# Deep Learning for Surgical Instrument Recognition and Segmentation in Robotic-Assisted Surgeries: A Systematic Review arXiv: https://arxiv.org/abs/2410.07269 ยท HTML: https://mlsanity.com/paper/2410.07269/ ## Key facts - Cited by 13 papers in the map (data through 14 Oct 2026). - Builds on 13 of its 81 references that are in the map. - First posted on arXiv 9 Oct 2024. ## Builds on (most cited first, 13 of 13) - [Incorporating Temporal Prior from Motion Flow for Instrument Segmentation in Minimally Invasive Surgery Video](https://mlsanity.com/paper/1907.07899/) (2019) - [Learning How To Robustly Estimate Camera Pose in Endoscopic Videos](https://mlsanity.com/paper/2304.08023/) (2023) - [Exploiting the potential of unlabeled endoscopic video data with self-supervised learning](https://mlsanity.com/paper/1711.09726/) (2017) - [Exploring Intra- and Inter-Video Relation for Surgical Semantic Scene Segmentation](https://mlsanity.com/paper/2203.15251/) (2022) - [U-NetPlus: A Modified Encoder-Decoder U-Net Architecture for Semantic and Instance Segmentation of Surgical Instrument](https://mlsanity.com/paper/1902.08994/) (2019) - [Real-Time Instrument Segmentation in Robotic Surgery using Auxiliary Supervised Deep Adversarial Learning](https://mlsanity.com/paper/2007.11319/) (2020) - [Image Compositing for Segmentation of Surgical Tools without Manual Annotations](https://mlsanity.com/paper/2102.09528/) (2021) - [FUN-SIS: a Fully UNsupervised approach for Surgical Instrument Segmentation](https://mlsanity.com/paper/2202.08141/) (2022) - [Attention-Guided Lightweight Network for Real-Time Segmentation of Robotic Surgical Instruments](https://mlsanity.com/paper/1910.11109/) (2019) - [Dynamic Interactive Relation Capturing via Scene Graph Learning for Robotic Surgical Report Generation](https://mlsanity.com/paper/2306.02651/) (2023) - [Co-Generation and Segmentation for Generalized Surgical Instrument Segmentation on Unlabelled Data](https://mlsanity.com/paper/2103.09276/) (2021) - [Rethinking Surgical Captioning: End-to-End Window-Based MLP Transformer Using Patches](https://mlsanity.com/paper/2207.00113/) (2022) - [A Nested U-Structure for Instrument Segmentation in Robotic Surgery](https://mlsanity.com/paper/2307.08630/) (2023) ## Built on by (most cited first, 13 of 13) - [Medical Multimodal Foundation Models in Clinical Diagnosis and Treatment: Applications, Challenges, and Future Directions](https://mlsanity.com/paper/2412.02621/) (2024) - [Comparative validation of surgical phase recognition, instrument keypoint estimation, and instrument instance segmentation in endoscopy: Results of the PhaKIR 2024 challenge](https://mlsanity.com/paper/2507.16559/) (2025) - [Towards Next-Generation Healthcare: A Survey of Medical Embodied AI for Perception, Decision-Making, and Action](https://mlsanity.com/paper/2606.15647/) (2026) - [SCOPE: Speech-guided COllaborative PErception Framework for Surgical Scene Segmentation](https://mlsanity.com/paper/2509.10748/) (2025) - [Surgical Video Understanding with Label Interpolation](https://mlsanity.com/paper/2509.18802/) (2025) - [Grounding Surgical Action Triplets with Instrument Instance Segmentation: A Dataset and Target-Aware Fusion Approach](https://mlsanity.com/paper/2511.00643/) (2025) - [SurgSLOT: Segment Anything in Surgical Videos via Semantic Long-term Tracking](https://mlsanity.com/paper/2511.16618/) (2025) - [Surg-SegFormer: A Dual Transformer-Based Model for Holistic Surgical Scene Segmentation](https://mlsanity.com/paper/2507.04304/) (2025) - [CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning](https://mlsanity.com/paper/2507.04317/) (2025) - [FASL-Seg: Anatomy and Tool Segmentation of Surgical Scenes](https://mlsanity.com/paper/2509.06159/) (2025) - [Adaptive transfer learning for surgical tool presence detection in laparoscopic videos through gradual freezing fine-tuning](https://mlsanity.com/paper/2510.15372/) (2025) - [Leveraging Transfer Learning with Class-Specific Decoders for Laparoscopic Segmentation](https://mlsanity.com/paper/2607.29509/) (2026) - [Toward Mask Annotation-Free Surgical Instrument Segmentation from Endoscopic Images Using Text-Prompted Segment Anything Model 3 (SAM3)](https://mlsanity.com/paper/2608.08844/) (2026) ## Abstract (arXiv) Applying deep learning (DL) for annotating surgical instruments in robot-assisted minimally invasive surgeries (MIS) represents a significant advancement in surgical technology. This systematic review examines 48 studies that and advanced DL methods and architectures. These sophisticated DL models have shown notable improvements in the precision and efficiency of detecting and segmenting surgical tools. The enhanced capabilities of these models support various clinical applications, including real-time intraoperative guidance, comprehensive postoperative evaluations, and objective assessments of surgical skills. By accurately identifying and segmenting surgical instruments in video data, DL models provide detailed feedback to surgeons, thereby improving surgical outcomes and reducing complication risks. Furthermore, the application of DL in surgical education is transformative. The review underscores the significant impact of DL on improving the accuracy of skill assessments and the overall quality of surgical training programs. However, implementing DL in surgical tool detection and segmentation faces challenges, such as the need for large, accurately annotated datasets to train these models effectively. The manual annotation process is labor-intensive and time-consuming, posing a significant bottleneck. Future research should focus on automating the detection and segmentation process and enhancing the robustness of DL models against environmental variations. Expanding the application of DL models across various surgical specialties will be essential to fully realize this technology's potential. Integrating DL with other emerging technologies, such as augmented reality (AR), also offers promising opportunities to further enhance the precision and efficacy of surgical procedures.