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LOCAL SURF-BASED KEYPOINT TRANSFER SEGMENTATION

Antoine Bralet 1 Razmig Kéchichian 2, 1 Sebastien Valette 3, 1
2 MYRIAD - Modeling & analysis for medical imaging and Diagnosis
CREATIS - Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé
3 Imagerie Tomographique et Radiothérapie
CREATIS - Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé
Abstract : This paper presents an improvement of the keypoint transfer method for the segmentation of 3D medical images. Our approach is based on 3D SURF keypoint extraction, instead of 3D SIFT in the original algorithm. This yields a significantly higher number of keypoints, which allows to use a local segmentation transfer approach. The resulting segmentation accuracy is significantly increased, and smaller organs can be segmented correctly. We also propose a keypoint selection step which provides a good balance between speed and accuracy. We illustrate the efficiency of our approach with comparisons against state of the art methods.
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https://hal.archives-ouvertes.fr/hal-03244747
Contributor : Sébastien Valette <>
Submitted on : Tuesday, June 1, 2021 - 1:47:14 PM
Last modification on : Tuesday, June 15, 2021 - 4:03:34 PM

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bralet21ISBI.pdf
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Antoine Bralet, Razmig Kéchichian, Sebastien Valette. LOCAL SURF-BASED KEYPOINT TRANSFER SEGMENTATION. 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), Apr 2021, Nice, France. pp.1390-1393, ⟨10.1109/ISBI48211.2021.9434106⟩. ⟨hal-03244747⟩

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