Scene-graph-driven semantic feature matching for monocular digestive endoscopy
Scene-graph-driven semantic feature matching for monocular digestive endoscopy
复制标题
场景图驱动的单目消化内窥镜语义特征匹配
DOI：
10.1016/j.compbiomed.2022.105616
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发表时间：
2022-05
影响因子：
7.7
通讯作者：
Hong Qin
中科院分区：
文献类型：
--
作者：
Zhuoyue Yang;Junjun Pan;Ranyang Li;Hong Qin
Registration of the preoperative 3D model with the video of the digestive tract is the key task in endoscopy surgical navigation. Accurate 3D reconstruction of soft tissue surfaces is essential to complete registration. However, existing feature matching methods still fall short of desirable performance, due to the soft tissue deformation and smooth but less-textured surface. In this paper, we present a new semantic description based on the scene graph to integrate contour features and SIFT features. Firstly, we construct the semantic feature descriptor using the SIFT features and dense points in the contour regions to obtain more dense point feature matching. Secondly, we design a clustering algorithm based on the proposed semantic feature descriptor. Finally, we apply the semantic description to the structure from motion (SfM) reconstruction framework. Our techniques are validated by the phantom tests and real surgery videos. We compare our approaches with other typical methods in contour extraction, feature matching, and SfM reconstruction. On average, the feature matching accuracy reaches 75.6% and improves 16.6% in pose estimation. In addition, 39.8% of sparse points are increased in SfM results, and 35.31% more valid points are obtained for the DenseDescriptorNet training in 3D reconstruction. The new semantic feature description has the potential to reveal more accurate and dense feature correspondence and provides local semantic information in feature matching. Our experiments on the clinical dataset demonstrate the effectiveness and robustness of the novel approach. • A semantic description combining SIFT and contours for denser, robust matching. • A semantic similarity metric for clustering to speed up the initialization of SfM. • A new SfM with semantic description for more 3D points and accurate pose estimation.
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DOI：
10.1016/j.future.2020.12.016
发表时间：
2021-05
期刊：
Future Gener. Comput. Syst.
影响因子：
--
作者：
Khaldoon Dhou;Christopher Cruzen
通讯作者：
Khaldoon Dhou;Christopher Cruzen
DOI：
10.1023/b:visi.0000029664.99615.94
发表时间：
2004-11-01
影响因子：
19.5
作者：
Lowe, DG
通讯作者：
Lowe, DG
影响因子：
5.2
作者：
Recasens, David;Lamarca, Jose;Civera, Javier
通讯作者：
Civera, Javier
DOI：
10.1109/34.845378
发表时间：
2000-04
期刊：
IEEE Trans. Pattern Anal. Mach. Intell.
影响因子：
--
作者：
J. Han;Jong Seung Park
通讯作者：
J. Han;Jong Seung Park
影响因子：
10.6
作者：
Mahmoud, Nader;Collins, Toby;Martinez Montiel, Jose Maria
通讯作者：
Martinez Montiel, Jose Maria