Data Adaptive Few-shot Multi Label Segmentation with Foundation Model

TL;DR

This paper introduces a foundation model-based approach for adaptive few-shot multi-label segmentation and localization in medical images, addressing limitations of existing methods in handling variability and multi-label complexity.

Contribution

The work proposes foundation model adapters for robust multi-label segmentation and localization, improving generalization across diverse clinical data and modalities.

Findings

Effective in 2D and 3D medical image segmentation

Handles pose variations and inter-protocol differences

Outperforms existing few-shot segmentation methods

Abstract

The high cost of obtaining accurate annotations for image segmentation and localization makes the use of one and few shot algorithms attractive. Several state-of-the-art methods for few-shot segmentation have emerged, including text-based prompting for the task but suffer from sub-optimal performance for medical images. Leveraging sub-pixel level features of existing Vision Transformer (ViT) based foundation models for identifying similar region of interest (RoI) based on a single template image have been shown to be very effective for one shot segmentation and localization in medical images across modalities. However, such methods rely on assumption that template image and test image are well matched and simple correlation is sufficient to obtain correspondences. In practice, however such an approach can fail to generalize in clinical data due to patient pose changes, inter-protocol variations even within a single modality or extend to 3D data using single template image. Moreover, for multi-label tasks, the RoI identification has to be performed sequentially. In this work, we propose foundation model (FM) based adapters for single label, multi-label localization and segmentation to address these concerns. We demonstrate the efficacy of the proposed method for multiple segmentation and localization tasks for both 2D and 3D data as we well as clinical data with different poses and evaluate against the state of the art few shot segmentation methods.