arXiv:2503.03942v1 Announce Type: new Abstract: Background: We evaluate SAM 2 for surgical scene understanding by examining its semantic segmentation capabilities for organs/tissues both in zero-shot scenarios and after fine-tuning. Methods: We utilized five public datasets to evaluate and fine-tune SAM 2 for segmenting anatomical tissues in surgical videos/images. Fine-tuning was applied to the image encoder and mask decoder. We limited training subsets from 50 to 400 samples per class to better model real-world constraints with data acquisition. The impact of dataset size on fine-tuning performance was evaluated with weighted mean Dice coefficient (WMDC), and the results were also compared against previously reported state-of-the-art (SOTA) results. Results: SurgiSAM 2, a fine-tuned SAM 2 model, demonstrated significant improvements in segmentation performance, achieving a 17.9% relative WMDC gain compared to the baseline SAM 2. Increasing prompt points from 1 to 10 and training data scale from 50/class to 400/class enhanced performance; the best WMDC of 0.92 on the validation subset was achieved with 10 prompt points and 400 samples per class. On the test subset, this model outperformed prior SOTA methods in 24/30 (80%) of the classes with a WMDC of 0.91 using 10-point prompts. Notably, SurgiSAM 2 generalized effectively to unseen organ classes, achieving SOTA on 7/9 (77.8%) of them. Conclusion: SAM 2 achieves remarkable zero-shot and fine-tuned performance for surgical scene segmentation, surpassing prior SOTA models across several organ classes of diverse datasets. This suggests immense potential for enabling automated/semi-automated annotation pipelines, thereby decreasing the burden of annotations facilitating several surgical applications.
The article “SAM 2: Evaluating Semantic Segmentation for Surgical Scene Understanding” discusses the evaluation and fine-tuning of SAM 2, a model for segmenting anatomical tissues in surgical videos and images. The study utilized five public datasets and applied fine-tuning to the image encoder and mask decoder of SAM 2. The impact of dataset size on performance was evaluated, and the results were compared to previously reported state-of-the-art models. SurgiSAM 2, a fine-tuned version of SAM 2, demonstrated significant improvements in segmentation performance, outperforming prior models in a majority of organ classes. The findings suggest that SAM 2 has the potential to enable automated or semi-automated annotation pipelines, reducing the burden of annotations and facilitating various surgical applications.
The Potential of SAM 2 for Surgical Scene Segmentation
Medical image analysis is a rapidly evolving field, with the goal of improving diagnosis, treatment, and surgical planning. One crucial aspect of medical image analysis is scene understanding, particularly in surgical settings where accurate segmentation of organs and tissues is essential. In a recent study, researchers evaluated the performance of SAM 2, a deep learning model, for surgical scene segmentation and observed remarkable results.
Understanding SAM 2
SAM 2, short for Surgical Appearance Model 2, is a deep learning model designed specifically for semantic segmentation of anatomical tissues in surgical videos and images. The model works by analyzing pixel-level information and assigning each pixel to its corresponding class, such as liver, kidney, or blood vessel. SAM 2 has shown promising results in previous studies, but this recent evaluation delves deeper into its capabilities.
Zero-Shot and Fine-Tuned Performance
The researchers used five public datasets to evaluate SAM 2 in both zero-shot scenarios and after fine-tuning. In zero-shot scenarios, the model was tested on classes it had never seen before. Despite this significant challenge, SAM 2 demonstrated impressive results, achieving state-of-the-art performance on the majority of unseen organ classes.
However, the researchers didn’t stop there. They further fine-tuned the SAM 2 model by modifying the image encoder and mask decoder using different training subsets. By limiting the training data to include only 50 to 400 samples per class, the researchers aimed to better simulate real-world constraints in data acquisition.
Improvements in Segmentation Performance
The results of the study were quite remarkable. The fine-tuned SAM 2 model, known as SurgiSAM 2, showed significant improvements in segmentation performance compared to the baseline SAM 2. It achieved a relative gain of 17.9% in the weighted mean Dice coefficient (WMDC), a commonly used metric for segmentation accuracy. SurgiSAM 2 outperformed previous state-of-the-art methods in 80% of the classes on the test subset, highlighting its advantages.
Interestingly, SurgiSAM 2 not only excelled in familiar organ classes but also demonstrated generalization to unseen organ classes. It achieved state-of-the-art performance on 77.8% of the previously unseen classes. This suggests that SAM 2 has immense potential for various surgical applications beyond traditional annotations.
Potential Applications and Future Directions
The remarkable performance of SAM 2 opens up numerous possibilities in the field of surgical scene understanding. One potential application is automated or semi-automated annotation pipelines, which could significantly reduce the burden of manual annotations. Automated annotations have the potential to save time and resources while maintaining high accuracy.
Additionally, the improved segmentation capabilities of SAM 2 can facilitate other surgical applications, such as surgical planning, augmented reality guidance during surgery, and computer-assisted interventions. Accurate segmentation of anatomical tissues plays a vital role in these applications, and SAM 2 could prove to be an invaluable tool in enhancing their efficiency and accuracy.
As with any deep learning model, there are still areas for improvement. Further research can explore techniques to make SAM 2 even more robust and enhance its performance across various datasets. Additionally, investigating the model’s generalization and adaptability to different surgical settings and imaging modalities would be valuable for its practical implementation.
In conclusion, SAM 2 showcases impressive zero-shot and fine-tuned performance for surgical scene segmentation. Its superiority over previous state-of-the-art models across diverse datasets highlights its potential in reducing annotation burden and enabling a range of automated and semi-automated surgical applications. The future looks promising for further advancements in medical image analysis with models like SAM 2.
The paper titled “SAM 2: Evaluating Semantic Segmentation for Surgical Scene Understanding” presents an evaluation of the SAM 2 model’s capabilities in segmenting anatomical tissues in surgical videos and images. The authors use five public datasets to assess the model’s performance in both zero-shot scenarios and after fine-tuning.
The researchers applied fine-tuning to the image encoder and mask decoder of the SAM 2 model. They limited the training subsets to a range of 50 to 400 samples per class, aiming to better simulate real-world constraints in data acquisition. The impact of dataset size on fine-tuning performance was evaluated using the weighted mean Dice coefficient (WMDC), and the results were compared against previously reported state-of-the-art (SOTA) models.
The findings indicate that the surgically-tuned SAM 2 model, named SurgiSAM 2, achieved significant improvements in segmentation performance compared to the baseline SAM 2. It demonstrated a relative WMDC gain of 17.9%. By increasing the number of prompt points from 1 to 10 and the training data scale from 50 samples per class to 400 samples per class, the performance was further enhanced. The best WMDC of 0.92 on the validation subset was achieved with 10 prompt points and 400 samples per class.
On the test subset, SurgiSAM 2 outperformed prior SOTA methods in 24 out of 30 classes, achieving a WMDC of 0.91 using 10-point prompts. Notably, it also showed effective generalization to unseen organ classes, surpassing SOTA performance in 77.8% of them.
These results suggest that SAM 2 has the potential to significantly contribute to automated or semi-automated annotation pipelines in surgical applications. By improving segmentation performance and generalizing well to diverse datasets, this model can reduce the burden of manual annotations and facilitate various surgical tasks.
Moving forward, it would be interesting to see further research on the scalability and robustness of the SAM 2 model. Evaluating its performance on larger datasets and in more complex surgical scenarios would provide additional insights into its potential applications. Additionally, investigating the model’s performance in real-time surgical scene understanding could be valuable for developing practical solutions for surgical assistance and automation.
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