Edge2Prompt: Modality-Agnostic Model for Out-of-Distribution Liver Segmentation

TL;DR

Edge2Prompt introduces a modality-agnostic liver segmentation pipeline that combines classical edge detection with foundation models, enabling effective out-of-distribution segmentation in clinical scenarios with limited data.

Contribution

It presents a novel method integrating edge detection and foundation models to achieve robust, modality-agnostic liver segmentation, especially in data-scarce and out-of-distribution settings.

Findings

Achieves 86.4% Dice score on OOD liver segmentation tasks.

Outperforms U-Net baseline by 27.4% in data-scarce scenarios.

Outperforms other self-prompting methods by 9.1%.

Abstract

Liver segmentation is essential for preoperative planning in interventions like tumor resection or transplantation, but implementation in clinical workflows faces challenges due to modality-specific tools and data scarcity. We propose Edge2Prompt, a novel pipeline for modality-agnostic liver segmentation that generalizes to out-of-distribution (OOD) data. Our method integrates classical edge detection with foundation models. Modality-agnostic edge maps are first extracted from input images, then processed by a U-Net to generate logit-based prompts. These prompts condition the Segment Anything Model 2 (SAM-2) to generate 2D liver segmentations, which can then be reconstructed into 3D volumes. Evaluated on the multi-modal CHAOS dataset, Edge2Prompt achieves competitive results compared to classical segmentation methods when trained and tested in-distribution (ID), and outperforms them in data-scarce scenarios due to the SAM-2 module. Furthermore, it achieves a mean Dice Score of 86.4% on OOD tasks, outperforming U-Net baselines by 27.4% and other self-prompting methods by 9.1%, demonstrating its effectiveness. This work bridges classical and foundation models for clinically adaptable, data-efficient segmentation.