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One-shot Learning Landmarks Detection

Abstract : Landmarks detection in a medical image is a mainstay for many clinical algorithms application. Learning-based landmarks detection is now a major successful methodology for many types of objects detection. However, learningbased approaches usually need a number of the annotated dataset for training the learning models. To tackle the lack of annotation issue, in this work, an automatic one-shot learning-based landmarks detection approach is proposed for identifying the landmarks in 3D volume images. A convolutional neural network-based iterative objects localization method in combine with a registration framework is applied for automatically target organ localization and landmarks matching. We investigated both qualitatively and quantitatively the performance of the proposed approach on clinical temporal bone CT volumes. The result shows that the proposed method is robust in convergence, effective in accuracy and reliable for clinical usage.
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Preprints, Working Papers, ...
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https://hal.inria.fr/hal-03024759
Contributor : Zihao Wang <>
Submitted on : Thursday, November 26, 2020 - 7:38:42 AM
Last modification on : Thursday, January 28, 2021 - 11:19:32 AM
Long-term archiving on: : Saturday, February 27, 2021 - 6:14:02 PM

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  • HAL Id : hal-03024759, version 1

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Zihao Wang, Clair Vandersteen, Charles Raffaelli, Nicolas Guevara, Hervé Delingette. One-shot Learning Landmarks Detection. 2020. ⟨hal-03024759⟩

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