Annotation and labeling of images are some of the biggest challenges in applying deep learning to medical data. Current processes are time and cost-intensive and, therefore, a limiting factor for the wide adoption of the technology. Additionally validating that measured performance improvements are significant is important to select the best model. In this paper, we demonstrate a method for creating segmentations, a necessary part of a data cleaning for ultrasound imaging machine learning pipelines. We propose a four-step method to leverage automatically generated training data and fast human visual checks to improve model accuracy while keeping the time/effort and cost low. We also showcase running experiments multiple times to allow the usage of statistical analysis. Poor quality automated ground truth data and quick visual inspections efficiently train an initial base model, which is refined using a small set of more expensive human-generated ground truth data. The method is demonstrated on a cardiac ultrasound segmentation task, removing background data, including static PHI. Significance is shown by running the experiments multiple times and using the student's t-test on the performance distributions. The initial segmentation accuracy of a simple thresholding algorithm of 92% was improved to 98%. The performance of models trained on complicated algorithms can be matched or beaten by pre-training with the poorer performing algorithms and a small quantity of high-quality data. The introduction of statistic significance analysis for deep learning models helps to validate the performance improvements measured. The method offers a cost-effective and fast approach to achieving high-accuracy models while minimizing the cost and effort of acquiring high-quality training data.

翻译：图像标注与标记是将深度学习应用于医疗数据时面临的最大挑战之一。当前流程耗时且成本高昂，因此成为该技术广泛采用的关键制约因素。此外，验证所测性能改进是否具有统计显著性对于选择最优模型至关重要。本文展示了一种创建分割标签的方法，这是超声成像机器学习管道中数据清洗的必要环节。我们提出四步法：利用自动生成的训练数据与快速人工视觉校验，在保持低时间/人力成本的同时提升模型精度。同时通过重复实验引入统计分析机制。低质量的自动标注真值数据结合快速人工视觉检查可高效训练初始基础模型，再通过少量成本更高的人工标注真值数据进行迭代优化。该方法在心脏超声分割任务中验证，用于去除包括静态PHI在内的背景数据。通过重复实验并采用学生t检验对性能分布进行分析，验证了方法的统计显著性。简单阈值算法的初始分割准确率从92%提升至98%。通过低性能算法预训练结合少量高质量数据，可达到甚至超越复杂算法训练模型的性能。引入深度学习模型的统计显著性分析有助于验证性能改进的可靠性。该方法提供了一种经济高效的快速建模途径，在最小化高质量训练数据获取成本与人力投入的同时实现高精度模型。