Abstract
In brain cancer surgery, it is critical to achieve extensive resection without compromising adjacent healthy, non-cancerous regions. Various technological advances have made major contributions in imaging, including intraoperative magnetic imaging (MRI) and computed tomography (CT). However, these technologies have pros and cons in providing quantitative, real-time and three-dimensional (3D) continuous guidance in brain cancer detection. Optical Coherence Tomography (OCT) is a non-invasive, label-free, cost-effective technique capable of imaging tissue in three dimensions and real time. The purpose of this study is to reliably and efficiently discriminate between non-cancer and cancer-infiltrated brain regions using OCT images. To this end, a mathematical model for quantitative evaluation known as the Blind End-Member and Abundances Extraction method (BEAE). This BEAE method is a constrained optimization technique which extracts spatial information from volumetric OCT images. Using this novel method, we are able to discriminate between cancerous and non-cancerous tissues and using logistic regression as a classifier for automatic brain tumor margin detection. Using this technique, we are able to achieve excellent performance using an extensive cross-validation of the training dataset (sensitivity 92.91% and specificity 98.15%) and again using an independent, blinded validation dataset (sensitivity 92.91% and specificity 86.36%). In summary, BEAE is well-suited to differentiate brain tissue which could support the guiding surgery process for tissue resection.
| Original language | English (US) |
|---|---|
| Title of host publication | Medical Imaging 2018 |
| Subtitle of host publication | Computer-Aided Diagnosis |
| Publisher | SPIE |
| Volume | 10575 |
| ISBN (Electronic) | 9781510616394 |
| DOIs | |
| State | Published - Jan 1 2018 |
| Event | Medical Imaging 2018: Computer-Aided Diagnosis - Houston, United States Duration: Feb 12 2018 → Feb 15 2018 |
Other
| Other | Medical Imaging 2018: Computer-Aided Diagnosis |
|---|---|
| Country | United States |
| City | Houston |
| Period | 2/12/18 → 2/15/18 |
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Keywords
- Brain Cancer
- Image-guided surgery
- Machine Learning
- Optical Coherence Tomography
- Quadratic Optimization
ASJC Scopus subject areas
- Electronic, Optical and Magnetic Materials
- Biomaterials
- Atomic and Molecular Physics, and Optics
- Radiology Nuclear Medicine and imaging
Cite this
Detection of brain tumor margins using optical coherence tomography. / Juarez-Chambi, Ronald M.; Kut, Carmen; Rico-Jimenez, Jesus; Campos-Delgado, Daniel U.; Quinones-Hinojosa, Alfredo; Li, Xingde; Jo, Javier.
Medical Imaging 2018: Computer-Aided Diagnosis. Vol. 10575 SPIE, 2018. 105751R.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Detection of brain tumor margins using optical coherence tomography
AU - Juarez-Chambi, Ronald M.
AU - Kut, Carmen
AU - Rico-Jimenez, Jesus
AU - Campos-Delgado, Daniel U.
AU - Quinones-Hinojosa, Alfredo
AU - Li, Xingde
AU - Jo, Javier
PY - 2018/1/1
Y1 - 2018/1/1
N2 - In brain cancer surgery, it is critical to achieve extensive resection without compromising adjacent healthy, non-cancerous regions. Various technological advances have made major contributions in imaging, including intraoperative magnetic imaging (MRI) and computed tomography (CT). However, these technologies have pros and cons in providing quantitative, real-time and three-dimensional (3D) continuous guidance in brain cancer detection. Optical Coherence Tomography (OCT) is a non-invasive, label-free, cost-effective technique capable of imaging tissue in three dimensions and real time. The purpose of this study is to reliably and efficiently discriminate between non-cancer and cancer-infiltrated brain regions using OCT images. To this end, a mathematical model for quantitative evaluation known as the Blind End-Member and Abundances Extraction method (BEAE). This BEAE method is a constrained optimization technique which extracts spatial information from volumetric OCT images. Using this novel method, we are able to discriminate between cancerous and non-cancerous tissues and using logistic regression as a classifier for automatic brain tumor margin detection. Using this technique, we are able to achieve excellent performance using an extensive cross-validation of the training dataset (sensitivity 92.91% and specificity 98.15%) and again using an independent, blinded validation dataset (sensitivity 92.91% and specificity 86.36%). In summary, BEAE is well-suited to differentiate brain tissue which could support the guiding surgery process for tissue resection.
AB - In brain cancer surgery, it is critical to achieve extensive resection without compromising adjacent healthy, non-cancerous regions. Various technological advances have made major contributions in imaging, including intraoperative magnetic imaging (MRI) and computed tomography (CT). However, these technologies have pros and cons in providing quantitative, real-time and three-dimensional (3D) continuous guidance in brain cancer detection. Optical Coherence Tomography (OCT) is a non-invasive, label-free, cost-effective technique capable of imaging tissue in three dimensions and real time. The purpose of this study is to reliably and efficiently discriminate between non-cancer and cancer-infiltrated brain regions using OCT images. To this end, a mathematical model for quantitative evaluation known as the Blind End-Member and Abundances Extraction method (BEAE). This BEAE method is a constrained optimization technique which extracts spatial information from volumetric OCT images. Using this novel method, we are able to discriminate between cancerous and non-cancerous tissues and using logistic regression as a classifier for automatic brain tumor margin detection. Using this technique, we are able to achieve excellent performance using an extensive cross-validation of the training dataset (sensitivity 92.91% and specificity 98.15%) and again using an independent, blinded validation dataset (sensitivity 92.91% and specificity 86.36%). In summary, BEAE is well-suited to differentiate brain tissue which could support the guiding surgery process for tissue resection.
KW - Brain Cancer
KW - Image-guided surgery
KW - Machine Learning
KW - Optical Coherence Tomography
KW - Quadratic Optimization
UR - http://www.scopus.com/inward/record.url?scp=85046257778&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85046257778&partnerID=8YFLogxK
U2 - 10.1117/12.2293599
DO - 10.1117/12.2293599
M3 - Conference contribution
AN - SCOPUS:85046257778
VL - 10575
BT - Medical Imaging 2018
PB - SPIE
ER -