Abstract
Segmentation of the carotid intima-media (CIM) offers more precise morphological evidence for obesity and atherosclerotic disease compared to the method that measures its thickness and roughness during routine ultrasound scans. Although advanced deep learning technology has shown promise in enabling automatic and accurate medical image segmentation, the lack of a large quantity of high-quality CIM labels may hinder the model training process. Active learning (AL) tackles this issue by iteratively annotating the subset whose labels contribute the most to the training performance at each iteration. However, this approach substantially relies on the expert’s experience, particularly when addressing ambiguous CIM boundaries that may be present in real-world ultrasound images. Our proposed approach, called pseudo-label divergence-based active learning (PLD-AL), aims to train segmentation models using a gradually enlarged and refined labeled pool. The approach has an outer and an inner loops: The outer loop calculates the Kullback-Leibler (KL) divergence of predictive pseudo-labels related to two consecutive AL iterations. It determines which portion of the unlabeled pool should be annotated by an expert. The inner loop trains two networks: The student network is fully trained on the current labeled pool, while the teacher network is weighted upon itself and the student one, ultimately refining the labeled pool. We evaluated our approach using both the Carotid Ultrasound Boundary Study dataset and an in-house dataset from Children’s Hospital, Zhejiang University School of Medicine. Our results demonstrate that our approach outperforms state-of-the-art AL approaches. Furthermore, the visualization results show that our approach less over-estimates the CIM area than the rest methods, especially for severely ambiguous ultrasound images at the thickness direction.
Y. Tang—This work was performed when Yucheng Tang was visiting Zhejiang Lab as an intern.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Tarvainen, A., Valpola, H.: Mean teachers are better role models: weight-averaged consistency targets improve semi-supervised deep learning results. In: NeurIPS, NIPS, Long Beach (2017)
Xu, M.C., et al.: Bayesian pseudo labels: expectation maximization for robust and efficient semi-supervised segmentation. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) MICCAI 2022. LNCS, vol. 13435, pp. 580–590. Springer, Singapore (2022). https://doi.org/10.1007/978-3-031-16443-9_56
Yao, H., Hu, X., Li, X.: Enhancing pseudo label quality for semi-supervised domain-generalized medical image segmentation. In: AAAI, pp. 3099–3107. AAAI (2022)
Liu, F., Tian, Y., Chen, Y., Liu, Y., Belagiannis, V., Carneiro, G.: ACPL: anti-curriculum pseudo-labelling for semi-supervised medical image classification. In: CVPR, New Orleans, pp. 20697–20706. IEEE Computer Society (2022)
Lu, L., Yin, M., Fu, L., Yang, F.: Uncertainty-aware pseudo-label and consistency for semi-supervised medical image segmentation. Biomed. Signal Process. Control 79(2), 104203 (2023)
Parvaneh, A., Abbasnejad, E., Teney, D., Haffari, G.R., Van Den Hengel, A., Shi, J.Q.: Active learning by feature mixing. In: CVPR, New Orleans, pp. 12237–12246. IEEE Computer Society (2022)
Sinha, S., Ebrahimi, S., Darrell, T.: Variational adversarial active learning. In: ICCV, Seoul, pp. 5972–5981. IEEE (2019)
Caramalau, R., Bhattarai, B., Kim, T.K.: Sequential graph convolutional network for active learning. In: CVPR, pp. 9583–9592. IEEE Computer Society (2021)
Casanova, A., Pinheiro, P.O., Rostamzadeh, N., Pal, C.J.: Reinforced active learning for image segmentation. arXiv preprint arXiv:2002.06583 (2020)
Siddiqui, Y., Valentin, J., Nießner, M.: Viewal: active learning with viewpoint entropy for semantic segmentation. In: CVPR, pp. 9433–9443. IEEE Computer Society (2020)
Yang, L., Zhang, Y., Chen, J., Zhang, S., Chen, D.Z.: Suggestive annotation: a deep active learning framework for biomedical image segmentation. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10435, pp. 399–407. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66179-7_46
Xu, Y., et al.: Partially-supervised learning for vessel segmentation in ocular images. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12901, pp. 271–281. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87193-2_26
Zheng, Z., Yang, Y.: Rectifying pseudo label learning via uncertainty estimation for domain adaptive semantic segmentation. Int. J. Comput. Vision 129(4), 1106–1120 (2021)
Luo, X., et al.: Efficient semi-supervised gross target volume of nasopharyngeal carcinoma segmentation via uncertainty rectified pyramid consistency. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12902, pp. 318–329. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87196-3_30
Liu, S., Liu, K., Zhu, W., Shen, Y., Fernandez-Granda, C.: Adaptive early-learning correction for segmentation from noisy annotations. In: CVPR, New Orleans, pp. 2606–2616. IEEE Computer Society (2022)
Rahman, M.A., Wang, Y.: Optimizing intersection-over-union in deep neural networks for image segmentation. In: Bebis, G., et al. (eds.) ISVC 2016. LNCS, vol. 10072, pp. 234–244. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-50835-1_22
Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: UNet++: redesigning skip connections to exploit multiscale features in image segmentation. IEEE Trans. Med. Imaging 39(6), 1856–1867 (2019)
Brunelli, R.: Template Matching Techniques in Computer Vision: Theory and Practice. Wiley, Hoboken (2009)
Akima, H.: A method of bivariate interpolation and smooth surface fitting based on local procedures. Commun. ACM 17(1), 18–20 (1974)
He, K., Girshick, R., Dollár, P.: Rethinking imagenet pre-training. In: ICCV, Seoul, pp. 4918–4927. IEEE (2019)
Meiburger, K.M., et al.: DATASET for “Carotid Ultrasound Boundary Study (CUBS): an open multi-center analysis of computerized intima-media thickness measurement systems and their clinical impact”. Mendeley Data, V1 (2021). https://doi.org/10.17632/fpv535fss7.1
Sipahi, S., Timor, M.: The analytic hierarchy process and analytic network process: an overview of applications. Manag. Decis. 48(5), 775–808 (2010)
Bradski, G.: The openCV library. Dr. Dobb’s J. Softw. Tools Prof. Program. 25(11), 120–123 (2000)
Bertels, J., et al.: Optimizing the dice score and jaccard index for medical image segmentation: theory and practice. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11765, pp. 92–100. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32245-8_11
Aspert, N., Santa-Cruz, D., Ebrahimi, T.: Mesh: measuring errors between surfaces using the hausdorff distance. In: ICME, Lausanne, pp. 705–708. IEEE (2022)
Wang, D., Shang, Y.: A new active labeling method for deep learning. In: IJCNN, Beijing, pp. 112–119. IEEE (2014)
Sener, O., Savarese, S.: Active learning for convolutional neural networks: a core-set approach. arXiv preprint arXiv:1708.00489 (2017)
Agarwal, S., Arora, H., Anand, S., Arora, C.: Contextual diversity for active learning. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12361, pp. 137–153. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58517-4_9
Xu, Z., et al.: Noisy labels are treasure: mean-teacher-assisted confident learning for hepatic vessel segmentation. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12901, pp. 3–13. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87193-2_1
Zhang, L., et al.: Learning from multiple annotators for medical image segmentation. Pattern Recognit. 138, 109400 (2023)
Acknowledgement
This work was supported in part by Research Initiation Project (2021ND0PI02) and Key Research Project (2022KI0AC01) of Zhejiang Lab, National Key Research and Development Programme of China (No. 2021YFC2701902), and National Natural Science Foundation of China (No. 12071430).
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Tang, Y. et al. (2023). PLD-AL: Pseudo-label Divergence-Based Active Learning in Carotid Intima-Media Segmentation for Ultrasound Images. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14221. Springer, Cham. https://doi.org/10.1007/978-3-031-43895-0_6
Download citation
DOI: https://doi.org/10.1007/978-3-031-43895-0_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-43894-3
Online ISBN: 978-3-031-43895-0
eBook Packages: Computer ScienceComputer Science (R0)
