# Automatic Instrument Segmentation in Robot-Assisted Surgery Using Deep Learning

arXiv: https://arxiv.org/abs/1803.01207 · HTML: https://mlsanity.com/paper/1803.01207/

## Key facts

- Cited by 81 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Mar 2018.

## Built on by (most cited first, 30 of 81)

- [SurgicalSAM: Efficient Class Promptable Surgical Instrument Segmentation](https://mlsanity.com/paper/2308.08746/) (2023)
- [PIDNet: A Real-time Semantic Segmentation Network Inspired by PID Controllers](https://mlsanity.com/paper/2206.02066/) (2022)
- [MA-SAM: Modality-agnostic SAM Adaptation for 3D Medical Image Segmentation](https://mlsanity.com/paper/2309.08842/) (2023)
- [SAR-RARP50: Segmentation of surgical instrumentation and Action Recognition on Robot-Assisted Radical Prostatectomy Challenge](https://mlsanity.com/paper/2401.00496/) (2023)
- [Generalist Vision Foundation Models for Medical Imaging: A Case Study of Segment Anything Model on Zero-Shot Medical Segmentation](https://mlsanity.com/paper/2304.12637/) (2023)
- [Pixel-Wise Recognition for Holistic Surgical Scene Understanding](https://mlsanity.com/paper/2401.11174/) (2024)
- [Surgical SAM 2: Real-time Segment Anything in Surgical Video by Efficient Frame Pruning](https://mlsanity.com/paper/2408.07931/) (2024)
- [TraSeTR: Track-to-Segment Transformer with Contrastive Query for Instance-level Instrument Segmentation in Robotic Surgery](https://mlsanity.com/paper/2202.08453/) (2022)
- [AdaptiveSAM: Towards Efficient Tuning of SAM for Surgical Scene Segmentation](https://mlsanity.com/paper/2308.03726/) (2023)
- [MATIS: Masked-Attention Transformers for Surgical Instrument Segmentation](https://mlsanity.com/paper/2303.09514/) (2023)
- [Frequency-Adaptive Dilated Convolution for Semantic Segmentation](https://mlsanity.com/paper/2403.05369/) (2024)
- [Semi-supervised Domain Adaptation based on Dual-level Domain Mixing for Semantic Segmentation](https://mlsanity.com/paper/2103.04705/) (2021)
- [From Forks to Forceps: A New Framework for Instance Segmentation of Surgical Instruments](https://mlsanity.com/paper/2211.16200/) (2022)
- [SAM Meets Robotic Surgery: An Empirical Study on Generalization, Robustness and Adaptation](https://mlsanity.com/paper/2308.07156/) (2023)
- [OphCLIP: Hierarchical Retrieval-Augmented Learning for Ophthalmic Surgical Video-Language Pretraining](https://mlsanity.com/paper/2411.15421/) (2024)
- [Text Promptable Surgical Instrument Segmentation with Vision-Language Models](https://mlsanity.com/paper/2306.09244/) (2023)
- [Exploring Intra- and Inter-Video Relation for Surgical Semantic Scene Segmentation](https://mlsanity.com/paper/2203.15251/) (2022)
- [SAM Meets Robotic Surgery: An Empirical Study in Robustness Perspective](https://mlsanity.com/paper/2304.14674/) (2023)
- [Image Compositing for Segmentation of Surgical Tools without Manual Annotations](https://mlsanity.com/paper/2102.09528/) (2021)
- [SAM 2 in Robotic Surgery: An Empirical Evaluation for Robustness and Generalization in Surgical Video Segmentation](https://mlsanity.com/paper/2408.04593/) (2024)
- [ST-MTL: Spatio-Temporal Multitask Learning Model to Predict Scanpath While Tracking Instruments in Robotic Surgery](https://mlsanity.com/paper/2112.08189/) (2021)
- [FUN-SIS: a Fully UNsupervised approach for Surgical Instrument Segmentation](https://mlsanity.com/paper/2202.08141/) (2022)
- [Semantic-SuPer: A Semantic-aware Surgical Perception Framework for Endoscopic Tissue Identification, Reconstruction, and Tracking](https://mlsanity.com/paper/2210.16674/) (2022)
- [Methods and datasets for segmentation of minimally invasive surgical instruments in endoscopic images and videos: A review of the state of the art](https://mlsanity.com/paper/2304.13014/) (2023)
- [Federated Split Vision Transformer for COVID-19 CXR Diagnosis using Task-Agnostic Training](https://mlsanity.com/paper/2111.01338/) (2021)
- [Real-time Instance Segmentation of Surgical Instruments using Attention and Multi-scale Feature Fusion](https://mlsanity.com/paper/2111.04911/) (2021)
- [CaRTS: Causality-driven Robot Tool Segmentation from Vision and Kinematics Data](https://mlsanity.com/paper/2203.09475/) (2022)
- [One to Many: Adaptive Instrument Segmentation via Meta Learning and Dynamic Online Adaptation in Robotic Surgical Video](https://mlsanity.com/paper/2103.12988/) (2021)
- [Tilted Cross Entropy (TCE): Promoting Fairness in Semantic Segmentation](https://mlsanity.com/paper/2103.14051/) (2021)
- [Efficient Global-Local Memory for Real-time Instrument Segmentation of Robotic Surgical Video](https://mlsanity.com/paper/2109.13593/) (2021)

## Abstract (arXiv)

Semantic segmentation of robotic instruments is an important problem for the robot-assisted surgery. One of the main challenges is to correctly detect an instrument's position for the tracking and pose estimation in the vicinity of surgical scenes. Accurate pixel-wise instrument segmentation is needed to address this challenge. In this paper we describe our winning solution for MICCAI 2017 Endoscopic Vision SubChallenge: Robotic Instrument Segmentation. Our approach demonstrates an improvement over the state-of-the-art results using several novel deep neural network architectures. It addressed the binary segmentation problem, where every pixel in an image is labeled as an instrument or background from the surgery video feed. In addition, we solve a multi-class segmentation problem, where we distinguish different instruments or different parts of an instrument from the background. In this setting, our approach outperforms other methods in every task subcategory for automatic instrument segmentation thereby providing state-of-the-art solution for this problem. The source code for our solution is made publicly available at https://github.com/ternaus/robot-surgery-segmentation