HydraMarker: Efficient, Flexible, and Multifold Marker Field Generation

Zhu, Mingzhu; He, Bingwei; Yu, Junzhi; Yuan, Fusong; Liu, Jiantao

doi:10.1109/tpami.2022.3212862

Cited by 10 publications

(2 citation statements)

References 33 publications

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“…During the positioning process, the precision of positioning is related to the number of feature points in the position-sensing Marker and the detection accuracy of feature points. In our system, we introduce the HydraMarker toolkit from the previous work of [24] to generate a marker field. The marker field is a binary matrix with a key property.…”

Section: Position-sensing Markermentioning

confidence: 99%

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Distributed visual positioning for surgical instrument tracking

Cai,

Zhu,

et al. 2024

Phys Eng Sci Med

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In clinical operations, it is crucial for surgeons to know the location of the surgical instrument. Traditional positioning systems have difficulty dealing with camera occlusion, marker occlusion, and environmental interference. To address these issues, we propose a distributed visual positioning system for surgical instrument tracking in surgery. First, we design the marker pattern with a black and white triangular grid and dot that can be adapted to various instrument surfaces and improve the marker location accuracy of the feature. The cross-points in the marker are the features that each feature has a unique ID. Furthermore, we proposed detection and identification for the position-sensing marker to realize the accurate location and identification of features. Second, we introduce mPnP (multi Perspective-n-Point) method, which fuses feature coordinates from all cameras to deduce the final result directly by the intrinsic and extrinsic parameters. This method provides a reliable initial value for the Bundle Adjustment algorithms. During instrument tracking, we assess the motion state of the instrument and select either dynamic or static Kalman filtering to mitigate any jitter in the instrument's movement. The core algorithms comparison experiment indicates our positioning algorithm has a lower reprojection error comparison to the mainstream algorithms. A series of quantitative experiments showed that the proposed system positioning error is below 0.207 mm, and the run time is below 118.842 ms. The results demonstrate 1 Manuscript the tremendous clinical application potential of our system providing accurate positioning of instruments promoting the efficiency and safety of clinical surgery.

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Section: Position-sensing Markermentioning

confidence: 99%

“…Position-Sensing marker. A novel marker with denser identifiable features is adopted to code the instrument, and the marker is termed position-sensing marker [24], see Figure 1f. Different from a stack of self-identifying patterns, the codes are intertwined over the feature arrays, thus consuming almost no extra space.…”

Section: Introductionmentioning

confidence: 99%