TV-SAM: Increasing Zero-Shot Segmentation Performance on Multimodal Medical Images Using GPT-4 Generated Descriptive Prompts Without Human Annotation

Zekun Jiang; Dongjie Cheng; Ziyuan Qin; Jun Gao; Qicheng Lao; Abdullaev Bakhrom Ismoilovich; Urazboev Gayrat; Yuldashov Elyorbek; Bekchanov Habibullo; Defu Tang; LinJing Wei; Kang Li; Le Zhang

arXiv · 2402.15759

TV-SAM: Increasing Zero-Shot Segmentation Performance on Multimodal Medical Images Using GPT-4 Generated Descriptive Prompts Without Human Annotation

Zekun Jiang·Dongjie Cheng·Ziyuan Qin·Jun Gao·Qicheng Lao·Abdullaev Bakhrom Ismoilovich·Urazboev Gayrat·Yuldashov Elyorbek·Bekchanov Habibullo·Defu Tang·LinJing Wei·Kang Li·Le Zhang

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Abstract

This study presents a novel multimodal medical image zero-shot segmentation algorithm named the text-visual-prompt segment anything model (TV-SAM) without any manual annotations. The TV-SAM incorporates and integrates the large language model GPT-4, the vision language model GLIP, and the SAM to autonomously generate descriptive text prompts and visual bounding box prompts from medical images, thereby enhancing the SAM's capability for zero-shot segmentation. Comprehensive evaluations are implemented on seven public datasets encompassing eight imaging modalities to demonstrate that TV-SAM can effectively segment unseen targets across various modalities without additional training. TV-SAM significantly outperforms SAM AUTO and GSAM, closely matching the performance of SAM BBOX with gold standard bounding box prompts and surpasses the state-of-the-art methods on specific datasets such as ISIC and WBC. The study indicates that TV-SAM serves as an effective multimodal medical image zero-shot segmentation algorithm, highlighting the significant contribution of GPT-4 to zero-shot segmentation. By integrating foundational models such as GPT-4, GLIP, and SAM, the ability to address complex problems in specialized domains can be enhanced.

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Zekun Jiang, Dongjie Cheng, Ziyuan Qin, Jun Gao, Qicheng Lao, Abdullaev Bakhrom Ismoilovich, Urazboev Gayrat, Yuldashov Elyorbek, Bekchanov Habibullo, Defu Tang, LinJing Wei, Kang Li, Le Zhang. 2024-10-14. TV-SAM: Increasing Zero-Shot Segmentation Performance on Multimodal Medical Images Using GPT-4 Generated Descriptive Prompts Without Human Annotation. https://arxiv.org/abs/2402.15759

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