Evidential Calibrated Uncertainty-Guided Interactive Segmentation paradigm for Ultrasound Images

• URL: http://arxiv.org/abs/2501.01072v1
• Date: Thu, 02 Jan 2025 05:41:25 GMT
• Title: Evidential Calibrated Uncertainty-Guided Interactive Segmentation paradigm for Ultrasound Images
• Authors: Jiang Shang, Yuanmeng Wu, Xiaoxiang Han, Xi Chen, Qi Zhang,
• Abstract summary: Evidential Uncertainty-Guided Interactive (EUGIS) is an end-to-end interactive segmentation paradigm based on evidential uncertainty estimation for ultrasound image segmentation. Our method can effectively simulate the interactive behavior of well-trained radiologists, enhancing the targeted of sampling while reducing the number of prompts and iterations required.
• Score: 8.010602776500237
• License:
• Abstract: Accurate and robust ultrasound image segmentation is critical for computer-aided diagnostic systems. Nevertheless, the inherent challenges of ultrasound imaging, such as blurry boundaries and speckle noise, often cause traditional segmentation methods to struggle with performance. Despite recent advancements in universal image segmentation, such as the Segment Anything Model, existing interactive segmentation methods still suffer from inefficiency and lack of specialization. These methods rely heavily on extensive accurate manual or random sampling prompts for interaction, necessitating numerous prompts and iterations to reach satisfactory performance. In response to this challenge, we propose the Evidential Uncertainty-Guided Interactive Segmentation (EUGIS), an end-to-end, efficient tiered interactive segmentation paradigm based on evidential uncertainty estimation for ultrasound image segmentation. Specifically, EUGIS harnesses evidence-based uncertainty estimation, grounded in Dempster-Shafer theory and Subjective Logic, to gauge the level of uncertainty in the predictions of model for different regions. By prioritizing sampling the high-uncertainty region, our method can effectively simulate the interactive behavior of well-trained radiologists, enhancing the targeted of sampling while reducing the number of prompts and iterations required.Additionally, we propose a trainable calibration mechanism for uncertainty estimation, which can further optimize the boundary between certainty and uncertainty, thereby enhancing the confidence of uncertainty estimation.

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