Skip to main content

Tipp

Weitere Kapitel dieses Buchs durch Wischen aufrufen

2019 | OriginalPaper | Buchkapitel

Active Learning Technique for Multimodal Brain Tumor Segmentation Using Limited Labeled Images

verfasst von : Dhruv Sharma, Zahil Shanis, Chandan K. Reddy, Samuel Gerber, Andinet Enquobahrie

Erschienen in: Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data

Verlag: Springer International Publishing

share
TEILEN

Abstract

Image segmentation is an essential step in biomedical image analysis. In recent years, deep learning models have achieved significant success in segmentation. However, deep learning requires the availability of large annotated data to train these models, which can be challenging in biomedical imaging domain. In this paper, we aim to accomplish biomedical image segmentation with limited labeled data using active learning. We present a deep active learning framework that selects additional data points to be annotated by combining U-Net with an efficient and effective query strategy to capture the most uncertain and representative points. This algorithm decouples the representative part by first finding the core points in the unlabeled pool and then selecting the most uncertain points from the reduced pool, which are different from the labeled pool. In our experiment, only 13% of the dataset was required with active learning to outperform the model trained on the entire 2018 MICCAI Brain Tumor Segmentation (BraTS) dataset. Thus, active learning reduced the amount of labeled data required for image segmentation without a significant loss in the accuracy.
Literatur
1.
Zurück zum Zitat Zaitoun, N., et al.: Survey on image segmentation techniques. Procedia Comput. Sci. 65, 797–806 (2015) CrossRef Zaitoun, N., et al.: Survey on image segmentation techniques. Procedia Comput. Sci. 65, 797–806 (2015) CrossRef
2.
Zurück zum Zitat Long, J., et al.: Fully convolutional networks for semantic segmentation. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015) Long, J., et al.: Fully convolutional networks for semantic segmentation. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015)
3.
Zurück zum Zitat Girshick, R.B. et al.: Rich feature hierarchies for accurate object detection and semantic segmentation. CoRR. abs/1311.2524 (2013) Girshick, R.B. et al.: Rich feature hierarchies for accurate object detection and semantic segmentation. CoRR. abs/1311.2524 (2013)
4.
Zurück zum Zitat Chen, H., Qi, X., Cheng, J.Z., Heng, P.A.: Deep contextual networks for neuronal structure segmentation. In: AAAI, pp. 1167–1173 (2016) Chen, H., Qi, X., Cheng, J.Z., Heng, P.A.: Deep contextual networks for neuronal structure segmentation. In: AAAI, pp. 1167–1173 (2016)
5.
Zurück zum Zitat Chen, H., Qi, X., Yu, L., Heng, P.A.: DCAN: deep contour-aware networks for accurate gland segmentation. In: CVPR, pp. 2487–2496 (2016) Chen, H., Qi, X., Yu, L., Heng, P.A.: DCAN: deep contour-aware networks for accurate gland segmentation. In: CVPR, pp. 2487–2496 (2016)
7.
Zurück zum Zitat Settles, B.: Active Learning Literature Survey. University of Wisconsin-Madison (2009) Settles, B.: Active Learning Literature Survey. University of Wisconsin-Madison (2009)
8.
Zurück zum Zitat Yin, C., et al.: Deep similarity-based batch mode active learning with exploration-exploitation. In: Raghavan, V. et al. (ed.) ICDM, pp. 575–584. IEEE Computer Society (2017) Yin, C., et al.: Deep similarity-based batch mode active learning with exploration-exploitation. In: Raghavan, V. et al. (ed.) ICDM, pp. 575–584. IEEE Computer Society (2017)
9.
Zurück zum Zitat Yang, L., et al.: Suggestive Annotation: A Deep Active Learning Framework for Biomedical Image Segmentation. CoRR. abs/1706.04737 (2017) Yang, L., et al.: Suggestive Annotation: A Deep Active Learning Framework for Biomedical Image Segmentation. CoRR. abs/1706.04737 (2017)
10.
Zurück zum Zitat Zheng, H., et al.: Biomedical Image Segmentation via Representative Annotation (2019) Zheng, H., et al.: Biomedical Image Segmentation via Representative Annotation (2019)
12.
13.
Zurück zum Zitat Loy, C.C., et al.: Stream-based joint exploration-exploitation active learning. In: CVPR, pp. 1560–1567. IEEE Computer Society (2012) Loy, C.C., et al.: Stream-based joint exploration-exploitation active learning. In: CVPR, pp. 1560–1567. IEEE Computer Society (2012)
14.
Zurück zum Zitat Guo, Y., Schuurmans, D.: Discriminative batch mode active learning. In: Platt, J.C. et al. (ed.) NIPS, pp. 593–600. Curran Associates, Inc. (2007) Guo, Y., Schuurmans, D.: Discriminative batch mode active learning. In: Platt, J.C. et al. (ed.) NIPS, pp. 593–600. Curran Associates, Inc. (2007)
15.
Zurück zum Zitat Xu, H., Wang, X., Liao, Y., Zheng, C.: An uncertainty sampling-based active learning approach for support vector machines. In: International Conference on Artificial Intelligence and Computational Intelligence, Shanghai 2009, pp. 208–213 (2009) Xu, H., Wang, X., Liao, Y., Zheng, C.: An uncertainty sampling-based active learning approach for support vector machines. In: International Conference on Artificial Intelligence and Computational Intelligence, Shanghai 2009, pp. 208–213 (2009)
16.
Zurück zum Zitat Cardoso, T.N.C., et al.: Ranked batch-mode active learning. Inf. Sci. 379, 313–337 (2017) CrossRef Cardoso, T.N.C., et al.: Ranked batch-mode active learning. Inf. Sci. 379, 313–337 (2017) CrossRef
17.
Zurück zum Zitat Gal, Y. et al.: Deep Bayesian Active Learning with Image Data. CoRR. abs/1703.02910 (2017) Gal, Y. et al.: Deep Bayesian Active Learning with Image Data. CoRR. abs/1703.02910 (2017)
Metadaten
Titel
Active Learning Technique for Multimodal Brain Tumor Segmentation Using Limited Labeled Images
verfasst von
Dhruv Sharma
Zahil Shanis
Chandan K. Reddy
Samuel Gerber
Andinet Enquobahrie
Copyright-Jahr
2019
DOI
https://doi.org/10.1007/978-3-030-33391-1_17

Premium Partner