Identifying Components in 3D Density Maps of Protein Nanomachines by Multi-scale Segmentation
Segmentation of density maps obtained using cryo-electron microscopy (cryo-EM) is a challenging task, and is typically accomplished by time-intensive interactive methods. The goal of segmentation is to identify the regions inside the density map that correspond to individual components. We present a...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
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Institute of Electrical and Electronics Engineers
2009
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| Online Access: | http://hdl.handle.net/1721.1/49519 https://orcid.org/0000-0001-7706-6954 |
| _version_ | 1826217609623764992 |
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| author | Pintilie, Grigore Dimitrie Zhang, Junjie Chiu, Wah Gossard, David C. |
| author2 | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science |
| author_facet | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Pintilie, Grigore Dimitrie Zhang, Junjie Chiu, Wah Gossard, David C. |
| author_sort | Pintilie, Grigore Dimitrie |
| collection | MIT |
| description | Segmentation of density maps obtained using cryo-electron microscopy (cryo-EM) is a challenging task, and is typically accomplished by time-intensive interactive methods. The goal of segmentation is to identify the regions inside the density map that correspond to individual components. We present a multi-scale segmentation method for accomplishing this task that requires very little user interaction. The method uses the concept of scale space, which is created by convolution of the input density map with a Gaussian filter. The latter process smoothes the density map. The standard deviation of the Gaussian filter is varied, with smaller values corresponding to finer scales and larger values to coarser scales. Each of the maps at different scales is segmented using the watershed method, which is very efficient, completely automatic, and does not require the specification of seed points. Some detail is lost in the smoothing process. A sharpening process reintroduces detail into the segmentation at the coarsest scale by using the segmentations at the finer scales. We apply the method to simulated density maps, where the exact segmentation (or ground truth) is known, and rigorously evaluate the accuracy of the resulting segmentations. |
| first_indexed | 2024-09-23T17:06:24Z |
| format | Article |
| id | mit-1721.1/49519 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T17:06:24Z |
| publishDate | 2009 |
| publisher | Institute of Electrical and Electronics Engineers |
| record_format | dspace |
| spelling | mit-1721.1/495192022-10-03T10:27:36Z Identifying Components in 3D Density Maps of Protein Nanomachines by Multi-scale Segmentation Pintilie, Grigore Dimitrie Zhang, Junjie Chiu, Wah Gossard, David C. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology. Department of Mechanical Engineering Pintilie, Grigore Pintilie, Grigore Dimitrie Gossard, David C. Segmentation of density maps obtained using cryo-electron microscopy (cryo-EM) is a challenging task, and is typically accomplished by time-intensive interactive methods. The goal of segmentation is to identify the regions inside the density map that correspond to individual components. We present a multi-scale segmentation method for accomplishing this task that requires very little user interaction. The method uses the concept of scale space, which is created by convolution of the input density map with a Gaussian filter. The latter process smoothes the density map. The standard deviation of the Gaussian filter is varied, with smaller values corresponding to finer scales and larger values to coarser scales. Each of the maps at different scales is segmented using the watershed method, which is very efficient, completely automatic, and does not require the specification of seed points. Some detail is lost in the smoothing process. A sharpening process reintroduces detail into the segmentation at the coarsest scale by using the segmentations at the finer scales. We apply the method to simulated density maps, where the exact segmentation (or ground truth) is known, and rigorously evaluate the accuracy of the resulting segmentations. 2009-10-30T13:11:12Z 2009-10-30T13:11:12Z 2009-04 Article http://purl.org/eprint/type/ConferencePaper http://hdl.handle.net/1721.1/49519 Pintilie, Grigore Dimitrie et al. “Identifying components in 3D density maps of protein nanomachines by multi-scale segmentation.” IEEE/NIH Life Science Systems and Applications Workshop. 2009. 44-47. https://orcid.org/0000-0001-7706-6954 en_US http://dx.doi.org/10.1109/LISSA.2009.4906705 IEEE/NIH Life Science Systems and Applications Workshop Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf Institute of Electrical and Electronics Engineers Grigore Pintilie |
| spellingShingle | Pintilie, Grigore Dimitrie Zhang, Junjie Chiu, Wah Gossard, David C. Identifying Components in 3D Density Maps of Protein Nanomachines by Multi-scale Segmentation |
| title | Identifying Components in 3D Density Maps of Protein Nanomachines by Multi-scale Segmentation |
| title_full | Identifying Components in 3D Density Maps of Protein Nanomachines by Multi-scale Segmentation |
| title_fullStr | Identifying Components in 3D Density Maps of Protein Nanomachines by Multi-scale Segmentation |
| title_full_unstemmed | Identifying Components in 3D Density Maps of Protein Nanomachines by Multi-scale Segmentation |
| title_short | Identifying Components in 3D Density Maps of Protein Nanomachines by Multi-scale Segmentation |
| title_sort | identifying components in 3d density maps of protein nanomachines by multi scale segmentation |
| url | http://hdl.handle.net/1721.1/49519 https://orcid.org/0000-0001-7706-6954 |
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