CLIP-Driven Universal Model for Organ Segmentation and Tumor Detection (2301.00785v5)

Abstract: An increasing number of public datasets have shown a marked impact on automated organ segmentation and tumor detection. However, due to the small size and partially labeled problem of each dataset, as well as a limited investigation of diverse types of tumors, the resulting models are often limited to segmenting specific organs/tumors and ignore the semantics of anatomical structures, nor can they be extended to novel domains. To address these issues, we propose the CLIP-Driven Universal Model, which incorporates text embedding learned from Contrastive Language-Image Pre-training (CLIP) to segmentation models. This CLIP-based label encoding captures anatomical relationships, enabling the model to learn a structured feature embedding and segment 25 organs and 6 types of tumors. The proposed model is developed from an assembly of 14 datasets, using a total of 3,410 CT scans for training and then evaluated on 6,162 external CT scans from 3 additional datasets. We rank first on the Medical Segmentation Decathlon (MSD) public leaderboard and achieve state-of-the-art results on Beyond The Cranial Vault (BTCV). Additionally, the Universal Model is computationally more efficient (6x faster) compared with dataset-specific models, generalized better to CT scans from varying sites, and shows stronger transfer learning performance on novel tasks.

Summary of "CLIP-Driven Universal Model for Organ Segmentation and Tumor Detection"

The paper presents the CLIP-Driven Universal Model, a novel approach for organ segmentation and tumor detection in medical imaging. This model leverages Contrastive Language-Image Pre-training (CLIP) to enhance segmentation models by utilizing text embeddings to capture anatomical relationships. This integration aids in segmenting 25 organs and detecting 6 types of tumors with higher efficacy.

Dataset Utilization and Methodology

The authors address the common challenges in medical datasets, such as small size, partial labeling, and lack of diversity. They assembled 14 diverse public datasets comprising 3,410 CT scans for training and validated the model on 6,162 external CT scans. The model's performance was tested against state-of-the-art benchmarks, ranking first on the Medical Segmentation Decathlon (MSD) and Beyond The Cranial Vault (BTCV) public leaderboards.

The Universal Model introduces a structured feature embedding via CLIP-based label encoding. This encoding is crucial for handling partial labels, enabling loss computation only for classes with available labels. The model improves computational efficiency, proving to be 6 times faster than dataset-specific models.

Strong Numerical Results

The Universal Model showed significant improvements in segmentation accuracy, achieving high Dice Similarity Coefficient (DSC) scores. For instance, on the MSD tasks, the model consistently outperformed others, particularly in segmenting liver and pancreatic tumors. It demonstrated a robust increase in harmonic mean metrics for tumor detection across multiple datasets, balancing sensitivity and specificity effectively.

Theoretical and Practical Implications

The integration of CLIP embeddings represents a pivotal shift in how anatomical relationships are modeled within segmentation tasks. The Universal Model's capacity for semantically meaningful embeddings addresses the previously unresolved label orthogonality problem.

Practically, the model offers substantial advancements in processing speed and generalizability across different datasets. The model's ability to serve as a pre-training foundation further extends its utility for transferring learned visual representations to varied medical imaging tasks.

Future Developments

The results suggest promising avenues for future research, aiming to refine annotation consistency across diverse and partially labeled datasets. Employing larger, more diversified datasets could further validate the approach. Moreover, exploring alternative prompt templates for CLIP embeddings might enhance its utility in varied medical domains.

In conclusion, the CLIP-Driven Universal Model signifies a significant advancement in the field of organ segmentation and tumor detection, showing robust performance, adaptability, and efficiency. Such innovations are pivotal, bringing us closer to fully automated and accurate diagnostic tools in medical imaging.