Zero-shot performance of the Segment Anything Model (SAM) in 2D medical imaging: A comprehensive evaluation and practical guidelines

• URL: http://arxiv.org/abs/2305.00109v2
• Date: Fri, 5 May 2023 19:02:03 GMT
• Title: Zero-shot performance of the Segment Anything Model (SAM) in 2D medical imaging: A comprehensive evaluation and practical guidelines
• Authors: Christian Mattjie and Luis Vinicius de Moura and Rafaela Cappelari Ravazio and Lucas Silveira Kupssinsk\"u and Ot\'avio Parraga and Marcelo Mussi Delucis and Rodrigo Coelho Barros
• Abstract summary: Segment Anything Model (SAM) harnesses a massive training dataset to segment nearly any object. Our findings reveal that SAM's zero-shot performance is not only comparable, but in certain cases, surpasses the current state-of-the-art. We propose practical guidelines that require minimal interaction while consistently yielding robust outcomes.
• Score: 0.13854111346209866
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Segmentation in medical imaging is a critical component for the diagnosis, monitoring, and treatment of various diseases and medical conditions. Presently, the medical segmentation landscape is dominated by numerous specialized deep learning models, each fine-tuned for specific segmentation tasks and image modalities. The recently-introduced Segment Anything Model (SAM) employs the ViT neural architecture and harnesses a massive training dataset to segment nearly any object; however, its suitability to the medical domain has not yet been investigated. In this study, we explore the zero-shot performance of SAM in medical imaging by implementing eight distinct prompt strategies across six datasets from four imaging modalities, including X-ray, ultrasound, dermatoscopy, and colonoscopy. Our findings reveal that SAM's zero-shot performance is not only comparable to, but in certain cases, surpasses the current state-of-the-art. Based on these results, we propose practical guidelines that require minimal interaction while consistently yielding robust outcomes across all assessed contexts. The source code, along with a demonstration of the recommended guidelines, can be accessed at https://github.com/Malta-Lab/SAM-zero-shot-in-Medical-Imaging.

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