TY - GEN
T1 - A novel clinical gland feature for detection of early Barrett's neoplasia using volumetric laser endomicroscopy
AU - Scheeve, Thom
AU - Struyvenberg, Maarten R.
AU - Curvers, Wouter L.
AU - de Groof, Albert J.
AU - Schoon, Erik J.
AU - Bergman, Jacques J. G. H. M.
AU - van der Sommen, Fons
AU - de With, Peter H. N.
PY - 2019
Y1 - 2019
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.
UR - https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85067235868&origin=inward
U2 - https://doi.org/10.1117/12.2508244
DO - https://doi.org/10.1117/12.2508244
M3 - Conference contribution
VL - 10950
T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE
BT - Medical Imaging 2019: Computer-Aided Diagnosis
A2 - Mori, Kensaku
A2 - Hahn, Horst K.
PB - SPIE
T2 - Medical Imaging 2019: Computer-Aided Diagnosis
Y2 - 17 February 2019 through 20 February 2019
ER -