SurgicalPart-SAM: Part-to-Whole Collaborative Prompting for Surgical Instrument Segmentation

• URL: http://arxiv.org/abs/2312.14481v2
• Date: Sat, 23 Mar 2024 03:13:35 GMT
• Title: SurgicalPart-SAM: Part-to-Whole Collaborative Prompting for Surgical Instrument Segmentation
• Authors: Wenxi Yue, Jing Zhang, Kun Hu, Qiuxia Wu, Zongyuan Ge, Yong Xia, Jiebo Luo, Zhiyong Wang,
• Abstract summary: The Segment Anything Model (SAM) exhibits promise in generic object segmentation and offers potential for various applications. Existing methods have applied SAM to surgical instrument segmentation (SIS) by tuning SAM-based frameworks with surgical data. We propose SurgicalPart-SAM (SP-SAM), a novel SAM efficient-tuning approach that explicitly integrates instrument structure knowledge with SAM's generic knowledge.
• Score: 66.21356751558011
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The Segment Anything Model (SAM) exhibits promise in generic object segmentation and offers potential for various applications. Existing methods have applied SAM to surgical instrument segmentation (SIS) by tuning SAM-based frameworks with surgical data. However, they fall short in two crucial aspects: (1) Straightforward model tuning with instrument masks treats each instrument as a single entity, neglecting their complex structures and fine-grained details; and (2) Instrument category-based prompts are not flexible and informative enough to describe instrument structures. To address these problems, in this paper, we investigate text promptable SIS and propose SurgicalPart-SAM (SP-SAM), a novel SAM efficient-tuning approach that explicitly integrates instrument structure knowledge with SAM's generic knowledge, guided by expert knowledge on instrument part compositions. Specifically, we achieve this by proposing (1) Collaborative Prompts that describe instrument structures via collaborating category-level and part-level texts; (2) Cross-Modal Prompt Encoder that encodes text prompts jointly with visual embeddings into discriminative part-level representations; and (3) Part-to-Whole Adaptive Fusion and Hierarchical Decoding that adaptively fuse the part-level representations into a whole for accurate instrument segmentation in surgical scenarios. Built upon them, SP-SAM acquires a better capability to comprehend surgical instruments in terms of both overall structure and part-level details. Extensive experiments on both the EndoVis2018 and EndoVis2017 datasets demonstrate SP-SAM's state-of-the-art performance with minimal tunable parameters. The code will be available at https://github.com/wenxi-yue/SurgicalPart-SAM.

Related papers

• Amodal Segmentation for Laparoscopic Surgery Video Instruments [30.39518393494816]
  We introduce AmodalVis to the realm of surgical instruments in the medical field. This technique identifies both the visible and occluded parts of an object. To achieve this, we introduce a new Amoal Instruments dataset.
  arXiv  Detail & Related papers  (2024-08-02T07:40:34Z)
• SAM-CP: Marrying SAM with Composable Prompts for Versatile Segmentation [88.80792308991867]
  Segment Anything model (SAM) has shown ability to group image pixels into patches, but applying it to semantic-aware segmentation still faces major challenges. This paper presents SAM-CP, a simple approach that establishes two types of composable prompts beyond SAM and composes them for versatile segmentation. Experiments show that SAM-CP achieves semantic, instance, and panoptic segmentation in both open and closed domains.
  arXiv  Detail & Related papers  (2024-07-23T17:47:25Z)
• Surgical-DeSAM: Decoupling SAM for Instrument Segmentation in Robotic Surgery [9.466779367920049]
  In safety-critical surgical tasks, prompting is not possible due to lack of per-frame prompts for supervised learning. It is unrealistic to prompt frame-by-frame in a real-time tracking application, and it is expensive to annotate prompts for offline applications. We develop Surgical-DeSAM to generate automatic bounding box prompts for decoupling SAM to obtain instrument segmentation in real-time robotic surgery.
  arXiv  Detail & Related papers  (2024-04-22T09:53:55Z)
• Learning to Prompt Segment Anything Models [55.805816693815835]
  Segment Anything Models (SAMs) have demonstrated great potential in learning to segment anything. SAMs work with two types of prompts including spatial prompts (e.g., points) and semantic prompts (e.g., texts) We propose spatial-semantic prompt learning (SSPrompt) that learns effective semantic and spatial prompts for better SAMs.
  arXiv  Detail & Related papers  (2024-01-09T16:24:25Z)
• Open-Vocabulary SAM: Segment and Recognize Twenty-thousand Classes Interactively [69.97238935096094]
  The Open-Vocabulary SAM is a SAM-inspired model designed for simultaneous interactive segmentation and recognition. Our method can segment and recognize approximately 22,000 classes.
  arXiv  Detail & Related papers  (2024-01-05T18:59:22Z)
• SurgicalSAM: Efficient Class Promptable Surgical Instrument Segmentation [65.52097667738884]
  We introduce SurgicalSAM, a novel end-to-end efficient-tuning approach for SAM to integrate surgical-specific information with SAM's pre-trained knowledge for improved generalisation. Specifically, we propose a lightweight prototype-based class prompt encoder for tuning, which directly generates prompt embeddings from class prototypes. In addition, to address the low inter-class variance among surgical instrument categories, we propose contrastive prototype learning.
  arXiv  Detail & Related papers  (2023-08-17T02:51:01Z)
• SAM Meets Robotic Surgery: An Empirical Study on Generalization, Robustness and Adaptation [15.995869434429274]
  The Segment Anything Model (SAM) serves as a fundamental model for semantic segmentation. We examine SAM's robustness and zero-shot generalizability in the field of robotic surgery.
  arXiv  Detail & Related papers  (2023-08-14T14:09:41Z)
• SAM Meets Robotic Surgery: An Empirical Study in Robustness Perspective [21.2080716792596]
  Segment Anything Model (SAM) is a foundation model for semantic segmentation. We investigate the robustness and zero-shot generalizability of the SAM in the domain of robotic surgery.
  arXiv  Detail & Related papers  (2023-04-28T08:06:33Z)
• Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation [51.770805270588625]
  The Segment Anything Model (SAM) has recently gained popularity in the field of image segmentation. Recent studies and individual experiments have shown that SAM underperforms in medical image segmentation. We propose the Medical SAM Adapter (Med-SA), which incorporates domain-specific medical knowledge into the segmentation model.
  arXiv  Detail & Related papers  (2023-04-25T07:34:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.