TY - GEN
T1 - A convolutional neural network method for boundary optimization enables few-shot learning for biomedical image segmentation
AU - Rutter, Erica M.
AU - Lagergren, John H.
AU - Flores, Kevin B.
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2019.
PY - 2019
Y1 - 2019
N2 - Obtaining large amounts of annotated biomedical data to train convolutional neural networks (CNNs) for image segmentation is expensive. We propose a method that requires only a few segmentation examples to accurately train a semi-automated segmentation algorithm. Our algorithm, a convolutional neural network method for boundary optimization (CoMBO), can be used to rapidly outline object boundaries using orders of magnitude less annotation than full segmentation masks, i.e., only a few pixels per image. We found that CoMBO is significantly more accurate than state-of-the-art machine learning methods such as Mask R-CNN. We also show how we can use CoMBO predictions, when CoMBO is trained on just 3 images, to rapidly create large amounts of accurate training data for Mask R-CNN. Our few-shot method is demonstrated on ISBI cell tracking challenge datasets.
AB - Obtaining large amounts of annotated biomedical data to train convolutional neural networks (CNNs) for image segmentation is expensive. We propose a method that requires only a few segmentation examples to accurately train a semi-automated segmentation algorithm. Our algorithm, a convolutional neural network method for boundary optimization (CoMBO), can be used to rapidly outline object boundaries using orders of magnitude less annotation than full segmentation masks, i.e., only a few pixels per image. We found that CoMBO is significantly more accurate than state-of-the-art machine learning methods such as Mask R-CNN. We also show how we can use CoMBO predictions, when CoMBO is trained on just 3 images, to rapidly create large amounts of accurate training data for Mask R-CNN. Our few-shot method is demonstrated on ISBI cell tracking challenge datasets.
KW - Biomedical image segmentation
KW - Convolutional neural network
KW - Few shot learning
UR - http://www.scopus.com/inward/record.url?scp=85075665137&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-33391-1_22
DO - 10.1007/978-3-030-33391-1_22
M3 - Conference contribution
AN - SCOPUS:85075665137
SN - 9783030333904
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 190
EP - 198
BT - Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data First MICCAI Workshop, DART 2019 and First International Workshop, MIL3ID 2019 Shenzhen, Held in Conjunction with MICCAI 2019 Shenzhen, 2019 Proceedings
A2 - Wang, Qian
A2 - Milletari, Fausto
A2 - Rieke, Nicola
A2 - Nguyen, Hien V.
A2 - Roysam, Badri
A2 - Albarqouni, Shadi
A2 - Cardoso, M. Jorge
A2 - Xu, Ziyue
A2 - Kamnitsas, Konstantinos
A2 - Patel, Vishal
A2 - Jiang, Steve
A2 - Zhou, Kevin
A2 - Luu, Khoa
A2 - Le, Ngan
PB - Springer
T2 - 1st MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2019, and the 1st International Workshop on Medical Image Learning with Less Labels and Imperfect Data, MIL3ID 2019, held in conjunction with 22nd International Conference on Medical Image Computing and Computer- Assisted Intervention, MICCAI 2019
Y2 - 13 October 2019 through 17 October 2019
ER -