组织伪影分割与严重度分析用于全切片图像的自动诊断
Tissue Artifact Segmentation and Severity Analysis for Automated Diagnosis Using Whole Slide Images
AI总结：
本文提出一种结合DoubleUNet分割与六模型集成网络评估组织伪影严重度的系统，在分割准确率上提升9%，严重度评估与病理学家相关性达97%，并集成于自动分析系统。
摘要：
传统上，病理分析和诊断由专家通过显微镜手动观察玻璃切片标本完成。全切片图像是由玻璃切片生成的数字标本。全切片图像使得标本可以在计算机屏幕上观察，并催生了计算病理学，其中利用计算机视觉和人工智能进行自动分析和诊断。随着当前计算能力的进步，整个全切片图像可以在无需人工监督的情况下自主分析。然而，如果全切片图像受到组织伪影（如组织折叠或气泡）的影响，根据严重程度，分析可能会失败或导致错误诊断。现有的伪影检测方法依赖专家进行严重度评估，以从分析中排除受伪影影响的区域。这一过程耗时、费力，且削弱了自动分析的目标，或在不评估严重度的情况下移除伪影，可能导致诊断重要数据的丢失。因此，有必要检测伪影并自动评估其严重度。在本文中，我们提出了一种利用卷积神经网络将严重度评估与伪影检测相结合的系统。所提出的系统使用DoubleUNet分割伪影，并使用由六个微调卷积神经网络模型组成的集成网络来确定严重度。该方法在伪影分割的准确率上比当前最先进技术提高了9%，并在严重度评估中与病理学家的评估达到了97%的强相关性。系统的鲁棒性通过我们提出的异构数据集得到验证，并通过与自动分析系统的集成确保了其实用性。
英文摘要：
Traditionally, pathological analysis and diagnosis are performed by manually eyeballing glass slide specimens under a microscope by an expert. The whole slide image is the digital specimen produced from the glass slide. Whole slide image enabled specimens to be observed on a computer screen and led to computational pathology where computer vision and artificial intelligence are utilized for automated analysis and diagnosis. With the current computational advancement, the entire whole slide image can be analyzed autonomously without human supervision. However, the analysis could fail or lead to wrong diagnosis if the whole slide image is affected by tissue artifacts such as tissue fold or air bubbles depending on the severity. Existing artifact detection methods rely on experts for severity assessment to eliminate artifact affected regions from the analysis. This process is time consuming, exhausting and undermines the goal of automated analysis or removal of artifacts without evaluating their severity, which could result in the loss of diagnostically important data. Therefore, it is necessary to detect artifacts and then assess their severity automatically. In this paper, we propose a system that incorporates severity evaluation with artifact detection utilizing convolutional neural networks. The proposed system uses DoubleUNet to segment artifacts and an ensemble network of six fine tuned convolutional neural network models to determine severity. This method outperformed current state of the art in accuracy by 9 percent for artifact segmentation and achieved a strong correlation of 97 percent with the evaluation of pathologists for severity assessment. The robustness of the system was demonstrated using our proposed heterogeneous dataset and practical usability was ensured by integrating it with an automated analysis system.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
