SimTxtSeg: Weakly-Supervised Medical Image Segmentation Using Simple Text Cues

Alapfogalmak

A novel weakly-supervised medical image segmentation framework, SimTxtSeg, that leverages simple text cues to generate high-quality pseudo-labels and integrates text and image features to enhance segmentation performance.

Kivonat

The paper proposes a weakly-supervised medical image segmentation framework called SimTxtSeg, which consists of two key components:

1. Textual-to-Visual Cue Converter (TVCC):

   • Converts simple text cues into visual prompts that can be used to generate pseudo-masks using the Segment Anything Model (SAM).
   • Eliminates the need for expensive pixel-level annotations from experts.

2. Text-Vision Hybrid Attention (TVHA):

   • Integrates text cues into the target segmentation model to enhance the effect of language-driven segmentation performance.
   • Includes a dual-way cross-modal attention and a channel attention module to effectively fuse text and image features.

The authors evaluate their framework on two medical image segmentation tasks: colonic polyp segmentation and MRI brain tumor segmentation. They achieve consistent state-of-the-art performance compared to other weakly-supervised methods, surpassing even the pseudo-mask quality generated by TVCC and SAM.

The key findings include:

• Using simple text cues, the proposed approach achieves state-of-the-art performance with minimal supervision.
• The generated pseudo-masks are on par with fully-supervised models, and the final segmentation model outperforms other weakly-supervised methods.
• The TVHA module significantly boosts the segmentation performance, with a +5.09% increase in mDice and a +7.55% increase in mIoU on the polyp dataset.
• The text cue granularity (individual words vs. descriptive sentences) and the choice of SAM variant (SAM-huge, SAM-base, SAM-Med2d-base) both impact the pseudo-mask quality.

Statisztikák

The colonic polyp dataset contains 3,784 images, with 3,190 for training, 299 for validation, and 295 for testing.
The MRI brain tumor dataset contains 3,929 brain MRI images, with the same data split as the polyp dataset.

Idézetek

"Using simple text cues, our approach achieves state-of-the-art performance with minimal supervision."
"The generated pseudo-masks are on par with fully-supervised models, and the final segmentation model outperforms other weakly-supervised methods."
"The TVHA module significantly boosts the segmentation performance, with a +5.09% increase in mDice and a +7.55% increase in mIoU on the polyp dataset."

Főbb Kivonatok

SimTxtSeg: Weakly-Supervised Medical Image Segmentation with Simple Text Cues

by Yuxin Xie, T... : arxiv.org 09-26-2024

https://arxiv.org/pdf/2406.19364.pdf

Mélyebb kérdések

How can the proposed framework be extended to other medical imaging modalities beyond CT and MRI, such as ultrasound or histopathology images?

The proposed framework, SimTxtSeg, can be extended to other medical imaging modalities such as ultrasound and histopathology images by adapting the Textual-to-Visual Cue Converter and the Text-Vision Hybrid Attention mechanism to accommodate the unique characteristics of these modalities.

Modality-Specific Pre-training: For ultrasound images, which often have different noise characteristics and image artifacts compared to CT and MRI, the framework can be pre-trained on a large dataset of ultrasound images to learn modality-specific features. Similarly, for histopathology images, the model can be fine-tuned on datasets that include annotated tissue samples to capture the intricate details necessary for segmentation.

Textual Cues Adaptation: The text prompts used in the framework can be tailored to reflect the specific terminology and descriptions relevant to ultrasound and histopathology. For instance, prompts for ultrasound might focus on anatomical structures visible in the images, while histopathology prompts could describe cellular characteristics or tissue types.

Feature Extraction Adjustments: The feature extraction components of the framework, such as the vision encoder, may need to be modified to handle the different resolutions and color channels of ultrasound and histopathology images. For example, histopathology images are often stained and may require color normalization techniques to ensure consistent feature extraction.

Integration of Additional Modalities: The framework can also incorporate multi-modal data by integrating ultrasound or histopathology images with other imaging modalities. This could enhance the segmentation performance by providing complementary information, leveraging the strengths of each modality.

Evaluation and Validation: Finally, extensive validation on datasets specific to ultrasound and histopathology is crucial to ensure that the adapted framework maintains high segmentation accuracy and generalizability across different medical imaging tasks.

What are the potential limitations of using text cues for weakly-supervised medical image segmentation, and how can they be addressed?

While using text cues for weakly-supervised medical image segmentation presents several advantages, there are notable limitations that need to be addressed:

Ambiguity in Text Prompts: Text cues can be vague or ambiguous, leading to misinterpretation by the model. For instance, a prompt like "tumor" may not specify the type or location, resulting in inaccurate segmentation. To mitigate this, more detailed and context-specific prompts can be generated using advanced natural language processing techniques, ensuring clarity and specificity.

Dependence on Quality of Textual Descriptions: The effectiveness of the framework heavily relies on the quality of the textual descriptions provided. If the descriptions are poorly constructed or lack relevant details, the resulting pseudo-labels may be suboptimal. This can be addressed by employing pre-trained language models fine-tuned on medical texts to generate high-quality, contextually relevant descriptions.

Limited Semantic Information: Text cues may not capture the full complexity of the visual information present in medical images. To enhance the semantic richness, the framework could incorporate additional modalities, such as combining text with visual features extracted from the images, thereby providing a more comprehensive understanding of the segmentation task.

Generalizability Across Different Conditions: The model may struggle to generalize across different patient populations or imaging conditions if the text cues are not representative of the diversity in the data. This can be addressed by training the model on a diverse dataset that includes a wide range of conditions and demographics, ensuring that the text prompts cover various scenarios.

Evaluation Metrics: The evaluation of segmentation performance based on text cues may not always align with clinical relevance. Therefore, incorporating expert feedback and clinical validation into the evaluation process can help ensure that the segmentation results are clinically meaningful and applicable.

Can the textual-to-visual cue converter be further improved by incorporating domain-specific medical knowledge or pre-trained language models fine-tuned on medical text?

Yes, the textual-to-visual cue converter can be significantly improved by incorporating domain-specific medical knowledge and utilizing pre-trained language models fine-tuned on medical text. Here are several ways to achieve this:

Integration of Medical Ontologies: By integrating medical ontologies and knowledge bases, the converter can leverage structured medical knowledge to enhance the understanding of text prompts. This can help in generating more accurate visual cues by ensuring that the model comprehends the relationships between different medical terms and concepts.

Fine-tuning on Medical Corpora: Utilizing pre-trained language models, such as BERT or GPT, that have been fine-tuned on medical literature can improve the converter's ability to generate contextually relevant and precise text prompts. This fine-tuning process allows the model to better understand medical terminology and the nuances of language used in clinical settings.

Contextual Embeddings: By employing contextual embeddings that capture the meaning of words based on their usage in medical contexts, the converter can generate more accurate visual prompts. This can be particularly beneficial in distinguishing between similar terms that may have different implications in a medical context.

Feedback Mechanisms: Implementing feedback mechanisms where medical professionals can review and refine the generated text prompts can enhance the quality of the input to the converter. This iterative process can help in continuously improving the accuracy and relevance of the textual cues.

Multi-Modal Learning: Combining textual information with visual features through multi-modal learning approaches can enhance the converter's performance. By training the model to understand how text and images relate to each other, it can generate more effective visual cues that align closely with the intended segmentation tasks.

In summary, incorporating domain-specific medical knowledge and fine-tuning language models on medical texts can greatly enhance the performance of the textual-to-visual cue converter, leading to improved segmentation outcomes in weakly-supervised medical image segmentation tasks.