Relative distance matters for one-shot landmark detection
- URL: http://arxiv.org/abs/2203.01687v2
- Date: Fri, 4 Mar 2022 05:54:47 GMT
- Title: Relative distance matters for one-shot landmark detection
- Authors: Qingsong Yao and Jianji Wang and Yihua Sun and Quan Quan and Heqin Zhu
and S. Kevin Zhou
- Abstract summary: We upgrade cascade comparing to detect (CC2D) to version II by incorporating a simple-yet-effective relative distance bias in the training stage.
CC2Dv2 is less possible to detect a wrong point far from the correct landmark.
We present an open-source, landmark-labeled dataset for the measurement of biomechanical parameters of the lower extremity.
- Score: 16.032695993409853
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Contrastive learning based methods such as cascade comparing to detect (CC2D)
have shown great potential for one-shot medical landmark detection. However,
the important cue of relative distance between landmarks is ignored in CC2D. In
this paper, we upgrade CC2D to version II by incorporating a
simple-yet-effective relative distance bias in the training stage, which is
theoretically proved to encourage the encoder to project the relatively distant
landmarks to the embeddings with low similarities. As consequence, CC2Dv2 is
less possible to detect a wrong point far from the correct landmark.
Furthermore, we present an open-source, landmark-labeled dataset for the
measurement of biomechanical parameters of the lower extremity to alleviate the
burden of orthopedic surgeons. The effectiveness of CC2Dv2 is evaluated on the
public dataset from the ISBI 2015 Grand-Challenge of cephalometric radiographs
and our new dataset, which greatly outperforms the state-of-the-art one-shot
landmark detection approaches.
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