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Concurrent Segmentation and Localization for Tracking of Surgical Instruments

arXiv 1703.10701 ↗First posted 30 Mar 2017cs.CVcited by 24

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Abstract (arXiv)

Real-time instrument tracking is a crucial requirement for various computer-assisted interventions. In order to overcome problems such as specular reflections and motion blur, we propose a novel method that takes advantage of the interdependency between localization and segmentation of the surgical tool. In particular, we reformulate the 2D instrument pose estimation as heatmap regression and thereby enable a concurrent, robust and near real-time regression of both tasks via deep learning. As demonstrated by our experimental results, this modeling leads to a significantly improved performance than directly regressing the tool position and allows our method to outperform the state of the art on a Retinal Microsurgery benchmark and the MICCAI EndoVis Challenge 2015.

Key facts. Cited by 24 papers in the map (data through 14 Oct 2026). First posted on arXiv 30 Mar 2017.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 5; 2022: 4; 2023: 4; 2024: 5; 2025: 3; 2026: 3.

24 papers.

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  1. 20224D-OR: Semantic Scene Graphs for OR Domain Modelingcited by 49
  2. 2023MATIS: Masked-Attention Transformers for Surgical Instrument Segmentationcited by 36
  3. 2024OphCLIP: Hierarchical Retrieval-Augmented Learning for Ophthalmic Surgical Video-Language Pretrainingcited by 29
  4. 2021ST-MTL: Spatio-Temporal Multitask Learning Model to Predict Scanpath While Tracking Instruments in Robotic Surgerycited by 20
  5. 2022FUN-SIS: a Fully UNsupervised approach for Surgical Instrument Segmentationcited by 20
  6. 2023Methods and datasets for segmentation of minimally invasive surgical instruments in endoscopic images and videos: A review of the state of the artcited by 18
  7. 2021A Kinematic Bottleneck Approach For Pose Regression of Flexible Surgical Instruments directly from Imagescited by 11
  8. 2021Simulation-to-Real domain adaptation with teacher-student learning for endoscopic instrument segmentationcited by 9
  9. 2025Surgical Scene Understanding in the Era of Foundation AI Models: A Comprehensive Reviewcited by 8
  10. 2023SegMatch: A semi-supervised learning method for surgical instrument segmentationcited by 5
  11. 2024Multitask Learning in Minimally Invasive Surgical Vision: A Reviewcited by 5
  12. 2021Real-time landmark detection for precise endoscopic submucosal dissection via shape-aware relation networkcited by 3
12 more
  1. 2022A comprehensive survey on recent deep learning-based methods applied to surgical datacited by 3
  2. 2024Revisiting Surgical Instrument Segmentation Without Human Intervention: A Graph Partitioning Viewcited by 3
  3. 2024AMNCutter: Affinity-Attention-Guided Multi-View Normalized Cutter for Unsupervised Surgical Instrument Segmentationcited by 3
  4. 2021Temporally Guided Articulated Hand Pose Tracking in Surgical Videoscited by 2
  5. 2023SAF-IS: a Spatial Annotation Free Framework for Instance Segmentation of Surgical Toolscited by 2
  6. 2024Strategies to Improve Real-World Applicability of Laparoscopic Anatomy Segmentation Modelscited by 2
  7. 2025Identifying Surgical Instruments in Laparoscopy Using Deep Learning Instance Segmentationcited by 1
  8. 2022Segmenting Medical Instruments in Minimally Invasive Surgeries using AttentionMaskcited by 0
  9. 2025Real-Time Surgical Instrument Defect Detection via Non-Destructive Testingcited by 0
  10. 2026Towards Comprehensive Real-Time Scene Understanding in Ophthalmic Surgery through Multimodal Image Fusioncited by 0
  11. 2026SurfSurg6D: Geometry Consistent Dense Correspondence for Textureless Surgical Instrument Pose Estimationcited by 0
  12. 2026Temporally Consistent Label Interpolation for Robust Surgical Multi-Task Learning under Challenging Conditionscited by 0