TY - GEN
T1 - Transfer Learning for KiTS21 Challenge
AU - Yang, Xi
AU - Zhang, Jianpeng
AU - Zhang, Jing
AU - Xia, Yong
N1 - Publisher Copyright:
© 2022, Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - Transfer learning has witnessed a recent surge of interest after proving successful in multiple applications. However, it highly relies on the quantity of annotated data. Constrained by the labor cost and expertise, it is hard to annotate sufficient organs and tumors at the voxel level for medical image segmentation. Consequently, most bench-mark datasets were collected for the segmentation of only one type of organ and/or tumor, and all task-irrelevant organs and tumors were annotated as the background. We aim to make use of these partially but plentifully labeled datasets to boost the segmentation performance of the annotation-limited KiTS21 segmentation task. To this end, we first construct a general medical image segmentation model that learns to segment these partially labeled organs or tumors. Then we transfer its pre-trained weights to a specific downstream task, i.e., KiTS21. The primary experiments demonstrate the effectiveness of the proposed transfer learning strategy. Our method achieves 0.890 Dice score, 0.805 SurfaceDice, and 0.822 Tumor Dice in the KiTS21 challenge.
AB - Transfer learning has witnessed a recent surge of interest after proving successful in multiple applications. However, it highly relies on the quantity of annotated data. Constrained by the labor cost and expertise, it is hard to annotate sufficient organs and tumors at the voxel level for medical image segmentation. Consequently, most bench-mark datasets were collected for the segmentation of only one type of organ and/or tumor, and all task-irrelevant organs and tumors were annotated as the background. We aim to make use of these partially but plentifully labeled datasets to boost the segmentation performance of the annotation-limited KiTS21 segmentation task. To this end, we first construct a general medical image segmentation model that learns to segment these partially labeled organs or tumors. Then we transfer its pre-trained weights to a specific downstream task, i.e., KiTS21. The primary experiments demonstrate the effectiveness of the proposed transfer learning strategy. Our method achieves 0.890 Dice score, 0.805 SurfaceDice, and 0.822 Tumor Dice in the KiTS21 challenge.
KW - Kidney tumor segmentation
KW - Limited annotation
KW - Transfer learning
UR - http://www.scopus.com/inward/record.url?scp=85127692004&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-98385-7_21
DO - 10.1007/978-3-030-98385-7_21
M3 - 会议稿件
AN - SCOPUS:85127692004
SN - 9783030983840
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 158
EP - 163
BT - Kidney and Kidney Tumor Segmentation - MICCAI 2021 Challenge, KiTS 2021, Held in Conjunction with MICCAI 2021, Proceedings
A2 - Heller, Nicholas
A2 - Isensee, Fabian
A2 - Trofimova, Darya
A2 - Tejpaul, Resha
A2 - Papanikolopoulos, Nikolaos
A2 - Weight, Christopher
PB - Springer Science and Business Media Deutschland GmbH
T2 - 2nd International challenge on Kidney and Kidney Tumor Segmentation, KiTS 2021 held in conjunction with 24th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021
Y2 - 27 September 2021 through 27 September 2021
ER -