CLIP-TNseg: A Multi-Modal Hybrid Framework for Thyroid Nodule Segmentation in Ultrasound Images

TL;DR

CLIP-TNseg introduces a multi-modal hybrid framework combining semantic and fine-grained features for improved thyroid nodule segmentation in ultrasound images, enhancing accuracy and robustness.

Contribution

It presents a novel multi-modal framework integrating CLIP-based semantic features with U-Net style residuals for thyroid nodule segmentation.

Findings

Achieves competitive segmentation accuracy on public and new datasets.

Effectively combines high-level semantic and spatial features.

Demonstrates robustness and interpretability improvements.

Abstract

Thyroid nodule segmentation in ultrasound images is crucial for accurate diagnosis and treatment planning. However, existing methods face challenges in segmentation accuracy, interpretability, and generalization, which hinder their performance. This letter proposes a novel framework, CLIP-TNseg, to address these issues by integrating a multimodal large model with a neural network architecture. CLIP-TNseg consists of two main branches: the Coarse-grained Branch, which extracts high-level semantic features from a frozen CLIP model, and the Fine-grained Branch, which captures fine-grained features using U-Net style residual blocks. These features are fused and processed by the prediction head to generate precise segmentation maps. CLIP-TNseg leverages the Coarse-grained Branch to enhance semantic understanding through textual and high-level visual features, while the Fine-grained Branch refines spatial details, enabling precise and robust segmentation. Extensive experiments on public and our newly collected datasets demonstrate its competitive performance. Our code and the original dataset are available at https://github.com/jayxjsun/CLIP-TNseg.