Abstract
With the advancement of high throughput and high resolution volumetric brain imaging, there is an unmet need to trace dense neuron fibers and study long-range neuron connectivity. An initial pipeline is described for processing cellular-level neuronal fiber data acquired by a new super resolution imaging method called Magnified Analysis of the Proteome (MAP). First, a multiscale vessel enhancement filter is applied to segment fibers of different diameters. Morphological operations are then employed to extract the fiber centerlines, from which a 3D connectivity map is computed. Applying this approach to an initial data set yielded 2% equal error rate for segmentation and 92% accuracy for end-to-end fiber tracing (22 out of 24 hand-traced fibers). Future work calls for scaling up the algorithm to process much larger brain datasets (terabytes and above) and performing graph-based long-range connectivity analysis. This work has the potential to extend our knowledge on brain networks at the cellular level.
| Original language | English |
|---|---|
| Title of host publication | 2017 IEEE 14th International Symposium on Biomedical Imaging, ISBI 2017 |
| Publisher | IEEE Computer Society |
| Pages | 332-336 |
| Number of pages | 5 |
| ISBN (Electronic) | 9781509011711 |
| DOIs | |
| Publication status | Published - 2017 Jun 15 |
| Event | 14th IEEE International Symposium on Biomedical Imaging, ISBI 2017 - Melbourne, Australia Duration: 2017 Apr 18 → 2017 Apr 21 |
Publication series
| Name | Proceedings - International Symposium on Biomedical Imaging |
|---|---|
| ISSN (Print) | 1945-7928 |
| ISSN (Electronic) | 1945-8452 |
Other
| Other | 14th IEEE International Symposium on Biomedical Imaging, ISBI 2017 |
|---|---|
| Country | Australia |
| City | Melbourne |
| Period | 17/4/18 → 17/4/21 |
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All Science Journal Classification (ASJC) codes
- Biomedical Engineering
- Radiology Nuclear Medicine and imaging
Cite this
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Automated dense neuronal fiber tracing and connectivity mapping at cellular level. / Brattain, Laura J.; Telfer, Brian A.; Samsi, Siddharth; Ku, Taeyun; Choi, Heejin; Chung, Kwanghun.
2017 IEEE 14th International Symposium on Biomedical Imaging, ISBI 2017. IEEE Computer Society, 2017. p. 332-336 7950531 (Proceedings - International Symposium on Biomedical Imaging).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Automated dense neuronal fiber tracing and connectivity mapping at cellular level
AU - Brattain, Laura J.
AU - Telfer, Brian A.
AU - Samsi, Siddharth
AU - Ku, Taeyun
AU - Choi, Heejin
AU - Chung, Kwanghun
PY - 2017/6/15
Y1 - 2017/6/15
N2 - With the advancement of high throughput and high resolution volumetric brain imaging, there is an unmet need to trace dense neuron fibers and study long-range neuron connectivity. An initial pipeline is described for processing cellular-level neuronal fiber data acquired by a new super resolution imaging method called Magnified Analysis of the Proteome (MAP). First, a multiscale vessel enhancement filter is applied to segment fibers of different diameters. Morphological operations are then employed to extract the fiber centerlines, from which a 3D connectivity map is computed. Applying this approach to an initial data set yielded 2% equal error rate for segmentation and 92% accuracy for end-to-end fiber tracing (22 out of 24 hand-traced fibers). Future work calls for scaling up the algorithm to process much larger brain datasets (terabytes and above) and performing graph-based long-range connectivity analysis. This work has the potential to extend our knowledge on brain networks at the cellular level.
AB - With the advancement of high throughput and high resolution volumetric brain imaging, there is an unmet need to trace dense neuron fibers and study long-range neuron connectivity. An initial pipeline is described for processing cellular-level neuronal fiber data acquired by a new super resolution imaging method called Magnified Analysis of the Proteome (MAP). First, a multiscale vessel enhancement filter is applied to segment fibers of different diameters. Morphological operations are then employed to extract the fiber centerlines, from which a 3D connectivity map is computed. Applying this approach to an initial data set yielded 2% equal error rate for segmentation and 92% accuracy for end-to-end fiber tracing (22 out of 24 hand-traced fibers). Future work calls for scaling up the algorithm to process much larger brain datasets (terabytes and above) and performing graph-based long-range connectivity analysis. This work has the potential to extend our knowledge on brain networks at the cellular level.
UR - http://www.scopus.com/inward/record.url?scp=85023162828&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85023162828&partnerID=8YFLogxK
U2 - 10.1109/ISBI.2017.7950531
DO - 10.1109/ISBI.2017.7950531
M3 - Conference contribution
T3 - Proceedings - International Symposium on Biomedical Imaging
SP - 332
EP - 336
BT - 2017 IEEE 14th International Symposium on Biomedical Imaging, ISBI 2017
PB - IEEE Computer Society
ER -