In this paper, multiple instance learning (MIL) algorithms to automatically perform root detection and segmentation in minirhizotron imagery using only image-level labels are proposed. Root and soil characteristics vary from location to location, and thus, supervised machine learning approaches that are trained with local data provide the best ability to identify and segment roots in minirhizotron imagery. However, labeling roots for training data (or otherwise) is an extremely tedious and time-consuming task. This paper aims to address this problem by labeling data at the image level (rather than the individual root or root pixel level) and train algorithms to perform individual root pixel level segmentation using MIL strategies. Three MIL methods (multiple instance adaptive cosine coherence estimator, multiple instance support vector machine, multiple instance learning with randomized trees) were applied to root detection and compared to non-MIL approaches. The results show that MIL methods improve root segmentation in challenging minirhizotron imagery and reduce the labeling burden. In our results, multiple instance support vector machine outperformed other methods. The multiple instance adaptive cosine coherence estimator algorithm was a close second with an added advantage that it learned an interpretable root signature which identified the traits used to distinguish roots from soil and did not require parameter selection.

中文翻译：

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带有多个实例学习的小根部放映图像中的根识别

本文提出了多实例学习（MIL）算法，该算法仅使用图像级标签就可以在微型根管电子成像中自动执行根检测和分割。根与土壤的特性因位置而异，因此，受监督的机器学习方法（使用本地数据进行训练）可提供最佳的能力，以识别和分割微型根际放映机中的根。但是，为训练数据（或其他方式）标记根是一项非常繁琐且耗时的任务。本文旨在通过在图像级别（而不是单个根或根像素级别）标记数据来解决此问题，并训练算法以使用MIL策略执行单个根像素级别分割。三种MIL方法（多实例自适应余弦相干估计器，多实例支持向量机，将带有随机树的多实例学习应用于根检测，并与非MIL方法进行比较。结果表明，MIL方法可改善具有挑战性的微根管成像中的根分割，并减少标记负担。在我们的结果中，多实例支持向量机的性能优于其他方法。多实例自适应余弦相干估计器算法紧随其后，具有一个额外的优势，即它学会了可解释的根签名，该签名可识别用于区分根与土壤的性状，并且不需要参数选择。多实例支持向量机的性能优于其他方法。多实例自适应余弦相干估计器算法紧随其后，具有一个额外的优势，即它学会了可解释的根签名，该签名可识别用于区分根与土壤的性状，并且不需要参数选择。多实例支持向量机的性能优于其他方法。多实例自适应余弦相干估计器算法紧随其后，具有一个额外的优势，即它学会了可解释的根签名，该签名可识别用于区分根与土壤的性状，并且不需要参数选择。