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Amodal Segmentation for Laparoscopic Surgery Video Instruments

arXiv 2408.01067 ↗First posted 2 Aug 2024cs.CVcited by 0

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Abstract (arXiv)

Segmentation of surgical instruments is crucial for enhancing surgeon performance and ensuring patient safety. Conventional techniques such as binary, semantic, and instance segmentation share a common drawback: they do not accommodate the parts of instruments obscured by tissues or other instruments. Precisely predicting the full extent of these occluded instruments can significantly improve laparoscopic surgeries by providing critical guidance during operations and assisting in the analysis of potential surgical errors, as well as serving educational purposes. In this paper, we introduce Amodal Segmentation to the realm of surgical instruments in the medical field. This technique identifies both the visible and occluded parts of an object. To achieve this, we introduce a new Amoal Instruments Segmentation (AIS) dataset, which was developed by reannotating each instrument with its complete mask, utilizing the 2017 MICCAI EndoVis Robotic Instrument Segmentation Challenge dataset. Additionally, we evaluate several leading amodal segmentation methods to establish a benchmark for this new dataset.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 32 of its 44 references that are in the map. First posted on arXiv 2 Aug 2024.

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32 of 44 references.

References · 32

  1. 2023Segment Anythingcited by 8,158
  2. 2024SAM 2: Segment Anything in Images and Videoscited by 2,784
  3. 20192017 Robotic Instrument Segmentation Challengecited by 166
  4. 2021Deep Occlusion-Aware Instance Segmentation with Overlapping BiLayerscited by 104
  5. 2023SurgicalSAM: Efficient Class Promptable Surgical Instrument Segmentationcited by 103
  6. 2015Semantic Amodal Segmentationcited by 102
  7. 2020Self-Supervised Scene De-occlusioncited by 96
  8. 2018Automatic Instrument Segmentation in Robot-Assisted Surgery Using Deep Learningcited by 81
  9. 2020ISINet: An Instance-Based Approach for Surgical Instrument Segmentationcited by 67
  10. 2019Incorporating Temporal Prior from Motion Flow for Instrument Segmentation in Minimally Invasive Surgery Videocited by 64
  11. 2016Amodal Instance Segmentationcited by 63
  12. 2018Learning to See the Invisible: End-to-End Trainable Amodal Instance Segmentationcited by 62
20 more
  1. 2017SeGAN: Segmenting and Generating the Invisiblecited by 60
  2. 2020Robust Object Detection under Occlusion with Context-Aware CompositionalNetscited by 54
  3. 2020Compositional Convolutional Neural Networks: A Deep Architecture with Innate Robustness to Partial Occlusioncited by 47
  4. 2020Amodal Segmentation Based on Visible Region Segmentation and Shape Priorcited by 42
  5. 2020Compositional Convolutional Neural Networks: A Robust and Interpretable Model for Object Recognition under Occlusioncited by 38
  6. 2022AISFormer: Amodal Instance Segmentation with Transformercited by 35
  7. 2020Amodal Segmentation through Out-of-Task and Out-of-Distribution Generalization with a Bayesian Modelcited by 29
  8. 2023Coarse-to-Fine Amodal Segmentation with Shape Priorcited by 29
  9. 2021Image Compositing for Segmentation of Surgical Tools without Manual Annotationscited by 23
  10. 2021A Weakly Supervised Amodal Segmenter with Boundary Uncertainty Estimationcited by 21
  11. 2018MVTec D2S: Densely Segmented Supermarket Datasetcited by 20
  12. 2022FUN-SIS: a Fully UNsupervised approach for Surgical Instrument Segmentationcited by 20
  13. 2019Learning Semantics-aware Distance Map with Semantics Layering Network for Amodal Instance Segmentationcited by 16
  14. 2019Attention-Guided Lightweight Network for Real-Time Segmentation of Robotic Surgical Instrumentscited by 15
  15. 2021PanoDR: Spherical Panorama Diminished Reality for Indoor Scenescited by 13
  16. 2020Joint Learning of Instance and Semantic Segmentation for Robotic Pick-and-Place with Heavy Occlusions in Cluttercited by 12
  17. 2022Amodal Cityscapes: A New Dataset, its Generation, and an Amodal Semantic Segmentation Challenge Baselinecited by 12
  18. 2024BLADE: Box-Level Supervised Amodal Segmentation through Directed Expansioncited by 9
  19. 2020Instance Segmentation of Visible and Occluded Regions for Finding and Picking Target from a Pile of Objectscited by 8
  20. 2024PLUG: Revisiting Amodal Segmentation with Foundation Model and Hierarchical Focuscited by 4

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