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Awesome Image-guided Surgery
https://github.com/mjahmadee/awesome_video-guided_surgery
List: awesome_video-guided_surgery
Last synced: about 1 month ago
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Awesome Image-guided Surgery
Host: GitHub
URL: https://github.com/mjahmadee/awesome_video-guided_surgery
Owner: MJAHMADEE
License: mit
Created: 2023-08-22T05:44:39.000Z (over 1 year ago)
Default Branch: main
Last Pushed: 2024-03-20T11:34:12.000Z (9 months ago)
Last Synced: 2024-03-20T12:38:00.495Z (9 months ago)
Size: 43 KB
Stars: 1
Watchers: 1
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE
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ultimate-awesome - awesome_video-guided_surgery - Awesome Image-guided Surgery. (Other Lists / PowerShell Lists)
README

        # Awesome-Surgical-Video-Analysis 🎥🔬

[![Awesome](https://img.shields.io/badge/awesome-yes-brightgreen.svg)](https://github.com/sindresorhus/awesome)

[![Under Development](https://img.shields.io/badge/status-under%20development-orange.svg)](https://github.com/your-github/awesome-surgical-video-analysis)

_🚀 **Under development. Welcome to contribute!**_ 🌟

📝 **TO DO:**

- 🖇 Add links

- 🔍 Search for more papers

- 📂 Better categories, Better curation

## 📚 Contents

- [The Big Picture](#the-big-picture)

- [Survey](#survey)

- [Surgical Skill Assessment](#surgical-skill-assessment)

- [Surgical Skill Transfer](#surgical-skill-transfer)

- [Surgical Phase Recognition](#surgical-phase-recognition)

- [Surgical Gesture Recognition](#surgical-gesture-recognition)

- [Surgical Instrument Recognition / Localization](#surgical-instrument-recognition--localization)

- [Surgical Scene / Anatomy Recognition](#surgical-scene--anatomy-recognition)

- [Human / Activity in Operating Rooms](#human--activity-in-operating-rooms)

- [Others](#others)

---

## 🌟 The Big Picture

| Title | Venue | Links |

| :--------------------: | :-------------: | :-----: |

| Surgical Data Science: from Concepts to Clinical Translation | **Arxiv 2020** | [Paper](https://arxiv.org/abs/2011.02284) |

| Surgical Data Science: A Consensus Perspective | **Arxiv 2018** | [Paper](https://arxiv.org/abs/1806.03184) |

| Surgical data science for next-generation interventions | **NBE 2017** | [Paper](https://www.nature.com/articles/s41551-017-0132-7) |

| CAI4CAI: The Rise of Contextual Artificial Intelligence in Computer-Assisted Interventions | **IEEE 2019** | [Paper](https://ieeexplore.ieee.org/document/8880624) | 

| OR Black Box and Surgical Control Tower: Recording and Streaming data and Analytics to Improve Surgical Care | **JVS 2021** | [Paper](https://www.sciencedirect.com/science/article/pii/S1878788621000163?via%3Dihub) |

---

## 📖 Survey

| Title | Venue | Links |

| :--------------------: | :-------------: | :-----: |

| Machine learning for technical skill assessment in surgery: a systematic review | **NPJ 2022** | [Paper](https://www.nature.com/articles/s41746-022-00566-0) |

| A Review on Deep Learning in Minimally Invasive Surgery | **IEEE Access 2021** | [Paper](https://ieeexplore.ieee.org/abstract/document/9386091) |

| Deep neural networks for the assessment of surgical skills: A systematic review | **IEEE Access 2021** | [Paper](https://arxiv.org/pdf/2103.05113) |

| Current methods for assessing technical skill in cataract surgery | **JCRS 2021** | [Paper](https://journals.lww.com/jcrs/Abstract/2021/02000/Current_methods_for_assessing_technical_skill_in.18.aspx) |

| Gesture Recognition in Robotic Surgery: a Review | **TBE 2021** | [Paper](https://iris.ucl.ac.uk/iris/publication/1845816/1) |

| Objective assessment of surgical technical skill and competency in the operating room | **ARBE 2017** | [Paper](https://www.annualreviews.org/doi/10.1146/annurev-bioeng-071516-044435) |

| Vision-based and marker-less surgical tool detection and tracking: a review of the literature | **MIA 2017** | [Paper](https://www.sciencedirect.com/science/article/abs/pii/S1361841516301657?via%3Dihub) |

| Surgical process modelling: a review | **IJCARS 2013** | [Paper](https://link.springer.com/article/10.1007%2Fs11548-013-0940-5) |

| Image Processing and Machine Vision in Surgery and Its Training | **JoC 2023** | [Paper](https://joc.kntu.ac.ir/article-1-999-en.html) |

---

## 🎯 Surgical Skill Assessment

| Title | Venue | Links |

| :--------------------: | :-------------: | :-----: |

| Comparative Validation of Machine Learning Algorithms for Surgical Workflow and Skill Analysis with the HeiChole Benchmark | **Arxiv 2021** | [Paper](https://arxiv.org/abs/2109.14956) |

| Video-based Surgical Skill Assessment using Tree-based Gaussian Process Classifier | **Arxiv 2023** | [Paper](https://arxiv.org/html/2312.10208v2) |

| Development and Validation of a 3-Dimensional Convolutional Neural Network for Automatic Surgical Skill Assessment Based on Spatiotemporal Video Analysis | **JAMA 2021** | [Paper](https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2782991?utm_campaign=articlePDF&utm_medium=articlePDFlink&utm_source=articlePDF&utm_content=jamanetworkopen.2021.20786) |

| Surgical Skill Evaluation From Robot-Assisted Surgery Recordings | **ISMR 2021** | [Paper](https://www.researchgate.net/publication/356471297_Surgical_Skill_Evaluation_From_Robot-Assisted_Surgery_Recordings)|

| Towards Unified Surgical Skill Assessment | **CVPR 2021** | [Paper](http://www.vie.group/media/pdf/CVPR2021_Puz4Y7Z.pdf) [Code](https://github.com/Finspire13/Towards-Unified-Surgical-Skill-Assessment) |

| Clearness of Operating Field: A Surrogate for Surgical Skills on In-Vivo Clinical Data | **IJCARS 2020** | [Paper](https://link.springer.com/article/10.1007/s11548-020-02267-z) |

| Construction of Quantitative Indexes for Cataract Surgery Evaluation Based on Deep Learning | **OMIA 2020** | [Paper](https://link.springer.com/chapter/10.1007%2F978-3-030-63419-3_20) |

| Predicting the quality of surgical exposure using spatial and procedural features from laparoscopic videos | **IJCARS 2020** | [Paper](https://link.springer.com/article/10.1007%2Fs11548-019-02072-3) |

| The Pros and Cons: Rank-aware Temporal Attention for Skill Determination in Long Videos | **CVPR 2019** | [Paper](https://openaccess.thecvf.com/content_CVPR_2019/papers/Doughty_The_Pros_and_Cons_Rank-Aware_Temporal_Attention_for_Skill_Determination_CVPR_2019_paper.pdf) [Code](https://github.com/hazeld/rank-aware-attention-network) |

| Surgical Skill Assessment on In-Vivo Clinical Data via the Clearness of Operating Field | **MICCAI 2019** | [Paper](https://arxiv.org/abs/2008.11954) |

| Modeling Surgical Technical Skill Using Expert Assessment for Automated Computer Rating | **AnnS 2019** | [Paper](https://www.researchgate.net/publication/319558609_Modeling_Surgical_Technical_Skill_Using_Expert_Assessment_for_Automated_Computer_Rating) |

| Towards Optimizing Convolutional Neural Networks for Robotic Surgery Skill Evaluation | **IJCNN 2019** | [Paper](https://www.researchgate.net/publication/336167355_Towards_Optimizing_Convolutional_Neural_Networks_for_Robotic_Surgery_Skill_Evaluation) |

| Video-based surgical skill assessment using 3D convolutional neural networks | **IJCARS 2019** | [Paper](https://arxiv.org/abs/1903.02306) [Code](https://gitlab.com/nct_tso_public/surgical_skill_classification?utm_source=catalyzex.com) |

| Accurate and interpretable evaluation of surgical skills from kinematic data using fully convolutional neural networks | **IJCARS 2019** | [Paper](https://arxiv.org/pdf/1908.07319.pdf) [Code](https://github.com/hfawaz/ijcars19) |

| Machine learning methods for automated technical skills assessment with instructional feedback in ultrasound-guided interventions | **IJCARS 2019** | [Paper](https://www.researchgate.net/publication/332546556_Machine_learning_methods_for_automated_technical_skills_assessment_with_instructional_feedback_in_ultrasound-guided_interventions) |

| Objective assessment of intraoperative technical skill in capsulorhexis using videos of cataract surgery | **IJCARS 2019** | [Paper](https://www.cs.jhu.edu/~tkim60/files/IJCARS_FINAL.pdf) |

| Objective classification of psychomotor laparoscopic skills of surgeons based on three different approaches | **IJCARS 2019** | |

| Who’s Better? Who’s Best? Pairwise Deep Ranking for Skill Determination | **CVPR 2018** | |

| Tool Detection and Operative Skill Assessment in Surgical Videos Using Region-Based Convolutional Neural Networks | **WACV 2018** | |

| Evaluating surgical skills from kinematic data using convolutional neural networks | **MICCAI 2018** | [Paper](https://arxiv.org/abs/1806.02750) [Code](https://github.com/hfawaz/miccai18) |

| Automated surgical skill assessment in RMIS training | **IJCARS 2018** | |

| Video and accelerometer-based motion analysis for automated surgical skills assessment | **IJCARS 2018** | |

| Deep learning with convolutional neural network for objective skill evaluation in robot-assisted surgery | **IJCARS 2018** | |

| Surgical skills: Can learning curves be computed from recordings of surgical activities? | **IJCARS 2018** | [Paper](https://link.springer.com/article/10.1007%2Fs11548-018-1713-y) |

| Automated robot‐assisted surgical skill evaluation: Predictive analytics approach | **IJMRCAR 2018** | |

| Meaningful Assessment of Surgical Expertise: Semantic Labeling with Data and Crowds | **MICCAI 2016** | |

| Automated video-based assessment of surgical skills for training and evaluation in medical schools | **IJCARS 2016** | |

| Task-Level vs. Segment-Level Quantitative Metrics for Surgical Skill Assessment | **Surg Educ 2016** | |

| Relative Hidden Markov Models for Video-Based Evaluation of Motion Skills in Surgical Training | **TPAMI 2015** | |

| Automated Assessment of Surgical Skills Using Frequency Analysis | **MICCAI 2015** | |

| Automated objective surgical skill assessment in the operating room from unstructured tool motion in septoplasty | **IJCARS 2015** | |

| Automated Surgical OSATS Prediction From Videos | **ISBI 2014** | |

| Pairwise Comparison-Based Objective Score for Automated Skill Assessment of Segments in a Surgical Task | **IPCAI 2014** | |

| Video Based Assessment of OSATS Using Sequential Motion Textures | **MICCAIW 2014** | |

| Augmenting Bag-of-Words: Data-Driven Discovery of Temporal and Structural Information for Activity Recognition | **CVPR 2013** | |

| String Motif-Based Description of Tool Motion for Detecting Skill and Gestures in Robotic Surgery | **MICCAI 2013** | |

| Robotic Path Planning for Surgeon Skill Evaluation in Minimally-Invasive Sinus Surgery | **MICCAI 2012** | |

| An objective and automated method for assessing surgical skill in endoscopic sinus surgery using eye-tracking and tool-motion data | **IFAR 2012** | |

| Sparse Hidden Markov Models for Surgical Gesture Classification and Skill Evaluation | **IPCAI 2012** | |

| Towards integrating task information in skills assessment for dexterous tasks in surgery and simulation | **ICRA 2011** | |

| Video-based Motion Expertise Analysis in Simulation-based Surgical Training Using Hierarchical Dirichlet Process Hidden Markov Model | **MMAR 2011** | |

| Eye Metrics as an Objective Assessment of Surgical Skill | **AnnS 2010** | |

| Surgical Task and Skill Classification from Eye Tracking and Tool Motion in Minimally Invasive Surgery | **MICCAI 2010** | |

| Task versus Subtask Surgical Skill Evaluation of Robotic Minimally Invasive Surgery | **MICCAI 2009** | |

| Data-derived models for segmentation with application to surgical assessment and training | **MICCAI 2009** | 

| Multitask learning for video-based surgical skill assessment | **DICTA 2009** | [Paper](https://researchers.mq.edu.au/en/publications/multitask-learning-for-video-based-surgical-skill-assessment) | |

---

## 🔄 Surgical Skill Transfer

| Title | Venue | Links |

| :--------------------: | :-------------: | :-----: |

| Deep Neural Skill Assessment and Transfer: Application to Robotic Surgery Training | **IROS 2021** | [Paper](http://www.ece.ualberta.ca/~tbs/pmwiki/pdf/IROS-Soleymani-2021.pdf) |

| Adaptive Surgical Robotic Training Using Real-Time Stylistic Behavior Feedback Through Haptic Cues | **CoRR 2021** | [Paper](https://arxiv.org/pdf/2101.00097) |

| Robot-Assisted Training in Laparoscopy Using Deep Reinforcement Learning | **IEEE RAL 2019** | [Paper](https://www.semanticscholar.org/paper/Robot-Assisted-Training-in-Laparoscopy-Using-Deep-Tan-Chng/f0df16721c94e57d60d1b4ca53c7755caf22626b) |

| Towards Skill Transfer via Learning-Based Guidance in Human-Robot Interaction: An Application to Orthopaedic Surgical Drilling Skill | **JINT 2020** | [Paper](https://link.springer.com/article/10.1007/s10846-019-01082-2) |

| Deep Learning-Based Haptic Guidance for Surgical Skills Transfer | **FiRaA 2021** | [Paper](https://www.frontiersin.org/articles/10.3389/frobt.2020.586707/full) |

| Human-robot skills transfer interfaces for a flexible surgical robot | **CMaPiB 2014** | [Paper](https://dl.acm.org/doi/abs/10.1016/j.cmpb.2013.12.015) |

| DESK: A Robotic Activity Dataset for Dexterous Surgical Skills Transfer to Medical Robots | **RSJ 2019** | [Paper](https://arxiv.org/pdf/1903.00959) [Code](https://github.com/nmadapan/Forward_Project?utm_source=catalyzex.com) |

| Transferring Dexterous Surgical Skill Knowledge between Robots for Semi-autonomous Teleoperation | **CoRaHIC 2019** | [Paper](https://www.researchgate.net/publication/335013887_Transferring_Dexterous_Surgical_Skill_Knowledge_between_Robots_for_Semi-autonomous_Teleoperation) | |

---

## ⏱ Surgical Phase Recognition

| Title | Venue | Links |

| :--------------------: | :-------------: | :-----: |

| Self-supervised representation learning for surgical activity recognition | **IJCARS 2021** | [Paper](https://link.springer.com/content/pdf/10.1007/s11548-021-02493-z.pdf) |

| Surgical Workflow Anticipation Using Instrument Interaction | **MICCAI 2021** | [Paper](https://link.springer.com/chapter/10.1007/978-3-030-87202-1_59) |

| Comparative Validation of Machine Learning Algorithms for Surgical Workflow and Skill Analysis with the HeiChole Benchmark | **Arxiv 2021** | [Paper](https://arxiv.org/abs/2109.14956) |

| OperA: Attention-Regularized Transformers for Surgical Phase Recognition | **MICCAI 2021** | [Paper](https://arxiv.org/abs/2103.03873) |

| Temporal Memory Relation Network for Workflow Recognition from Surgical Video | **TMI 2021** | [Paper](https://arxiv.org/abs/2103.16327) [Code](https://github.com/YuemingJin/TMRNet) |

| Multi-Task Temporal Convolutional Networks for Joint Recognition of Surgical Phases and Steps in Gastric Bypass | **IJCARS 2021** | [Paper](https://arxiv.org/abs/2102.12218v1) |

| Train one, Classify one, Teach one"–Cross-surgery transfer learning for surgical step recognition | **MIDL 2021** | [Paper](https://arxiv.org/pdf/2102.12308v2.pdf) |

| TeCNO: Surgical Phase Recognition with Multi-Stage Temporal Convolutional Networks | **MICCAI 2020** | |

| Automated laparoscopic colorectal surgery workflow recognition using artificial intelligence: Experimental research | **IJS 2020** | [Paper](https://www.sciencedirect.com/science/article/pii/S1743919120303988) |

| Multi-Task Recurrent Convolutional Network with Correlation Loss for Surgical Video Analysis | **MIA 2020** | [Paper](https://arxiv.org/abs/1907.06099) [Code](https://github.com/YuemingJin/MTRCNet-CL) |

| LRTD: Long-Range Temporal Dependency based Active Learning for Surgical Workflow Recognition | **IJCARS 2020** | [Paper](https://link.springer.com/content/pdf/10.1007/s11548-020-02198-9.pdf) [Code](https://github.com/xmichelleshihx/AL-LRTD) |

| Assisted phase and step annotation for surgical videos | **IJCARS 2020** | |

| Accurate Detection of Out of Body Segments in Surgical Video using Semi-Supervised Learning | **MIDL 2020** | [Paper](https://openreview.net/pdf?id=k-ANsPQJxY) |

| Impact of data on generalization of AI for surgical intelligence applications | **SciReports 2020** | [Paper](https://www.nature.com/articles/s41598-020-79173-6) |

| Hard Frame Detection and Online Mapping for Surgical Phase Recognition | **MICCAI 2019** | |

| MS-TCN: Multi-Stage Temporal Convolutional Network for Action Segmentation | **CVPR 2019** | |

| Learning from a tiny dataset of manual annotations: a teacher/student approach for surgical phase recognition | **IPCAI 2019** | [Paper](https://arxiv.org/abs/1812.00033) |

| Machine and deep learning for workflow recognition during surgery | **MITAT 2019** | |

| Assessment of automated identification of phases in videos of cataract surgery using machine learning and deep learning techniques | **JAMA 2019** | [Paper](https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2729808) |

| Multitask learning of temporal connectionism in convolutional networks using a joint distribution loss function to simultaneously identify tools and phase in surgical videos | **Arxiv 2019** | [Paper](https://arxiv.org/pdf/1905.08315.pdf) |

| SV-RCNet: Workflow Recognition from Surgical Videos using Recurrent Convolutional Network | **TMI 2018** | [Paper](https://ieeexplore.ieee.org/document/8240734) [Code](https://github.com/YuemingJin/SV-RCNet) |

| DeepPhase: Surgical Phase Recognition in CATARACTS Videos | **MICCAI 2018** | |

| Surgical activity recognition in robot-assisted radical prostatectomy using deep learning | **MICCAI 2018** | [Paper](https://arxiv.org/pdf/1806.00466v1.pdf) |

| Knowledge transfer for surgical activity prediction | **IJCARS 2018** | [Paper](https://link.springer.com/article/10.1007%2Fs11548-018-1768-9) |

| Temporal coherence-based self-supervised learning for laparoscopic workflow analysis | **MICCAIW 2018** | [Paper](https://arxiv.org/abs/1806.06811) |

| “Deep-Onto” network for surgical workflow and context recognition | **IJCARS 2018** | [Paper](https://link.springer.com/article/10.1007/s11548-018-1882-8) |

| Less is More: Surgical Phase Recognition with Less Annotations through Self-Supervised Pre-training of CNN-LSTM Networks | **Arxiv 2018** | |

| EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos | **TMI 2017**| |

| Unsupervised temporal context learning using convolutional neural networks for laparoscopic workflow analysis | **Arxiv 2017** | [Paper](https://arxiv.org/pdf/1702.03684.pdf) |

| System events: readily accessible features for surgical phase detection | **IJCARS 2016** | [Paper](https://link.springer.com/article/10.1007/s11548-016-1409-0) |

| Automatic data-driven real-time segmentation and recognition of surgical workflow | **IJCARS 2016** | [Paper](https://hal.archives-ouvertes.fr/hal-01299344/document) | 

| MICCAI Workflow Challenge: Convolutional neural networks with time smoothing and Hidden Markov Model for video frames classification | **Arxiv 2016** | |

| Automatic phase prediction from low-level surgical activities | **IJCARS 2015** | [Paper](https://link.springer.com/article/10.1007/s11548-015-1195-0) |

| Lapontospm: an ontology for laparoscopic surgeries and its application to surgical phase recognition | **IJCARS 2015** | [Paper](https://link.springer.com/article/10.1007%2Fs11548-015-1222-1) | 

| Real-time segmentation and recognition of surgical tasks in cataract surgery videos | **TMI 2014** | [Paper](https://ieeexplore.ieee.org/abstract/document/6860246) |

| Statistical modeling and recognition of surgical workflow | **MIA 2012** | |

| A framework for the recognition of high-level surgical tasks from video images for cataract surgeries | **TBE 2012** | [Paper](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3432023/) |

| An application-dependent framework for the recognition of high-level surgical tasks in the OR | **TBE 2011** | |

| Modeling and Segmentation of Surgical Workflow from Laparoscopic Video | **MICCAI 2010** | |

---

## 🤖 Surgical Gesture Recognition

| Title | Venue | Links |

| :--------------------: | :-------------: | :-----: |

| Relational Graph Learning on Visual and Kinematics Embeddings for Accurate Gesture Recognition in Robotic Surgery | **ICRA 2021** | [Paper](https://arxiv.org/abs/2011.01619) |

| Automatic Gesture Recognition in Robot-assisted Surgery with Reinforcement Learning and Tree Search | **ICRA 2020** | [Paper](https://arxiv.org/abs/2002.08718) |

| Multi-Task Recurrent Neural Network for Surgical Gesture Recognition and Progress Prediction | **ICRA 2020** | [Paper](https://discovery.ucl.ac.uk/id/eprint/10111216/) |

| Automated surgical activity recognition with one labeled sequence | **MICCAI 2019** | [Paper](https://arxiv.org/pdf/1907.08825.pdf) |

| Using 3d convolutional neural networks to learn spatiotemporal features for automatic surgical gesture recognition in video | **MICCAI 2019** | [Paper](https://link.springer.com/chapter/10.1007%2F978-3-030-32254-0_52) |

| Segmenting and classifying activities in robot-assisted surgery with recurrent neural networks | **IJCARS 2019** | [Paper](https://link.springer.com/article/10.1007%2Fs11548-019-01953-x) |

| Deep Reinforcement Learning for Surgical Gesture Segmentation and Classification | **MICCAI 2018** | [Paper](https://arxiv.org/abs/1806.08089v1) [Code](https://github.com/Finspire13/RL-Surgical-Gesture-Segmentation) | 

| Unsupervised learning for surgical motion by learning to predict the future | **MICCAI 2018** | | 

| Surgical motion analysis using discriminative interpretable patterns | **AIM 2018** | [Paper](https://www.sciencedirect.com/science/article/pii/S0933365717306681?via%3Dihub) |

| A dataset and benchmarks for segmentation and recognition of gestures in robotic surgery | **TBE 2017**| |

| Temporal Convolutional Networks for Action Segmentation and Detection | **CVPR 2017** | |

| Temporal convolutional networks: A unified approach to action segmentation | **ECCVW 2016** | |

| Recognizing surgical activities with recurrent neural networks | **MICCAI 2016** | |

| Segmental spatiotemporal cnns for fine-grained action segmentation | **ECCV 2016** | |

| Learning convolutional action primitives for fine-grained action recognition | **ICRA 2016** | |

| Unsupervised surgical data alignment with application to automatic activity annotation | **ICRA 2016** | [Paper](https://ieeexplore.ieee.org/document/7487608) |

| An improved model for segmentation and recognition of fine-grained activities with application to surgical training tasks | **WACV 2015** | |

| Learning shared, discriminative dictionaries for surgical gesture segmentation and classification | **MICCAIW 2015** | |

| JHU-ISI gesture and skill assessment working set (JIGSAWS): A surgical activity dataset for human motion modeling | **MICCAIW 2014**| |

| Surgical gesture segmentation and recognition | **MICCAI 2013** | |

| Surgical gesture classification from video and kinematic data | **MIA 2013** | [Paper](https://www.sciencedirect.com/science/article/pii/S1361841513000522) |

| Sparse hidden markov models for surgical gesture classification and skill evaluation | **IPCAI 2011** | |

| Data-derived models for segmentation with application to surgical assessment and training | **MICCAI 2009** | |

| Automatic detection and segmentation of robot-assisted surgical motions | **MICCAI 2005** | |

---

## 🔧 Surgical Instrument Recognition / Localization

| Title | Venue | Links |

| :--------------------: | :-------------: | :-----: |

| ARAS-Farabi Experimental Framework for Skill Assessment in Capsulorhexis Surgery | **ICRoM 2021** | [Paper](https://ieeexplore.ieee.org/document/9663494) |

| Surgical instrument tracking for capsulorhexis eye surgery based on siamese networks | **ICRoM 2022** | [Paper](https://ieeexplore.ieee.org/abstract/document/10025355/) |

| A Deep Dive Into Capsulorhexis Segmentation: From Dataset Creation to SAM Fine-tuning | **ICRoM 2023** | [Paper](https://ieeexplore.ieee.org/abstract/document/10412370/) |

| hSDB-instrument: Instrument Localization Database for Laparoscopic and Robotic Surgeries |  **MICCAI 2021**  | [Paper](https://link.springer.com/chapter/10.1007/978-3-030-87202-1_38) |

| Prototypical Interaction Graph for Unsupervised Domain Adaptation in Surgical Instrument Segmentation |  **MICCAI 2021**  | [Paper](https://link.springer.com/chapter/10.1007/978-3-030-87199-4_26) |

| Co-Generation and Segmentation for Generalized Surgical Instrument Segmentation on Unlabelled Data | **MICCAI 2021** | [Paper](https://arxiv.org/pdf/2103.09276.pdf) |

| Evaluation of Artificial Intelligence–Based Intraoperative Guidance Tools for Phacoemulsification Cataract Surgery | **JAMA 2022** | [Paper](https://jamanetwork.com/journals/jamaophthalmology/article-abstract/2787889) |

| One to Many: Adaptive Instrument Segmentation via Meta Learning and Dynamic Online Adaptation in Robotic Surgical Video | **ICRA 2021** | [Paper](https://arxiv.org/abs/2103.12988) |

| A Kinematic Bottleneck Approach For Pose Regression of Flexible Surgical Instruments directly from Images | **ICRA 2021** | [Paper](https://arxiv.org/abs/2103.00586) |

| Simulation-to-real domain adaptation with teacher–student learning for endoscopic instrument segmentation | **IJCARS 2021** | [Paper](https://link.springer.com/article/10.1007/s11548-021-02383-4) | 

| Searching for Efficient Architecture for Instrument Segmentation in Robotic Surgery | **MICCAI 2020** | [Paper](https://arxiv.org/pdf/2007.04449v1.pdf) |

| Unsupervised Surgical Instrument Segmentation via Anchor Generation and Semantic Diffusion | **MICCAI 2020** | [Paper](https://arxiv.org/abs/2008.11946) [Code](https://github.com/Finspire13/AGSD-Surgical-Instrument-Segmentation) |

| Learning Motion Flows for Semi-supervised Instrument Segmentation from Robotic Surgical Video| **MICCAI 2020** | [Paper](https://arxiv.org/abs/2007.02501) [Code](https://github.com/zxzhaoeric/Semi-InstruSeg) |

| Synthetic and Real Inputs for Tool Segmentation in Robotic Surgery | **MICCAI 2020** | [Paper](https://discovery.ucl.ac.uk/id/eprint/10113753/) | 

| BARNet: Bilinear Attention Network with Adaptive Receptive Field for Surgical Instrument Segmentation | **Arxiv 2020** | |

| Multi-Task Recurrent Convolutional Network with Correlation Loss for Surgical Video Analysis | **MIA 2020** | [Paper](https://arxiv.org/abs/1907.06099) [Code](https://github.com/YuemingJin/MTRCNet-CL) |

| Real-time surgical needle detection using region-based convolutional neural networks | **IJCARS 2020** | [Paper](https://link.springer.com/article/10.1007%2Fs11548-019-02050-9) |

| Learning Representations of Endoscopic Videos to Detect Tool Presence Without Supervision | **ML-CDS 2020** | [Paper](https://link.springer.com/chapter/10.1007/978-3-030-60946-7_6) [Code](https://github.com/zdavidli/tool-presence/) |

| Incorporating Temporal Prior from Motion Flow for Instrument Segmentation in Minimally Invasive Surgery Video | **MICCAI 2019** | [Paper](https://arxiv.org/abs/1907.07899) [Code](https://github.com/keyuncheng/MF-TAPNet) |

| Weakly supervised convolutional LSTM approach for tool tracking in laparoscopic videos | **IJCARS 2019** | |

| Self-supervised surgical tool segmentation using kinematic information | **ICRA 2019** | |

| Learning Where to Look While Tracking Instruments in Robot-assisted Surgery | **MICCAI 2019** | |

| Patch-based adaptive weighting with segmentation and scale (PAWSS) for visual tracking in surgical video | **MIA 2019** | [Paper](https://www.sciencedirect.com/science/article/pii/S1361841519300593) |

| CATARACTS: Challenge on automatic tool annotation for cataRACT surgery | **MIA 2019** | |

| 2017 robotic instrument segmentation challenge | **Arxiv 2019** | |

| Multitask learning of temporal connectionism in convolutional networks using a joint distribution loss function to simultaneously identify tools and phase in surgical videos | **Arxiv 2019** | [Paper](https://arxiv.org/pdf/1905.08315.pdf) |

| U-NetPlus: A Modified Encoder-Decoder U-Net Architecture for Semantic and Instance Segmentation of Surgical Instruments from Laparoscopic Images | **EMBC 2019**| |

| RASNet: Segmentation for tracking surgical instruments in surgical videos using refined attention segmentation network | **EMBC 2019** | |

| CFCM: Segmentation via coarse to fine context memory | **MICCAI 2018** | |

| Monitoring tool usage in surgery videos using boosted convolutional and recurrent neural networks | **MIA 2018** | [Paper](https://www.sciencedirect.com/science/article/pii/S1361841518302470) |

| 3D Pose Estimation of Articulated Instruments in Robotic Minimally Invasive Surgery | **TMI 2018** | [Paper](https://iris.ucl.ac.uk/iris/publication/1539968/1) |

| Exploiting the potential of unlabeled endoscopic video data with self-supervised learning | **IJCARS 2018** | [Paper](https://link.springer.com/article/10.1007/s11548-018-1772-0) |

| Comparative evaluation of instrument segmentation and tracking methods in minimally invasive surgery | **Arxiv 2018** | [Paper](https://arxiv.org/abs/1805.02475) |

| Weakly-supervised learning for tool localization in laparoscopic videos | **MICCAIW 2018** | |

| Automatic instrument segmentation in robot-assisted surgery using deep learning | **ICMLA 2018** | |

| Concurrent segmentation and localization for tracking of surgical instruments | **MICCAI 2017** | |

| Simultaneous recognition and pose estimation of instruments in minimally invasive surgery | **MICCAI 2017** | [Paper](https://discovery.ucl.ac.uk/id/eprint/1576315/) |

| Toolnet: holistically-nested real-time segmentation of robotic surgical tools | **IROS 2017** | |

| Real-time localization of articulated surgical instruments in retinal microsurgery | **MIA 2016** | |

| Combined 2D and 3D tracking of surgical instruments for minimally invasive and robotic-assisted surgery | **IJCARS 2016** | [Paper](https://discovery.ucl.ac.uk/id/eprint/1480794/) |

| Detecting surgical tools by modelling local appearance and global shape | **TMI 2015** | |

| Surgical Tool Tracking and Pose Estimation in Retinal Microsurgery | **MICCAI 2015** | [Paper](https://link.springer.com/chapter/10.1007/978-3-319-24553-9_33) |

| Toward Detection and Localization of Instruments in Minimally Invasive Surgery | **TBE 2013** | [Paper](https://ieeexplore.ieee.org/document/6359786) |

| Unified detection and tracking of instruments during retinal microsurgery | **TPAMI 2013** | [Paper](https://ieeexplore.ieee.org/document/6319313) |

| Data-driven visual tracking in retinal microsurgery | **MICCAI 2012** | [Paper](https://link.springer.com/chapter/10.1007%2F978-3-642-33418-4_70) |

---

## 🏥 Surgical Scene / Anatomy Recognition

| Title | Venue | Links |

| :--------------------: | :-------------: | :-----: |

| Artificial Intelligence for Surgical Safety: Automatic Assessment of the Critical View of Safety in Laparoscopic Cholecystectomy using Deep Learning | **AnnS 2020** | [Paper](https://journals.lww.com/annalsofsurgery/Abstract/9000/Artificial_Intelligence_for_Surgical_Safety_.94005.aspx) |

| EasyLabels: weak labels for scene segmentation in laparoscopic videos | **IJCARS 2019** | [Paper](https://discovery.ucl.ac.uk/id/eprint/10084862/) |

| Kidney edge detection in laparoscopic image data for computer-assisted surgery | **IJCARS 2019** | [Paper](https://link.springer.com/article/10.1007/s11548-019-02102-0) |

| Uncertainty-Aware Organ Classification for Surgical Data Science Applications in Laparoscopy | **TBE 2018** | [Paper](https://ieeexplore.ieee.org/abstract/document/8310960) |

| Long Term Safety Area Tracking (LT-SAT) with online failure detection and recovery for robotic minimally invasive surgery | **MIA 2018** | [Paper](https://doi.org/10.1016/j.media.2017.12.010) |

---

## 👥 Human / Activity in Operating Rooms

| Title | Venue | Links |

| :--------------------: | :-------------: | :-----: |

| Self-supervision on Unlabelled OR Data for Multi-person 2D/3D Human Pose Estimation | **MICCAI 2020** | [Paper](https://arxiv.org/abs/2007.08354) |

| Automatic Operating Room Surgical Activity Recognition for Robot-Assisted Surgery | **MICCAI 2020** | [Paper](https://link.springer.com/chapter/10.1007/978-3-030-59716-0_37) |

| Privacy-Preserving Human Pose Estimation on Low-Resolution Depth Images | **MICCAI 2019** | [Paper](https://arxiv.org/abs/2007.08340) |

| Face Detection in the Operating Room: Comparison of State-of-the-art Methods and a Self-supervised Approach | **IPCAI 2019** | [Paper](https://arxiv.org/abs/1811.12296) |

| A Multi-view RGB-D Approach for Human Pose Estimation in Operating Rooms | **WACV 2017** | [Paper](https://arxiv.org/abs/1701.07372) |

| Pictorial Structures on RGB-D Images for Human Pose Estimation in the Operating Room | **MICCAI 2015** | [Paper](https://link.springer.com/chapter/10.1007/978-3-319-24553-9_45) |

| Data-driven Spatio-temporal RGBD Feature Encoding for Action Recognition in Operating Rooms | **IJCARS 2015** | [Paper](https://link.springer.com/article/10.1007/s11548-015-1186-1) |

| Video based activity recognition in trauma resuscitation | **FG 2013** | [Paper](https://ieeexplore.ieee.org/document/6553758) |

##  **Video-Based Navigation in Surgery** 

| Title | Venue | Links |

| :--------------------: | :-------------: | :-----: |

| Autonomously navigating a surgical tool inside the eye by learning from demonstration | **ICRA 2020** | [Paper](https://asco.lcsr.jhu.edu/wp-content/uploads/2020/09/eye_surgery_ICRA_2020_paris.pdf) |

| Evaluation and stability analysis of video-based navigation system for functional endoscopic sinus surgery on in vivo clinical data | **TMI 2018** | [Paper](https://www.cs.jhu.edu/~ayushis/pdfs/preprints/Leonard_TMI18_Nav.pdf) |

| Endoscopic navigation in the absence of CT imaging | **MICCAI 2018** | [Paper](http://www.cs.jhu.edu/~ayushis/pdfs/preprints/Sinha_MICCAI2018_EndNavwoCT.pdf) |

| Global rigid registration of CT to video in laparoscopic liver surgery | **IJCARS 2018** | [Paper](https://discovery.ucl.ac.uk/id/eprint/10048586/) |

| Intelligent viewpoint selection for efficient CT to video registration in laparoscopic liver surgery | **IJCARS 2017**| [Paper](https://discovery.ucl.ac.uk/id/eprint/1549761/) |

| Rendering-based video-CT registration with physical constraints for image-guided endoscopic sinus surgery | **ISOP 2015** | [Paper](https://www.cs.jhu.edu/~areiter/JHU/Publications_files/Otake_SPIE2015_Paper_VideoCT_9415-8.pdf) |

| A system for video-based navigation for endoscopic endonasal skull base surgery | **TMI 2011** | [Paper](http://www.cs.jhu.edu/~hwang/papers/TMI2011.pdf) |

##  **Others** 

| Title | Venue | Links |

| :--------------------: | :-------------: | :-----: |

| Surgical Instruction Generation with Transformers | **MICCAI 2021** | [Paper](https://arxiv.org/pdf/2107.06964.pdf) |

| SurgeonAssist-Net: Towards Context-Aware Head-Mounted Display-Based Augmented Reality for Surgical Guidance | **MICCAI 2021** | [Paper](https://arxiv.org/pdf/2107.06397.pdf) |

| E-DSSR: Efficient Dynamic Surgical Scene Reconstruction with Transformer-based Stereoscopic Depth Perception | **MICCAI 2021** | [Paper](https://arxiv.org/pdf/2107.00229.pdf) |

| Learning Domain Adaptation with Model Calibration for Surgical Report Generation in Robotic Surgery | **ICRA 2021** | |

| A Computer Vision Platform to Automatically Locate Critical Events in Surgical Videos | **AnnS 2021** | [Paper](https://journals.lww.com/annalsofsurgery/Abstract/9000/A_Computer_Vision_Platform_to_Automatically_Locate.93788.aspx) |

| Ethical implications of AI in robotic surgical training: A Delphi consensus statement | **EUF 2021** | [Paper](https://www.sciencedirect.com/science/article/abs/pii/S2405456921001127) |

| Future Frame Prediction for Robot-assisted Surgery | **IPMI 2021** | [Paper](https://arxiv.org/abs/2103.10308) | 

| Surgical Visual Domain Adaptation: Results from the MICCAI 2020 SurgVisDom Challenge | **Arxiv 2021** | [Paper](https://arxiv.org/abs/2102.13644) | 

| Offline identification of surgical deviations in laparoscopic rectopexy | **AIM 2020** | [Paper](https://www.sciencedirect.com/science/article/pii/S0933365719303185) |

| Recognition of Instrument-Tissue Interactions in Endoscopic Videos via Action Triplets | **MICCAI 2020** | [Paper](https://arxiv.org/abs/2007.05405) |

| Orientation Matters: 6-DoF Autonomous Camera Movement for Minimally Invasive Surgery | **Arxiv 2020** | [Paper](https://arxiv.org/pdf/2012.02836.pdf) |

| Future-State Predicting LSTM for Early Surgery Type Recognition | **TMI 2019** | [Paper](https://arxiv.org/abs/1811.11727) |

| Dense depth estimation in monocular endoscopy with self-supervised learning methods | **TMI 2019** |  |

| Real-time identification of blood regions for hemostasis support in laparoscopic surgery | **SIVP 2019** | [Paper](https://link.springer.com/article/10.1007%2Fs11760-018-1369-7) | 

| RSDNet: Learning to Predict Remaining Surgery Duration from Laparoscopic Videos Without Manual Annotations | **TMI 2018** | [Paper](https://ieeexplore.ieee.org/document/8509608) |

| Learning to see forces: Surgical force prediction with rgb-point cloud temporal convolutional networks | **MICCAIW 2018** | [Paper](https://link.springer.com/chapter/10.1007/978-3-030-01201-4_14) |

| Deep Neural Networks Predict Remaining Surgery Duration from Cholecystectomy Videos | **MICCAI 2017** | [Paper](https://link.springer.com/chapter/10.1007/978-3-319-66185-8_66) |

| Projective biomechanical depth matching for soft tissue registration in laparoscopic surgery | **IJCARS 2017** | [Paper](https://link.springer.com/article/10.1007/s11548-017-1613-6) | 

| Surgical Soundtracks: Towards Automatic Musical Augmentation of Surgical Procedures | **MICCAI 2017** | [Paper](https://link.springer.com/chapter/10.1007/978-3-319-66185-8_76) |

| Distinguishing surgical behavior by sequential pattern discovery | **JBI 2017** | [Paper](https://www.sciencedirect.com/science/article/pii/S1532046417300229) |

| Finding discriminative and interpretable patterns in sequences of surgical activities | **AIM 2017** | [Paper](https://www.sciencedirect.com/science/article/pii/S0933365716305966?via%3Dihub) |

| Automatic matching of surgeries to predict surgeons’ next actions | **AIM 2017** | [Paper](https://www.sciencedirect.com/science/article/pii/S093336571730129X) | 

| Query-by-example surgical activity detection | **IJCARS 2016** | [Paper](https://link.springer.com/article/10.1007/s11548-016-1386-3) |

| Classification Approach for Automatic Laparoscopic Video Database Organization | **IJCARS 2015** | [Paper](https://link.springer.com/article/10.1007/s11548-015-1183-4) |

| Work domain constraints for modelling surgical performance | **IJCARS 2015** | [Paper](https://link.springer.com/article/10.1007%2Fs11548-015-1166-5) |

| Smoke detection in endoscopic surgery videos: a first step towards retrieval of semantic events | **IJMRCAS 2015** | [Paper](https://onlinelibrary.wiley.com/doi/abs/10.1002/rcs.1578) |

| Fisher Kernel Based Task Boundary Retrieval in Laparoscopic Database with Single Video Query | **MICCAI 2014** | [Paper](https://link.springer.com/chapter/10.1007/978-3-319-10443-0_52) |

| Non-linear temporal scaling of surgical processes | **AIM 2014** | [Paper](https://www.sciencedirect.com/science/article/pii/S0933365714001225?via%3Dihu) |

| Surgical process modelling: a review | **IJCARS 2014** | [Paper](https://link.springer.com/article/10.1007/s11548-013-0940-5) |

| Real-time dense stereo reconstruction using convex optimisation with a cost-volume for image-guided robotic surgery | **MICCAI 2013** | [Paper](https://link.springer.com/chapter/10.1007/978-3-642-40811-3_6) |

| Vision-based proximity detection in retinal surgery | **TBE 2012** | [Paper](https://ieeexplore.ieee.org/document/6212340) |

| Automatic knowledge-based recognition of low-level tasks in ophthalmological procedures | **IJCARS 2012** | [Paper](https://link.springer.com/article/10.1007%2Fs11548-012-0685-6) |

| Similarity metrics for surgical process models | **AIM 2012** | [Paper](https://www.sciencedirect.com/science/article/pii/S0933365711001394?via%3Dihub) |