CLISC: Bridging clip and sam by enhanced cam for unsupervised brain tumor segmentation

TL;DR

This paper introduces an unsupervised brain tumor segmentation method that combines foundation models, CAM, and SAM to achieve high accuracy without annotated training data, nearing supervised performance.

Contribution

The study presents a novel unsupervised segmentation pipeline leveraging CLIP, CAM, and SAM, significantly improving accuracy over existing methods.

Findings

Achieved an average DSC of 85.60% on BraTS2020.

Outperformed five state-of-the-art unsupervised methods by over 10 percentage points.

Close performance to fully supervised learning.

Abstract

Brain tumor segmentation is important for diagnosis of the tumor, and current deep-learning methods rely on a large set of annotated images for training, with high annotation costs. Unsupervised segmentation is promising to avoid human annotations while the performance is often limited. In this study, we present a novel unsupervised segmentation approach that leverages the capabilities of foundation models, and it consists of three main steps: (1) A vision-language model (i.e., CLIP) is employed to obtain image-level pseudo-labels for training a classification network. Class Activation Mapping (CAM) is then employed to extract Regions of Interest (ROIs), where an adaptive masking-based data augmentation is used to enhance ROI identification.(2) The ROIs are used to generate bounding box and point prompts for the Segment Anything Model (SAM) to obtain segmentation pseudo-labels. (3) A 3D segmentation network is trained with the SAM-derived pseudo-labels, where low-quality pseudo-labels are filtered out in a self-learning process based on the similarity between the SAM's output and the network's prediction. Evaluation on the BraTS2020 dataset demonstrates that our approach obtained an average Dice Similarity Score (DSC) of 85.60%, outperforming five state-of-the-art unsupervised segmentation methods by more than 10 percentage points. Besides, our approach outperforms directly using SAM for zero-shot inference, and its performance is close to fully supervised learning.