Rethinking RGB-D Fusion for Semantic Segmentation in Surgical Datasets

• URL: http://arxiv.org/abs/2407.19714v1
• Date: Mon, 29 Jul 2024 05:35:51 GMT
• Title: Rethinking RGB-D Fusion for Semantic Segmentation in Surgical Datasets
• Authors: Muhammad Abdullah Jamal, Omid Mohareri,
• Abstract summary: We propose a simple yet effective multi-modal (RGB and depth) training framework called SurgDepth. We show state-of-the-art (SOTA) results on all publicly available datasets applicable for this task. We conduct extensive experiments on benchmark datasets including EndoVis2022, AutoLapro, LapI2I and EndoVis 2017.
• Score: 5.069884983892437
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Surgical scene understanding is a key technical component for enabling intelligent and context aware systems that can transform various aspects of surgical interventions. In this work, we focus on the semantic segmentation task, propose a simple yet effective multi-modal (RGB and depth) training framework called SurgDepth, and show state-of-the-art (SOTA) results on all publicly available datasets applicable for this task. Unlike previous approaches, which either fine-tune SOTA segmentation models trained on natural images, or encode RGB or RGB-D information using RGB only pre-trained backbones, SurgDepth, which is built on top of Vision Transformers (ViTs), is designed to encode both RGB and depth information through a simple fusion mechanism. We conduct extensive experiments on benchmark datasets including EndoVis2022, AutoLapro, LapI2I and EndoVis2017 to verify the efficacy of SurgDepth. Specifically, SurgDepth achieves a new SOTA IoU of 0.86 on EndoVis 2022 SAR-RARP50 challenge and outperforms the current best method by at least 4%, using a shallow and compute efficient decoder consisting of ConvNeXt blocks.

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