Abstract
We present a conditional random field for three-dimensional neurite tracing, using a population of open curve active contours. We aim at increased robustness under spatially varying neurite-background contrast, and at the same time reducing the computational complexity compared to the state-of-the-art. While most existing active contour based methods perform tracing by evolving multiple snakes along the neurite centerline in a sequential manner, our approach implements a simultaneous evolution, reducing the complexity as we show theoretically in our algorithm analysis and experimentally. Our approach provides increased accuracy in ambiguous regions (e.g., low contrast, neurite bifurcations and crossovers, etc.), by exploiting interactions among spatially neighboring snakes. We illustrate the performance of our method and compare it with existing frameworks using sample volumes of wild-type sensory neurons in the larval Drosophila.
| Original language | English (US) |
|---|---|
| Title of host publication | 2016 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2016 - Proceedings |
| Publisher | IEEE Computer Society |
| Pages | 593-597 |
| Number of pages | 5 |
| Volume | 2016-June |
| ISBN (Electronic) | 9781479923502 |
| DOIs | |
| State | Published - Jun 15 2016 |
| Event | 2016 IEEE 13th International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2016 - Prague, Czech Republic Duration: Apr 13 2016 → Apr 16 2016 |
Other
| Other | 2016 IEEE 13th International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2016 |
|---|---|
| Country | Czech Republic |
| City | Prague |
| Period | 4/13/16 → 4/16/16 |
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Keywords
- active contours
- CRF inference
- Drosophila
- neurite tracing
ASJC Scopus subject areas
- Biomedical Engineering
- Radiology Nuclear Medicine and imaging
Cite this
CRF formulation of active contour population for efficient three-dimensional neurite tracing. / Gulyanon, S.; Sharifai, N.; Kim, M. D.; Chiba, Akira; Tsechpenakis, G.
2016 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2016 - Proceedings. Vol. 2016-June IEEE Computer Society, 2016. p. 593-597 7493338.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - CRF formulation of active contour population for efficient three-dimensional neurite tracing
AU - Gulyanon, S.
AU - Sharifai, N.
AU - Kim, M. D.
AU - Chiba, Akira
AU - Tsechpenakis, G.
PY - 2016/6/15
Y1 - 2016/6/15
N2 - We present a conditional random field for three-dimensional neurite tracing, using a population of open curve active contours. We aim at increased robustness under spatially varying neurite-background contrast, and at the same time reducing the computational complexity compared to the state-of-the-art. While most existing active contour based methods perform tracing by evolving multiple snakes along the neurite centerline in a sequential manner, our approach implements a simultaneous evolution, reducing the complexity as we show theoretically in our algorithm analysis and experimentally. Our approach provides increased accuracy in ambiguous regions (e.g., low contrast, neurite bifurcations and crossovers, etc.), by exploiting interactions among spatially neighboring snakes. We illustrate the performance of our method and compare it with existing frameworks using sample volumes of wild-type sensory neurons in the larval Drosophila.
AB - We present a conditional random field for three-dimensional neurite tracing, using a population of open curve active contours. We aim at increased robustness under spatially varying neurite-background contrast, and at the same time reducing the computational complexity compared to the state-of-the-art. While most existing active contour based methods perform tracing by evolving multiple snakes along the neurite centerline in a sequential manner, our approach implements a simultaneous evolution, reducing the complexity as we show theoretically in our algorithm analysis and experimentally. Our approach provides increased accuracy in ambiguous regions (e.g., low contrast, neurite bifurcations and crossovers, etc.), by exploiting interactions among spatially neighboring snakes. We illustrate the performance of our method and compare it with existing frameworks using sample volumes of wild-type sensory neurons in the larval Drosophila.
KW - active contours
KW - CRF inference
KW - Drosophila
KW - neurite tracing
UR - http://www.scopus.com/inward/record.url?scp=84978436613&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84978436613&partnerID=8YFLogxK
U2 - 10.1109/ISBI.2016.7493338
DO - 10.1109/ISBI.2016.7493338
M3 - Conference contribution
AN - SCOPUS:84978436613
VL - 2016-June
SP - 593
EP - 597
BT - 2016 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2016 - Proceedings
PB - IEEE Computer Society
ER -