PointNet-Based Jitter Decomposition on Point Cloud of Jitter Histogram

Ren, Nan; Fu, Zaiming; Zhou, Dawei; Kong, Dexuan; Tian, Shulin

doi:10.1109/iscas51556.2021.9401248

Cited by 2 publications

(1 citation statement)

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“…However, all voxels need to be calculated with this method which occupies large memory and consumes more time. Another popular method to address 3D image processing issue is PointNet 34–37 which addresses point clouds composed of unordered points. This network uses a multi‐layer perceptron to independently upgrade the dimension of each point feature and uses a symmetric function to aggregate point cloud features, aiming to solve the permutation invariances.…”

Section: Introductionmentioning

confidence: 99%

An automatic extraction method on medical feature points based on PointNet++ for robot‐assisted knee arthroplasty

Wang

Yan

Xia

et al. 2022

Robotics Computer Surgery

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Background Image registration is a crucial technology in robot‐assisted knee arthroplasty, which provides real‐time patient information by registering the pre‐operative image data with data acquired during the operation. The existing registration method requires surgeons to manually pick up medical feature points (i.e. anatomical points) in pre‐operative images, which is time‐consuming and relied on surgeons experience. Moreover, different doctors have different preferences in preoperative planning, which may influence the consistency of surgical results. Methods A medical feature points automatic extraction method based on PointNet++ named Point_RegNet is proposed to improve the efficiency of preoperative preparation and ensure the consistency of surgical results. The proposed method replaces the classification and segmentation layer of PointNet++ with a regression layer to predict the position of feature points. The comparative experiment is adopted to determine the optimal set of abstraction layers in PointNet++. Results The proposed network with three set abstraction layers is more suitable for extracting feature points. The feature points predictions mean error of our method is less than 5 mm, which is 1 mm less than the manual marking method. Ultimately, our method only requires less than 3 s to extract all medical feature points in practical application. It is much faster than the manual extraction way which usually requires more than half an hour to mark all necessary feature points. Conclusion Our deep learning‐based method can improve the surgery accuracy and reduce the preoperative preparation time. Moreover, this method can also be applied to other surgical navigation systems.

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Section: Introductionmentioning

confidence: 99%