1
|
Li G, Otake Y, Soufi M, Taniguchi M, Yagi M, Ichihashi N, Uemura K, Takao M, Sugano N, Sato Y. Hybrid representation-enhanced sampling for Bayesian active learning in musculoskeletal segmentation of lower extremities. Int J Comput Assist Radiol Surg 2024:10.1007/s11548-024-03065-7. [PMID: 38282095 DOI: 10.1007/s11548-024-03065-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Accepted: 01/10/2024] [Indexed: 01/30/2024]
Abstract
PURPOSE Manual annotations for training deep learning models in auto-segmentation are time-intensive. This study introduces a hybrid representation-enhanced sampling strategy that integrates both density and diversity criteria within an uncertainty-based Bayesian active learning (BAL) framework to reduce annotation efforts by selecting the most informative training samples. METHODS The experiments are performed on two lower extremity datasets of MRI and CT images, focusing on the segmentation of the femur, pelvis, sacrum, quadriceps femoris, hamstrings, adductors, sartorius, and iliopsoas, utilizing a U-net-based BAL framework. Our method selects uncertain samples with high density and diversity for manual revision, optimizing for maximal similarity to unlabeled instances and minimal similarity to existing training data. We assess the accuracy and efficiency using dice and a proposed metric called reduced annotation cost (RAC), respectively. We further evaluate the impact of various acquisition rules on BAL performance and design an ablation study for effectiveness estimation. RESULTS In MRI and CT datasets, our method was superior or comparable to existing ones, achieving a 0.8% dice and 1.0% RAC increase in CT (statistically significant), and a 0.8% dice and 1.1% RAC increase in MRI (not statistically significant) in volume-wise acquisition. Our ablation study indicates that combining density and diversity criteria enhances the efficiency of BAL in musculoskeletal segmentation compared to using either criterion alone. CONCLUSION Our sampling method is proven efficient in reducing annotation costs in image segmentation tasks. The combination of the proposed method and our BAL framework provides a semi-automatic way for efficient annotation of medical image datasets.
Collapse
Affiliation(s)
- Ganping Li
- Division of Information Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, 8916-5 Takayama, Ikoma, Nara, 630-0192, Japan.
| | - Yoshito Otake
- Division of Information Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, 8916-5 Takayama, Ikoma, Nara, 630-0192, Japan
| | - Mazen Soufi
- Division of Information Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, 8916-5 Takayama, Ikoma, Nara, 630-0192, Japan
| | - Masashi Taniguchi
- Human Health Sciences, Graduate School of Medicine, Kyoto University, 53-Kawahara-cho, Shogoin, Sakyo-ku, Kyoto, 606-8507, Japan
| | - Masahide Yagi
- Human Health Sciences, Graduate School of Medicine, Kyoto University, 53-Kawahara-cho, Shogoin, Sakyo-ku, Kyoto, 606-8507, Japan
| | - Noriaki Ichihashi
- Human Health Sciences, Graduate School of Medicine, Kyoto University, 53-Kawahara-cho, Shogoin, Sakyo-ku, Kyoto, 606-8507, Japan
| | - Keisuke Uemura
- Department of Orthopedic Surgery, Osaka University Graduate School of Medicine, Osaka University, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Masaki Takao
- Department of Bone and Joint Surgery, School of Medicine, Ehime University, 454 Shitsugawa, Toon, Ehime, 791-0295, Japan
| | - Nobuhiko Sugano
- Department of Orthopedic Surgery, Osaka University Graduate School of Medicine, Osaka University, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Yoshinobu Sato
- Division of Information Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, 8916-5 Takayama, Ikoma, Nara, 630-0192, Japan
| |
Collapse
|