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One-Shot Medical Landmark Detection

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Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 (MICCAI 2021)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12902))

Abstract

The success of deep learning methods relies on the availability of a large number of datasets with annotations; however, curating such datasets is burdensome, especially for medical images. To relieve such a burden for a landmark detection task, we explore the feasibility of using only a single annotated image and propose a novel framework named Cascade Comparing to Detect (CC2D) for one-shot landmark detection. CC2D consists of two stages: 1) Self-supervised learning (CC2D-SSL) and 2) Training with pseudo-labels (CC2D-TPL). CC2D-SSL captures the consistent anatomical information in a coarse-to-fine fashion by comparing the cascade feature representations and generates predictions on the training set. CC2D-TPL further improves the performance by training a new landmark detector with those predictions. The effectiveness of CC2D is evaluated on a widely-used public dataset of cephalometric landmark detection, which achieves a competitive detection accuracy of 86.25.01% within 4.0 mm, comparable to the state-of-the-art semi-supervised methods using a lot more than one training image. Our code is available at https://github.com/ICT-MIRACLE-lab/Oneshot_landmark_detection.

Q. Yao and Q. Quan—Contribute equally in this paper.

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Notes

  1. 1.

    We use the 126# labeled image in training set as the template image. Furthermore, we randomly select 10 template images from the training set, CC2D-SSL reaches 3.25 ± 0.62 mm MRE.

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Yao, Q., Quan, Q., Xiao, L., Kevin Zhou, S. (2021). One-Shot Medical Landmark Detection. In: de Bruijne, M., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. MICCAI 2021. Lecture Notes in Computer Science(), vol 12902. Springer, Cham. https://doi.org/10.1007/978-3-030-87196-3_17

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