Pixel-Wise Recognition for Holistic Surgical Scene Understanding

• URL: http://arxiv.org/abs/2401.11174v3
• Date: Fri, 27 Dec 2024 01:39:43 GMT
• Title: Pixel-Wise Recognition for Holistic Surgical Scene Understanding
• Authors: Nicolás Ayobi, Santiago Rodríguez, Alejandra Pérez, Isabela Hernández, Nicolás Aparicio, Eugénie Dessevres, Sebastián Peña, Jessica Santander, Juan Ignacio Caicedo, Nicolás Fernández, Pablo Arbeláez,
• Abstract summary: This paper presents the Holistic and Multi-Granular Surgical Scene Understanding of Prostatectomies dataset. Our benchmark models surgical scene understanding as a hierarchy of complementary tasks with varying levels of granularity. To exploit our proposed benchmark, we introduce the Transformers for Actions, Phases, Steps, and Instrument (TAPIS) model.
• Score: 33.40319680006502
• License:
• Abstract: This paper presents the Holistic and Multi-Granular Surgical Scene Understanding of Prostatectomies (GraSP) dataset, a curated benchmark that models surgical scene understanding as a hierarchy of complementary tasks with varying levels of granularity. Our approach encompasses long-term tasks, such as surgical phase and step recognition, and short-term tasks, including surgical instrument segmentation and atomic visual actions detection. To exploit our proposed benchmark, we introduce the Transformers for Actions, Phases, Steps, and Instrument Segmentation (TAPIS) model, a general architecture that combines a global video feature extractor with localized region proposals from an instrument segmentation model to tackle the multi-granularity of our benchmark. Through extensive experimentation in ours and alternative benchmarks, we demonstrate TAPIS's versatility and state-of-the-art performance across different tasks. This work represents a foundational step forward in Endoscopic Vision, offering a novel framework for future research towards holistic surgical scene understanding.

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