Abstract
As optical coherence tomography (OCT) has increasingly become a standard modality for imaging the retina, automated algorithms for processing OCT data have become necessary to do large scale studies looking for changes in specific layers. To provide accurate results, many of these algorithms rely on the consistency of layer intensities within a scan. Unfortunately, OCT data often exhibits inhomogeneity in a given layer's intensities, both within and between images. This problem negatively affects the performance of segmentation algorithms and little prior work has been done to correct this data. In this work, we adapt the N3 framework for intensity inhomogeneity correction, which was originally developed to correct MRI data, to work for macular OCT data. We first transform the data to a flattened macular space to create a template intensity profile for each layer giving us an accurate initial estimate of the gain field. N3 will then produce a smoothly varying field to correct the data. We show that our method is able to both accurately recover synthetically generated gain fields and improves the stability of the layer intensities.
| 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 | 197-200 |
| Number of pages | 4 |
| 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
- flat space
- inhomogeneity correction
- OCT
- retina
ASJC Scopus subject areas
- Biomedical Engineering
- Radiology Nuclear Medicine and imaging
Cite this
Intensity inhomogeneity correction of macular OCT using N3 and retinal flatspace. / Lang, Andrew; Carass, Aaron; Jedynak, Bruno M.; Solomon, Sharon; Calabresi, Peter; Prince, Jerry Ladd.
2016 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2016 - Proceedings. Vol. 2016-June IEEE Computer Society, 2016. p. 197-200 7493243.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Intensity inhomogeneity correction of macular OCT using N3 and retinal flatspace
AU - Lang, Andrew
AU - Carass, Aaron
AU - Jedynak, Bruno M.
AU - Solomon, Sharon
AU - Calabresi, Peter
AU - Prince, Jerry Ladd
PY - 2016/6/15
Y1 - 2016/6/15
N2 - As optical coherence tomography (OCT) has increasingly become a standard modality for imaging the retina, automated algorithms for processing OCT data have become necessary to do large scale studies looking for changes in specific layers. To provide accurate results, many of these algorithms rely on the consistency of layer intensities within a scan. Unfortunately, OCT data often exhibits inhomogeneity in a given layer's intensities, both within and between images. This problem negatively affects the performance of segmentation algorithms and little prior work has been done to correct this data. In this work, we adapt the N3 framework for intensity inhomogeneity correction, which was originally developed to correct MRI data, to work for macular OCT data. We first transform the data to a flattened macular space to create a template intensity profile for each layer giving us an accurate initial estimate of the gain field. N3 will then produce a smoothly varying field to correct the data. We show that our method is able to both accurately recover synthetically generated gain fields and improves the stability of the layer intensities.
AB - As optical coherence tomography (OCT) has increasingly become a standard modality for imaging the retina, automated algorithms for processing OCT data have become necessary to do large scale studies looking for changes in specific layers. To provide accurate results, many of these algorithms rely on the consistency of layer intensities within a scan. Unfortunately, OCT data often exhibits inhomogeneity in a given layer's intensities, both within and between images. This problem negatively affects the performance of segmentation algorithms and little prior work has been done to correct this data. In this work, we adapt the N3 framework for intensity inhomogeneity correction, which was originally developed to correct MRI data, to work for macular OCT data. We first transform the data to a flattened macular space to create a template intensity profile for each layer giving us an accurate initial estimate of the gain field. N3 will then produce a smoothly varying field to correct the data. We show that our method is able to both accurately recover synthetically generated gain fields and improves the stability of the layer intensities.
KW - flat space
KW - inhomogeneity correction
KW - OCT
KW - retina
UR - http://www.scopus.com/inward/record.url?scp=84978402427&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84978402427&partnerID=8YFLogxK
U2 - 10.1109/ISBI.2016.7493243
DO - 10.1109/ISBI.2016.7493243
M3 - Conference contribution
C2 - 27695603
AN - SCOPUS:84978402427
VL - 2016-June
SP - 197
EP - 200
BT - 2016 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2016 - Proceedings
PB - IEEE Computer Society
ER -