Pixel-Wise Recognition for Holistic Surgical Scene Understanding

• URL: http://arxiv.org/abs/2401.11174v2
• Date: Fri, 26 Jan 2024 04:24:07 GMT
• Title: Pixel-Wise Recognition for Holistic Surgical Scene Understanding
• Authors: Nicol\'as Ayobi and Santiago Rodr\'iguez and Alejandra P\'erez and Isabela Hern\'andez and Nicol\'as Aparicio and Eug\'enie Dessevres and Sebasti\'an Pe\~na and Jessica Santander and Juan Ignacio Caicedo and Nicol\'as Fern\'andez and Pablo Arbel\'aez
• Abstract summary: This paper presents the Holistic and Multi-Granular Surgical Scene Understanding of Prostatectomies (GraSP) dataset. GraSP is a curated benchmark that models surgical scene understanding as a hierarchy of complementary tasks with varying levels of granularity. We introduce the Transformers for Actions, Phases, Steps, and Instrument (TAPIS) model, a general architecture that combines a global video feature extractor with localized region proposals.
• Score: 31.338288460529046
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 enables a multi-level comprehension of surgical activities, encompassing long-term tasks such as surgical phases and steps 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, we demonstrate the impact of including segmentation annotations in short-term recognition tasks, highlight the varying granularity requirements of each task, and establish TAPIS's superiority over previously proposed baselines and conventional CNN-based models. Additionally, we validate the robustness of our method across multiple public benchmarks, confirming the reliability and applicability of our dataset. This work represents a significant step forward in Endoscopic Vision, offering a novel and comprehensive framework for future research towards a holistic understanding of surgical procedures.

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