Accurate Automatic Detection of Densely Distributed Cell Nuclei in 3D Space.
To measure the activity of neurons using whole-brain activity imaging, precise detection of each neuron or its nucleus is required. In the head region of the nematode C. elegans, the neuronal cell bodies are distributed densely in three-dimensional (3D) space. However, no existing computational meth...
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2016-06-01
|
| Series: | PLoS Computational Biology |
| Online Access: | http://europepmc.org/articles/PMC4894571?pdf=render |
| _version_ | 1818036299550949376 |
|---|---|
| author | Yu Toyoshima Terumasa Tokunaga Osamu Hirose Manami Kanamori Takayuki Teramoto Moon Sun Jang Sayuri Kuge Takeshi Ishihara Ryo Yoshida Yuichi Iino |
| author_facet | Yu Toyoshima Terumasa Tokunaga Osamu Hirose Manami Kanamori Takayuki Teramoto Moon Sun Jang Sayuri Kuge Takeshi Ishihara Ryo Yoshida Yuichi Iino |
| author_sort | Yu Toyoshima |
| collection | DOAJ |
| description | To measure the activity of neurons using whole-brain activity imaging, precise detection of each neuron or its nucleus is required. In the head region of the nematode C. elegans, the neuronal cell bodies are distributed densely in three-dimensional (3D) space. However, no existing computational methods of image analysis can separate them with sufficient accuracy. Here we propose a highly accurate segmentation method based on the curvatures of the iso-intensity surfaces. To obtain accurate positions of nuclei, we also developed a new procedure for least squares fitting with a Gaussian mixture model. Combining these methods enables accurate detection of densely distributed cell nuclei in a 3D space. The proposed method was implemented as a graphical user interface program that allows visualization and correction of the results of automatic detection. Additionally, the proposed method was applied to time-lapse 3D calcium imaging data, and most of the nuclei in the images were successfully tracked and measured. |
| first_indexed | 2024-12-10T07:08:45Z |
| format | Article |
| id | doaj.art-94e6d8c4e98c4478a02971de2e7fc135 |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-12-10T07:08:45Z |
| publishDate | 2016-06-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-94e6d8c4e98c4478a02971de2e7fc1352022-12-22T01:58:07ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582016-06-01126e100497010.1371/journal.pcbi.1004970Accurate Automatic Detection of Densely Distributed Cell Nuclei in 3D Space.Yu ToyoshimaTerumasa TokunagaOsamu HiroseManami KanamoriTakayuki TeramotoMoon Sun JangSayuri KugeTakeshi IshiharaRyo YoshidaYuichi IinoTo measure the activity of neurons using whole-brain activity imaging, precise detection of each neuron or its nucleus is required. In the head region of the nematode C. elegans, the neuronal cell bodies are distributed densely in three-dimensional (3D) space. However, no existing computational methods of image analysis can separate them with sufficient accuracy. Here we propose a highly accurate segmentation method based on the curvatures of the iso-intensity surfaces. To obtain accurate positions of nuclei, we also developed a new procedure for least squares fitting with a Gaussian mixture model. Combining these methods enables accurate detection of densely distributed cell nuclei in a 3D space. The proposed method was implemented as a graphical user interface program that allows visualization and correction of the results of automatic detection. Additionally, the proposed method was applied to time-lapse 3D calcium imaging data, and most of the nuclei in the images were successfully tracked and measured.http://europepmc.org/articles/PMC4894571?pdf=render |
| spellingShingle | Yu Toyoshima Terumasa Tokunaga Osamu Hirose Manami Kanamori Takayuki Teramoto Moon Sun Jang Sayuri Kuge Takeshi Ishihara Ryo Yoshida Yuichi Iino Accurate Automatic Detection of Densely Distributed Cell Nuclei in 3D Space. PLoS Computational Biology |
| title | Accurate Automatic Detection of Densely Distributed Cell Nuclei in 3D Space. |
| title_full | Accurate Automatic Detection of Densely Distributed Cell Nuclei in 3D Space. |
| title_fullStr | Accurate Automatic Detection of Densely Distributed Cell Nuclei in 3D Space. |
| title_full_unstemmed | Accurate Automatic Detection of Densely Distributed Cell Nuclei in 3D Space. |
| title_short | Accurate Automatic Detection of Densely Distributed Cell Nuclei in 3D Space. |
| title_sort | accurate automatic detection of densely distributed cell nuclei in 3d space |
| url | http://europepmc.org/articles/PMC4894571?pdf=render |
| work_keys_str_mv | AT yutoyoshima accurateautomaticdetectionofdenselydistributedcellnucleiin3dspace AT terumasatokunaga accurateautomaticdetectionofdenselydistributedcellnucleiin3dspace AT osamuhirose accurateautomaticdetectionofdenselydistributedcellnucleiin3dspace AT manamikanamori accurateautomaticdetectionofdenselydistributedcellnucleiin3dspace AT takayukiteramoto accurateautomaticdetectionofdenselydistributedcellnucleiin3dspace AT moonsunjang accurateautomaticdetectionofdenselydistributedcellnucleiin3dspace AT sayurikuge accurateautomaticdetectionofdenselydistributedcellnucleiin3dspace AT takeshiishihara accurateautomaticdetectionofdenselydistributedcellnucleiin3dspace AT ryoyoshida accurateautomaticdetectionofdenselydistributedcellnucleiin3dspace AT yuichiiino accurateautomaticdetectionofdenselydistributedcellnucleiin3dspace |