TY - GEN
T1 - Comparing Training Strategies Using Multi-Assessor Segmentation Labels for Barrett’s Neoplasia Detection
AU - Boers, Tim G. W.
AU - Kusters, Carolus H. J.
AU - Fockens, Kiki N.
AU - Jukema, Jelmer B.
AU - Jong, Martijn R.
AU - de Groof, Jeroen
AU - Bergman, Jacques J.
AU - van der Sommen, Fons
AU - de With, Peter H. N.
N1 - Publisher Copyright: © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - In medical imaging, segmentation ground truths generally suffer from large inter-observer variability. When multiple observers are used, simple fusion techniques are typically employed to combine multiple delineations into one consensus ground truth. However, in this process, potentially valuable information is discarded and it is yet unknown what strategy leads to optimal segmentation results. In this work, we compare several ground-truth types to train a U-net and apply it to the clinical use case of Barrett’s neoplasia detection. To this end, we have invited 14 international Barrett’s experts to delineate 2,851 neoplastic images derived from 812 patients into a higher- and lower-likelihood neoplasia areas. Five different ground-truths techniques along with four different training losses are compared with each other using the Area-under-the-curve (AUC) value for Barrett’s neoplasia detection. The value used to generate this curve is the maximum pixel value in the raw segmentation map, and the histologically proven ground truth of the image. The experiments show that random sampling of the four neoplastic areas together with a compound loss Binary Cross-entropy and DICE yields the highest value of 94.12%, while fusion-based ground truth clearly performs lower. The results show that researchers should incorporate measures for uncertainty in their design of networks.
AB - In medical imaging, segmentation ground truths generally suffer from large inter-observer variability. When multiple observers are used, simple fusion techniques are typically employed to combine multiple delineations into one consensus ground truth. However, in this process, potentially valuable information is discarded and it is yet unknown what strategy leads to optimal segmentation results. In this work, we compare several ground-truth types to train a U-net and apply it to the clinical use case of Barrett’s neoplasia detection. To this end, we have invited 14 international Barrett’s experts to delineate 2,851 neoplastic images derived from 812 patients into a higher- and lower-likelihood neoplasia areas. Five different ground-truths techniques along with four different training losses are compared with each other using the Area-under-the-curve (AUC) value for Barrett’s neoplasia detection. The value used to generate this curve is the maximum pixel value in the raw segmentation map, and the histologically proven ground truth of the image. The experiments show that random sampling of the four neoplastic areas together with a compound loss Binary Cross-entropy and DICE yields the highest value of 94.12%, while fusion-based ground truth clearly performs lower. The results show that researchers should incorporate measures for uncertainty in their design of networks.
KW - Interobserver variance
KW - Neural networks
KW - Segmentation
UR - http://www.scopus.com/inward/record.url?scp=85140456697&partnerID=8YFLogxK
U2 - https://doi.org/10.1007/978-3-031-17979-2_13
DO - https://doi.org/10.1007/978-3-031-17979-2_13
M3 - Conference contribution
SN - 9783031179785
VL - 13581 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 131
EP - 138
BT - Cancer Prevention Through Early Detection - 1st International Workshop, CaPTion 2022, Held in Conjunction with MICCAI 2022, Proceedings
A2 - Ali, Sharib
A2 - van der Sommen, Fons
A2 - van Eijnatten, Maureen
A2 - Kolenbrander, Iris
A2 - Papież, Bartłomiej Władysław
A2 - Jin, Yueming
PB - Springer Science and Business Media Deutschland GmbH
T2 - 1st International Workshop on Cancer Prevention through Early Detection, CaPTion 2022, held in conjunction with the 25th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2022
Y2 - 22 September 2022 through 22 September 2022
ER -