PDZSeg: adapting the foundation model for dissection zone segmentation with visual prompts in robot-assisted endoscopic submucosal dissection

TL;DR

This paper introduces PDZSeg, a new model for dissection zone segmentation in endoscopic surgery that uses visual prompts to improve accuracy and user experience.

Contribution

The novel contribution is the first integration of visual prompts like scribbles and bounding boxes into dissection zone segmentation for endoscopic submucosal dissection.

Findings

PDZSeg outperforms state-of-the-art segmentation methods in dissection zone tasks.

The model improves performance and user experience through a specialized dataset and visual referral method.

The ESD-DZSeg dataset is introduced as a benchmark for future research in this area.

Abstract

The intricate nature of endoscopic surgical environments poses significant challenges for the task of dissection zone segmentation. Specifically, the boundaries between different tissue types lack clarity, which can result in significant segmentation errors, as the models may misidentify or overlook object edges altogether. Thus, the goal of this work is to achieve the precise dissection zone suggestion under these challenges during endoscopic submucosal dissection (ESD) procedures and enhance the overall safety of ESD.

We introduce a prompted-based dissection zone segmentation (PDZSeg) model, aimed at segmenting dissection zones and specifically designed to incorporate different visual prompts, such as scribbles and bounding boxes. Our approach overlays these visual cues directly onto the images, utilizing fine-tuning of the foundational model on a specialized dataset created to handle diverse visual prompt instructions. This shift toward more flexible input methods is intended to significantly improve both the performance of dissection zone segmentation and the overall user experience.

We evaluate our approaches using the three experimental setups: in-domain evaluation, evaluation under variability in visual prompts availability, and robustness assessment. By validating our approaches on the ESD-DZSeg dataset, specifically focused on the dissection zone segmentation task of ESD, our experimental results show that our solution outperforms state-of-the-art segmentation methods for this task. To the best of our knowledge, this is the first study to incorporate visual prompt design in dissection zone segmentation.

We introduce the prompted-based dissection zone segmentation (PDZSeg) model, which is specifically designed for dissection zone segmentation and can effectively utilize various visual prompts, including scribbles and bounding boxes. This model improves segmentation performance and enhances user experience by integrating a specialized dataset with a novel visual referral method that optimizes the architecture and boosts the effectiveness of dissection zone suggestions. Furthermore, we present the ESD-DZSeg dataset for robot-assisted endoscopic submucosal dissection (ESD), which serves as a benchmark for assessing dissection zone suggestions and visual prompt interpretation, thus laying the groundwork for future research in this field. Our code is available at https://github.com/FrankMOWJ/PDZSeg.

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