Abstract
In this paper, we present PRISM, a Promptable and Robust Interactive Segmentation Model, aiming for precise segmentation of 3D medical images. PRISM accepts various visual inputs, including points, boxes, and scribbles as sparse prompts, as well as masks as dense prompts. Specifically, PRISM is designed with four principles to achieve robustness: (1) Iterative learning. The model produces segmentations by using visual prompts from previous iterations to achieve progressive improvement. (2) Confidence learning. PRISM employs multiple segmentation heads per input image, each generating a continuous map and a confidence score to optimize predictions. (3) Corrective learning. Following each segmentation iteration, PRISM employs a shallow corrective refinement network to reassign mislabeled voxels. (4) Hybrid design. PRISM integrates hybrid encoders to better capture both the local and global information. Comprehensive validation of PRISM is conducted using four public datasets for tumor segmentation in the colon, pancreas, liver, and kidney, highlighting challenges caused by anatomical variations and ambiguous boundaries in accurate tumor identification. Compared to state-of-the-art methods, both with and without prompt engineering, PRISM significantly improves performance, achieving results that are close to human levels. The code is publicly available at this https URL.
Abstract (translated)
在本文中，我们提出了PRISM，一种可编程和鲁棒的分割模型，旨在实现对3D医疗图像的准确分割。PRISM接受各种视觉输入，包括点、盒子和涂鸦作为稀疏提示，以及掩码作为稠密提示。具体来说，PRISM 设计了四个原则来实现鲁棒性：（1）迭代学习。模型通过使用前一次迭代中的视觉提示来产生分割，实现逐步改进。（2）信心学习。PRISM 对每个输入图像采用多个分割头，每个头产生一个连续的地图和信心分数，以优化预测。（3）纠正学习。在每个分割迭代后，PRISM 使用浅层纠正修复网络重新分配错误标注的体积。（4）混合设计。PRISM 整合了混合编码器，更好地捕捉局部的和全局信息。通过对大肠癌、胰腺、肝脏和肾脏等四个公开数据集进行全面的分割验证，PRISM 的性能得到了显著提高，达到接近人类水平。PRISM 的代码公开可用，https:// this URL。
URL
https://arxiv.org/abs/2404.15028
