaboratory of Cellular Neurophysiology, HUN-REN Institute of Experimental Medicine, Budapest, Hungary; Sensory Circuits and Neurotechnology Laboratory, Francis Crick Institute, London, United KingdomLaboratory of Cellular Neurophysiology, HUN-REN Institute of Experimental Medicine, Budapest, HungaryLendület Laboratory of Thalamus Research, HUN-REN Institute of Experimental Medicine, Budapest, HungaryUnsupervised segmentation in biological and non-biological images is only partially resolved. Segmentation either requires arbitrary thresholds or large teaching datasets. Here, we propose a spatial autocorrelation method based on Local Moran’s I coefficient to differentiate signal, background, and noise in any type of image. The method, originally described for geoinformatics, does not require a predefined intensity threshold or teaching algorithm for image segmentation and allows quantitative comparison of samples obtained in different conditions. It utilizes relative intensity as well as spatial information of neighboring elements to select spatially contiguous groups of pixels. We demonstrate that Moran’s method outperforms threshold-based method in both artificially generated as well as in natural images especially when background noise is substantial. This superior performance can be attributed to the exclusion of false positive pixels resulting from isolated, high intensity pixels in high noise conditions. To test the method’s power in real situation, we used high power confocal images of the somatosensory thalamus immunostained for Kv4.2 and Kv4.3 (A-type) voltage-gated potassium channels in mice. Moran’s method identified high-intensity Kv4.2 and Kv4.3 ion channel clusters in the thalamic neuropil. Spatial distribution of these clusters displayed strong correlation with large sensory axon terminals of subcortical origin. The unique association of the special presynaptic terminals and a postsynaptic voltage-gated ion channel cluster was confirmed with electron microscopy. These data demonstrate that Moran’s method is a rapid, simple image segmentation method optimal for variable and high noise conditions.https://elifesciences.org/articles/89361image segmentationthalamuspotassium channelssomatosensoryvoltage-gated ion channelscluster |
| spellingShingle | Csaba Dávid Kristóf Giber Katalin Kerti-Szigeti Mihály Köllő Zoltan Nusser Laszlo Acsady A novel image segmentation method based on spatial autocorrelation identifies A-type potassium channel clusters in the thalamus eLife image segmentation thalamus potassium channels somatosensory voltage-gated ion channels cluster |
| title | A novel image segmentation method based on spatial autocorrelation identifies A-type potassium channel clusters in the thalamus |
| title_full | A novel image segmentation method based on spatial autocorrelation identifies A-type potassium channel clusters in the thalamus |
| title_fullStr | A novel image segmentation method based on spatial autocorrelation identifies A-type potassium channel clusters in the thalamus |
| title_full_unstemmed | A novel image segmentation method based on spatial autocorrelation identifies A-type potassium channel clusters in the thalamus |
| title_short | A novel image segmentation method based on spatial autocorrelation identifies A-type potassium channel clusters in the thalamus |
| title_sort | novel image segmentation method based on spatial autocorrelation identifies a type potassium channel clusters in the thalamus |
| topic | image segmentation thalamus potassium channels somatosensory voltage-gated ion channels cluster |
| url | https://elifesciences.org/articles/89361 |
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