I-MedSAM: Implicit Medical Image Segmentation with Segment Anything
Xiaobao Wei Jiajun Cao Yizhu Jin Ming Lu Guangyu Wang Shanghang Zhang
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 continuous methods. The code will be released.