TY - GEN
T1 - Teacher-Student Semi-supervised Approach for Medical Image Segmentation
AU - Baldeon Calisto, Maria
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - Accurate segmentation of anatomical structures is a critical step for medical image analysis. Deep learning architectures have become the state-of-the-art models for automatic medical image segmentation. However, these models require an extensive labelled dataset to achieve a high performance. Given that obtaining annotated medical datasets is very expensive, in this work we present a two-phase teacher-student approach for semi-supervised learning. In phase 1, a three network U-Net ensemble, denominated the teacher, is trained using the labelled dataset. In phase 2, a student U-Net network is trained with the labelled dataset and the unlabelled dataset with pseudo-labels produced with the teacher network. The student network is then used for inference of the testing images. The proposed approach is evaluated on the task of abdominal segmentation from the FLARE2022 challenge, achieving a mean 0.53 dice, 0.57 NSD, and 44.97 prediction time on the validation set.
AB - Accurate segmentation of anatomical structures is a critical step for medical image analysis. Deep learning architectures have become the state-of-the-art models for automatic medical image segmentation. However, these models require an extensive labelled dataset to achieve a high performance. Given that obtaining annotated medical datasets is very expensive, in this work we present a two-phase teacher-student approach for semi-supervised learning. In phase 1, a three network U-Net ensemble, denominated the teacher, is trained using the labelled dataset. In phase 2, a student U-Net network is trained with the labelled dataset and the unlabelled dataset with pseudo-labels produced with the teacher network. The student network is then used for inference of the testing images. The proposed approach is evaluated on the task of abdominal segmentation from the FLARE2022 challenge, achieving a mean 0.53 dice, 0.57 NSD, and 44.97 prediction time on the validation set.
KW - Image segmentation
KW - Medical image analysis
KW - Semi-supervised learning
UR - http://www.scopus.com/inward/record.url?scp=85149668610&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-23911-3_14
DO - 10.1007/978-3-031-23911-3_14
M3 - Contribución a la conferencia
AN - SCOPUS:85149668610
SN - 9783031239106
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 152
EP - 162
BT - Fast and Low-Resource Semi-supervised Abdominal Organ Segmentation - MICCAI 2022 Challenge, FLARE 2022, Held in Conjunction with MICCAI 2022, Proceedings
A2 - Ma, Jun
A2 - Wang, Bo
PB - Springer Science and Business Media Deutschland GmbH
T2 - International challenge on Fast and Lowresource Semi-supervised Abdominal Organ Segmentation in CT Scans, FLARE 2022 held in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2022
Y2 - 22 September 2022 through 22 September 2022
ER -