Abstract
| Language | English |
|---|---|
| Title of host publication | Medical Imaging 2019: Computer-Aided Diagnosis |
| Subtitle of host publication | Computer-Aided Diagnosis |
| Editors | Kensaku Mori, Horst K. Hahn |
| Place of Publication | Bellingham |
| Publisher | SPIE |
| Number of pages | 6 |
| ISBN (Electronic) | 9781510625471 |
| DOIs | |
| State | Published - 13 Mar 2019 |
| Event | Medical Imaging 2019: Computer-Aided Diagnosis - San Diego, United States Duration: 17 Feb 2019 → 20 Feb 2019 |
Publication series
| Name | Proceedings of SPIE |
|---|---|
| Volume | 10950 |
| ISSN (Print) | 0277-786X |
Conference
| Conference | Medical Imaging 2019: Computer-Aided Diagnosis |
|---|---|
| Country | United States |
| City | San Diego |
| Period | 17/02/19 → 20/02/19 |
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Keywords
- Barrett's esophagus
- Computer-aided diagnosis
- Endoscopy
- Esophageal adenocarcinoma
- Volumetric laser endomicroscopy
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A novel clinical gland feature for detection of early Barrett’s neoplasia using volumetric laser endomicroscopy. / Scheeve, T.; Struyvenberg, Maarten R.; Curvers, Wouter L.; de Groof, Jeroen; Schoon, Erik J.; Bergman, Jacques J.G.H.M.; van der Sommen, F.; de With, P.H.N.
Medical Imaging 2019: Computer-Aided Diagnosis: Computer-Aided Diagnosis. ed. / Kensaku Mori; Horst K. Hahn. Bellingham : SPIE, 2019. 10950-70 (Proceedings of SPIE; Vol. 10950).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review
TY - GEN
T1 - A novel clinical gland feature for detection of early Barrett’s neoplasia using volumetric laser endomicroscopy
AU - Scheeve,T.
AU - Struyvenberg,Maarten R.
AU - Curvers,Wouter L.
AU - de Groof,Jeroen
AU - Schoon,Erik J.
AU - Bergman,Jacques J.G.H.M.
AU - van der Sommen,F.
AU - de With,P.H.N.
PY - 2019/3/13
Y1 - 2019/3/13
N2 - Volumetric laser endomicroscopy (VLE) is an advanced imaging system offering a promising solution for the detection of early Barrett’s esophagus (BE) neoplasia. BE is a known precursor lesion for esophageal adenocarcinoma and is often missed during regular endoscopic surveillance of BE patients. VLE provides a circumferential scan of near-microscopic resolution of the esophageal wall up to 3-mm depth, yielding a large amount of data that is hard to interpret in real time. In a preliminary study on an automated analysis system for ex vivo VLE scans, novel quantitative image features were developed for two previously identified clinical VLE features predictive for BE neoplasia, showing promising results. This paper proposes a novel quantitative image feature for a missing third clinical VLE feature. The novel gland-based image feature called “gland statistics” (GS), is compared to several generic image analysis features and the most promising clinically-inspired feature “layer histogram” (LH). All features are evaluated on a clinical, validated data set consisting of 88 non-dysplastic BE and 34 neoplastic in vivo VLE images for eight different widely-used machine learning methods. The new clinically-inspired feature has on average superior classification accuracy (0.84 AUC) compared to the generic image analysis features (0.61 AUC), as well as comparable performance to the LH feature (0.86 AUC). Also, the LH feature achieves superior classification accuracy compared to the generic image analysis features in vivo, confirming previous ex vivo results. Combining the LH and the novel GS features provides even further improvement of the performance (0.88 AUC), showing great promise for the clinical utility of this algorithm to detect early BE neoplasia.
AB - Volumetric laser endomicroscopy (VLE) is an advanced imaging system offering a promising solution for the detection of early Barrett’s esophagus (BE) neoplasia. BE is a known precursor lesion for esophageal adenocarcinoma and is often missed during regular endoscopic surveillance of BE patients. VLE provides a circumferential scan of near-microscopic resolution of the esophageal wall up to 3-mm depth, yielding a large amount of data that is hard to interpret in real time. In a preliminary study on an automated analysis system for ex vivo VLE scans, novel quantitative image features were developed for two previously identified clinical VLE features predictive for BE neoplasia, showing promising results. This paper proposes a novel quantitative image feature for a missing third clinical VLE feature. The novel gland-based image feature called “gland statistics” (GS), is compared to several generic image analysis features and the most promising clinically-inspired feature “layer histogram” (LH). All features are evaluated on a clinical, validated data set consisting of 88 non-dysplastic BE and 34 neoplastic in vivo VLE images for eight different widely-used machine learning methods. The new clinically-inspired feature has on average superior classification accuracy (0.84 AUC) compared to the generic image analysis features (0.61 AUC), as well as comparable performance to the LH feature (0.86 AUC). Also, the LH feature achieves superior classification accuracy compared to the generic image analysis features in vivo, confirming previous ex vivo results. Combining the LH and the novel GS features provides even further improvement of the performance (0.88 AUC), showing great promise for the clinical utility of this algorithm to detect early BE neoplasia.
KW - Barrett's esophagus
KW - Computer-aided diagnosis
KW - Endoscopy
KW - Esophageal adenocarcinoma
KW - Volumetric laser endomicroscopy
UR - http://www.scopus.com/inward/record.url?scp=85067235868&partnerID=8YFLogxK
U2 - 10.1117/12.2508244
DO - 10.1117/12.2508244
M3 - Conference contribution
T3 - Proceedings of SPIE
BT - Medical Imaging 2019: Computer-Aided Diagnosis
PB - SPIE
CY - Bellingham
ER -