# Text Promptable Surgical Instrument Segmentation with Vision-Language Models

arXiv: https://arxiv.org/abs/2306.09244 · HTML: https://mlsanity.com/paper/2306.09244/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- Placed in Medical vision-language models, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 44 of its 55 references that are in the map.
- First posted on arXiv 15 Jun 2023.

## Placed in

- Multimodal LLMs / vision-language models / [Medical vision-language models](https://mlsanity.com/mllm/medical-vision-language-models/)

## Builds on (most cited first, 30 of 44)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [LAION-5B: An open large-scale dataset for training next generation image-text models](https://mlsanity.com/paper/2210.08402/) (2022)
- [Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer](https://mlsanity.com/paper/1701.06538/) (2017)
- [Learning to Prompt for Vision-Language Models](https://mlsanity.com/paper/2109.01134/) (2021)
- [StyleCLIP: Text-Driven Manipulation of StyleGAN Imagery](https://mlsanity.com/paper/2103.17249/) (2021)
- [Scaling Vision with Sparse Mixture of Experts](https://mlsanity.com/paper/2106.05974/) (2021)
- [CLIP4Clip: An Empirical Study of CLIP for End to End Video Clip Retrieval](https://mlsanity.com/paper/2104.08860/) (2021)
- [SimVLM: Simple Visual Language Model Pretraining with Weak Supervision](https://mlsanity.com/paper/2108.10904/) (2021)
- [Image Segmentation Using Text and Image Prompts](https://mlsanity.com/paper/2112.10003/) (2021)
- [End-to-End Learning of Visual Representations from Uncurated Instructional Videos](https://mlsanity.com/paper/1912.06430/) (2019)
- [CRIS: CLIP-Driven Referring Image Segmentation](https://mlsanity.com/paper/2111.15174/) (2021)
- [LAVT: Language-Aware Vision Transformer for Referring Image Segmentation](https://mlsanity.com/paper/2112.02244/) (2021)
- [Learning to Prompt for Open-Vocabulary Object Detection with Vision-Language Model](https://mlsanity.com/paper/2203.14940/) (2022)
- [Cross-Modal Self-Attention Network for Referring Image Segmentation](https://mlsanity.com/paper/1904.04745/) (2019)
- [Segmentation from Natural Language Expressions](https://mlsanity.com/paper/1603.06180/) (2016)
- [Vision-Language Transformer and Query Generation for Referring Segmentation](https://mlsanity.com/paper/2108.05565/) (2021)
- [CLIP2Video: Mastering Video-Text Retrieval via Image CLIP](https://mlsanity.com/paper/2106.11097/) (2021)
- [CLIP-Driven Universal Model for Organ Segmentation and Tumor Detection](https://mlsanity.com/paper/2301.00785/) (2023)
- [ReSTR: Convolution-free Referring Image Segmentation Using Transformers](https://mlsanity.com/paper/2203.16768/) (2022)

## Built on by (most cited first, 28 of 28)

- [Image Segmentation in Foundation Model Era: A Survey](https://mlsanity.com/paper/2408.12957/) (2024)
- [OphCLIP: Hierarchical Retrieval-Augmented Learning for Ophthalmic Surgical Video-Language Pretraining](https://mlsanity.com/paper/2411.15421/) (2024)
- [MM-OR: A Large Multimodal Operating Room Dataset for Semantic Understanding of High-Intensity Surgical Environments](https://mlsanity.com/paper/2503.02579/) (2025)
- [CholecInstanceSeg: A Tool Instance Segmentation Dataset for Laparoscopic Surgery](https://mlsanity.com/paper/2406.16039/) (2024)
- [ReSurgSAM2: Referring Segment Anything in Surgical Video via Credible Long-term Tracking](https://mlsanity.com/paper/2505.08581/) (2025)
- [SurgVidLM: Towards Multi-grained Surgical Video Understanding with Large Language Model](https://mlsanity.com/paper/2506.17873/) (2025)
- [VLPrompt: Vision-Language Prompting for Panoptic Scene Graph Generation](https://mlsanity.com/paper/2311.16492/) (2023)
- [Taming Vision-Language Models for Medical Image Analysis: A Comprehensive Review](https://mlsanity.com/paper/2506.18378/) (2025)
- [SurgicalPart-SAM: Part-to-Whole Collaborative Prompting for Surgical Instrument Segmentation](https://mlsanity.com/paper/2312.14481/) (2023)
- [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)
- [Augmenting Efficient Real-time Surgical Instrument Segmentation in Video with Point Tracking and Segment Anything](https://mlsanity.com/paper/2403.08003/) (2024)
- [SurgTPGS: Semantic 3D Surgical Scene Understanding with Text Promptable Gaussian Splatting](https://mlsanity.com/paper/2506.23309/) (2025)
- [Personalizing Federated Instrument Segmentation with Visual Trait Priors in Robotic Surgery](https://mlsanity.com/paper/2408.03208/) (2024)
- [Segment as You Wish -- Free-Form Language-Based Segmentation for Medical Images](https://mlsanity.com/paper/2410.12831/) (2024)
- [SCOPE: Speech-guided COllaborative PErception Framework for Surgical Scene Segmentation](https://mlsanity.com/paper/2509.10748/) (2025)
- [An Approach to Enriching Surgical Video Datasets for Fine-Grained Spatial-Temporal Understanding of Vision-Language Models](https://mlsanity.com/paper/2604.00784/) (2026)
- [Hierarchical Prototype-Memory Adaptation of SAM for Surgical Instrument Segmentation](https://mlsanity.com/paper/2608.24541/) (2026)
- [Unity in Diversity: Multi-expert Knowledge Confrontation and Collaboration for Generalizable Vehicle Re-identification](https://mlsanity.com/paper/2407.07351/) (2024)
- [Bootstrapping Vision-language Models for Self-supervised Remote Physiological Measurement](https://mlsanity.com/paper/2407.08507/) (2024)
- [Rethinking Text-Promptable Surgical Instrument Segmentation with Robust Framework](https://mlsanity.com/paper/2411.12199/) (2024)
- [SurgPIS: Surgical-instrument-level Instances and Part-level Semantics for Weakly-supervised Part-aware Instance Segmentation](https://mlsanity.com/paper/2507.19592/) (2025)
- [ReMeDI: Refined Memory for Disambiguation of Identities with SAM3 in Surgical Segmentation](https://mlsanity.com/paper/2512.16880/) (2025)
- [VLM-Guided Iterative Refinement for Surgical Image Segmentation with Foundation Models](https://mlsanity.com/paper/2602.09252/) (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)
- [Bootstrapping Vision-Language Model for Hysteroscopic Surgical Scene Segmentation](https://mlsanity.com/paper/2608.09302/) (2026)
- [SIRA: Reasoning-Aware Surgical Instrument Segmentation via Query-Anchored Alignment](https://mlsanity.com/paper/2609.21402/) (2026)
- [LD-RSVIS: A Large-Scale and Diverse Benchmark for Referring Surgical Video Instrument Segmentation](https://mlsanity.com/paper/2609.23067/) (2026)
- [Multi-modal Representation Fusion for Long-term Microsurgical Anastomosis Trajectory Anticipation](https://mlsanity.com/paper/2610.14492/) (2026)

## Abstract (arXiv)

In this paper, we propose a novel text promptable surgical instrument segmentation approach to overcome challenges associated with diversity and differentiation of surgical instruments in minimally invasive surgeries. We redefine the task as text promptable, thereby enabling a more nuanced comprehension of surgical instruments and adaptability to new instrument types. Inspired by recent advancements in vision-language models, we leverage pretrained image and text encoders as our model backbone and design a text promptable mask decoder consisting of attention- and convolution-based prompting schemes for surgical instrument segmentation prediction. Our model leverages multiple text prompts for each surgical instrument through a new mixture of prompts mechanism, resulting in enhanced segmentation performance. Additionally, we introduce a hard instrument area reinforcement module to improve image feature comprehension and segmentation precision. Extensive experiments on several surgical instrument segmentation datasets demonstrate our model's superior performance and promising generalization capability. To our knowledge, this is the first implementation of a promptable approach to surgical instrument segmentation, offering significant potential for practical application in the field of robotic-assisted surgery. Code is available at https://github.com/franciszzj/TP-SIS.