I-MedSAM: Implicit Medical Image Segmentation with Segment Anything

TL;DR

I-MedSAM introduces an implicit, continuous representation-based medical image segmentation method that leverages SAM features and an implicit decoder, achieving superior accuracy and cross-domain adaptability with fewer trainable parameters.

Contribution

The paper proposes I-MedSAM, combining SAM features with an implicit neural decoder and an uncertainty-guided sampling strategy for improved medical image segmentation.

Findings

Outperforms existing methods on 2D medical segmentation tasks

Uses only 1.6 million trainable parameters

Achieves better boundary delineation and cross-domain performance

Abstract

With the development of Deep Neural Networks (DNNs), many efforts have been made to handle medical image segmentation. Traditional methods such as nnUNet train specific segmentation models on the individual datasets. Plenty of recent methods have been proposed to adapt the foundational Segment Anything Model (SAM) to medical image segmentation. However, they still focus on discrete representations to generate pixel-wise predictions, which are spatially inflexible and scale poorly to higher resolution. In contrast, implicit methods learn continuous representations for segmentation, which is crucial for medical image segmentation. In this paper, we propose I-MedSAM, which leverages the benefits of both continuous representations and SAM, to obtain better cross-domain ability and accurate boundary delineation. Since medical image segmentation needs to predict detailed segmentation boundaries, we designed a novel adapter to enhance the SAM features with high-frequency information during Parameter-Efficient Fine-Tuning (PEFT). To convert the SAM features and coordinates into continuous segmentation output, we utilize Implicit Neural Representation (INR) to learn an implicit segmentation decoder. We also propose an uncertainty-guided sampling strategy for efficient learning of INR. Extensive evaluations on 2D medical image segmentation tasks have shown that our proposed method with only 1.6M trainable parameters outperforms existing methods including discrete and implicit methods. The code will be available at: https://github.com/ucwxb/I-MedSAM.