TY - GEN
T1 - Deep learning biopsy marking of early neoplasia in barrett's esophagus by combining wle and BLI modalities
AU - Putten, Joost Van Der
AU - Wildeboer, Rogier
AU - Groof, Jeroen De
AU - Sloun, Ruud Van
AU - Struyvenberg, Maarten
AU - Sommen, Fons Van Der
AU - Zinger, Svitlana
AU - Curvers, Wouter
AU - Schoon, Erik
AU - Bergman, Jacques
AU - Peter, H. N. De With
PY - 2019
Y1 - 2019
N2 - Esophageal cancer is the fastest rising type of cancer in the western world. Also, early neoplasia in Barrett's esophagus (BE) is difficult to detect for endoscopists and research has shown it is similarly complicated for Computer-Aided Detection (CAD) algorithms. For these reasons, further development of CAD algorithms for BE is essential for the wellbeing of patients. In this work we propose a patch-based deep learning algorithm for early neoplasia in BE, utilizing state-of-the-art deep learning techniques on a new prospective data set. The new algorithm yields not only a high detection score but also identifies the ideal biopsy location for the first time. We define specific novel metrics such as sweet-spot flag and softspot flag, to obtain well-defined computation of the biopsy location. Furthermore, we show that combining white light and blue laser imaging improves localization results by 8%.
AB - Esophageal cancer is the fastest rising type of cancer in the western world. Also, early neoplasia in Barrett's esophagus (BE) is difficult to detect for endoscopists and research has shown it is similarly complicated for Computer-Aided Detection (CAD) algorithms. For these reasons, further development of CAD algorithms for BE is essential for the wellbeing of patients. In this work we propose a patch-based deep learning algorithm for early neoplasia in BE, utilizing state-of-the-art deep learning techniques on a new prospective data set. The new algorithm yields not only a high detection score but also identifies the ideal biopsy location for the first time. We define specific novel metrics such as sweet-spot flag and softspot flag, to obtain well-defined computation of the biopsy location. Furthermore, we show that combining white light and blue laser imaging improves localization results by 8%.
UR - https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85073915033&origin=inward
U2 - https://doi.org/10.1109/ISBI.2019.8759431
DO - https://doi.org/10.1109/ISBI.2019.8759431
M3 - Conference contribution
VL - 2019-April
T3 - Proceedings - International Symposium on Biomedical Imaging
SP - 1127
EP - 1131
BT - ISBI 2019 - 2019 IEEE International Symposium on Biomedical Imaging
PB - IEEE Computer Society
T2 - 16th IEEE International Symposium on Biomedical Imaging, ISBI 2019
Y2 - 8 April 2019 through 11 April 2019
ER -