# Encoding Surgical Videos as Latent Spatiotemporal Graphs for Object and Anatomy-Driven Reasoning

arXiv: https://arxiv.org/abs/2312.06829 · HTML: https://mlsanity.com/paper/2312.06829/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 29 references that are in the map.
- First posted on arXiv 11 Dec 2023.

## Builds on (most cited first, 24 of 24)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [Temporal Convolutional Networks for Action Segmentation and Detection](https://mlsanity.com/paper/1611.05267/) (2016)
- [EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos](https://mlsanity.com/paper/1602.03012/) (2016)
- [Videos as Space-Time Region Graphs](https://mlsanity.com/paper/1806.01810/) (2018)
- [TeCNO: Surgical Phase Recognition with Multi-Stage Temporal Convolutional Networks](https://mlsanity.com/paper/2003.10751/) (2020)
- [Surgical Data Science: Enabling Next-Generation Surgery](https://mlsanity.com/paper/1701.06482/) (2017)
- [Something-Else: Compositional Action Recognition with Spatial-Temporal Interaction Networks](https://mlsanity.com/paper/1912.09930/) (2019)
- [CholecSeg8k: A Semantic Segmentation Dataset for Laparoscopic Cholecystectomy Based on Cholec80](https://mlsanity.com/paper/2012.12453/) (2020)
- [Trans-SVNet: Accurate Phase Recognition from Surgical Videos via Hybrid Embedding Aggregation Transformer](https://mlsanity.com/paper/2103.09712/) (2021)
- [Semantic Image Manipulation Using Scene Graphs](https://mlsanity.com/paper/2004.03677/) (2020)
- [Object-Region Video Transformers](https://mlsanity.com/paper/2110.06915/) (2021)
- [4D-OR: Semantic Scene Graphs for OR Domain Modeling](https://mlsanity.com/paper/2203.11937/) (2022)
- [Dissecting Self-Supervised Learning Methods for Surgical Computer Vision](https://mlsanity.com/paper/2207.00449/) (2022)
- [CholecTriplet2021: A benchmark challenge for surgical action triplet recognition](https://mlsanity.com/paper/2204.04746/) (2022)
- [CAI4CAI: The Rise of Contextual Artificial Intelligence in Computer Assisted Interventions](https://mlsanity.com/paper/1910.09031/) (2019)
- [Latent Graph Representations for Critical View of Safety Assessment](https://mlsanity.com/paper/2212.04155/) (2022)
- [Using 3D Convolutional Neural Networks to Learn Spatiotemporal Features for Automatic Surgical Gesture Recognition in Video](https://mlsanity.com/paper/1907.11454/) (2019)
- [Learning and Reasoning with the Graph Structure Representation in Robotic Surgery](https://mlsanity.com/paper/2007.03357/) (2020)
- [Global-Reasoned Multi-Task Learning Model for Surgical Scene Understanding](https://mlsanity.com/paper/2201.11957/) (2022)
- [Is an Object-Centric Video Representation Beneficial for Transfer?](https://mlsanity.com/paper/2207.10075/) (2022)
- [Differentiable Scene Graphs](https://mlsanity.com/paper/1902.10200/) (2019)
- [Towards Generalizable Surgical Activity Recognition Using Spatial Temporal Graph Convolutional Networks](https://mlsanity.com/paper/2001.03728/) (2020)
- [Spatiotemporal Deformable Scene Graphs for Complex Activity Detection](https://mlsanity.com/paper/2104.08194/) (2021)

## Built on by (most cited first, 14 of 14)

- [The Endoscapes Dataset for Surgical Scene Segmentation, Object Detection, and Critical View of Safety Assessment: Official Splits and Benchmark](https://mlsanity.com/paper/2312.12429/) (2023)
- [ORacle: Large Vision-Language Models for Knowledge-Guided Holistic OR Domain Modeling](https://mlsanity.com/paper/2404.07031/) (2024)
- [SANGRIA: Surgical Video Scene Graph Optimization for Surgical Workflow Prediction](https://mlsanity.com/paper/2407.20214/) (2024)
- [The SAGES Critical View of Safety Challenge: A Global Benchmark for AI-Assisted Surgical Quality Assessment](https://mlsanity.com/paper/2509.17100/) (2025)
- [CAT-SG: A Large Dynamic Scene Graph Dataset for Fine-Grained Understanding of Cataract Surgery](https://mlsanity.com/paper/2506.21813/) (2025)
- [Adaptation of Multi-modal Representation Models for Multi-task Surgical Computer Vision](https://mlsanity.com/paper/2507.05020/) (2025)
- [Multi-modal Representations for Fine-grained Multi-label Critical View of Safety Recognition](https://mlsanity.com/paper/2507.05007/) (2025)
- [Towards Holistic Surgical Scene Graph](https://mlsanity.com/paper/2507.15541/) (2025)
- [Optimizing Latent Graph Representations of Surgical Scenes for Zero-Shot Domain Transfer](https://mlsanity.com/paper/2403.06953/) (2024)
- [SurgTEMP: Temporal-Aware Surgical Video Question Answering with Text-guided Visual Memory for Laparoscopic Cholecystectomy](https://mlsanity.com/paper/2603.29962/) (2026)
- [ProtoFlow: Interpretable and Robust Surgical Workflow Modeling with Learned Dynamic Scene Graph Prototypes](https://mlsanity.com/paper/2512.14092/) (2025)
- [Gaussian Spatial Priors for Anatomy-Aware Object Detection in Surgical Videos](https://mlsanity.com/paper/2606.15049/) (2026)
- [CasCVS-Net: A Staged Multi-Task Cascade for Critical View of Safety Assessment](https://mlsanity.com/paper/2609.27681/) (2026)
- [Structured Reasoning Agentic Framework for Interpretable Critical View of Safety Assessment](https://mlsanity.com/paper/2609.31524/) (2026)

## Abstract (arXiv)

Recently, spatiotemporal graphs have emerged as a concise and elegant manner of representing video clips in an object-centric fashion, and have shown to be useful for downstream tasks such as action recognition. In this work, we investigate the use of latent spatiotemporal graphs to represent a surgical video in terms of the constituent anatomical structures and tools and their evolving properties over time. To build the graphs, we first predict frame-wise graphs using a pre-trained model, then add temporal edges between nodes based on spatial coherence and visual and semantic similarity. Unlike previous approaches, we incorporate long-term temporal edges in our graphs to better model the evolution of the surgical scene and increase robustness to temporary occlusions. We also introduce a novel graph-editing module that incorporates prior knowledge and temporal coherence to correct errors in the graph, enabling improved downstream task performance. Using our graph representations, we evaluate two downstream tasks, critical view of safety prediction and surgical phase recognition, obtaining strong results that demonstrate the quality and flexibility of the learned representations. Code is available at github.com/CAMMA-public/SurgLatentGraph.