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23 April 2024 |
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Article overview
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CaRTS: Causality-driven Robot Tool Segmentation from Vision and Kinematics Data | | Hao Ding
; Jintan Zhang
; Peter Kazanzides
; Jieying Wu
; Mathias Unberath
; | | Date: |
15 Mar 2022 | | Abstract: | Vision-based segmentation of the robotic tool during robot-assisted surgery
enables downstream applications, such as augmented reality feedback, while
allowing for inaccuracies in robot kinematics. With the introduction of deep
learning, many methods were presented to solve instrument segmentation directly
and solely from images. While these approaches made remarkable progress on
benchmark datasets, fundamental challenges pertaining to their robustness
remain. We present CaRTS, a causality-driven robot tool segmentation algorithm,
that is designed based on a complementary causal model of the robot tool
segmentation task. Rather than directly inferring segmentation masks from
observed images, CaRTS iteratively aligns tool models with image observations
by updating the initially incorrect robot kinematic parameters through forward
kinematics and differentiable rendering to optimize image feature similarity
end-to-end. We benchmark CaRTS with competing techniques on both synthetic as
well as real data from the dVRK, generated in precisely controlled scenarios to
allow for counterfactual synthesis. On training-domain test data, CaRTS
achieves a Dice score of 93.4 that is preserved well (Dice score of 91.8) when
tested on counterfactual altered test data, exhibiting low brightness, smoke,
blood, and altered background patterns. This compares favorably to Dice scores
of 95.0 and 62.8, respectively, of a purely image-based method trained and
tested on the same data. Future work will involve accelerating CaRTS to achieve
video framerate and estimating the impact occlusion has in practice. Despite
these limitations, our results are promising: In addition to achieving high
segmentation accuracy, CaRTS provides estimates of the true robot kinematics,
which may benefit applications such as force estimation. | | Source: | arXiv, 2203.09475 | | Services: | Forum | Review | PDF | Favorites | |
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