UTC can quantitatively evaluate tendon structure and thereby discriminate symptomatic and asymptomatic tendons. As such, UTC might be useful to monitor treatment protocols.

Stability of echo patterns accurately reflects homogeneity of tendons in horses.

Computerized ultrasonography provided an excellent tool for objective monitoring of healing tendons in horses and reliable prognostication of repair quality.

Objects are often organized in a semantic hierarchy of categories, where finelevel categories are grouped into coarse-level categories according to their semantic relations. While previous works usually only classify objects into the leaf categories, we argue that generating hierarchical labels can actually describe how the leaf categories evolved from higher level coarse-grained categories, thus can provide a better understanding of the objects. In this paper, we propose to utilize the CNN-RNN framework to address the hierarchical image classification task. CNN allows us to obtain discriminative features for the input images, and RNN enables us to jointly optimize the classification of coarse and fine labels. This framework can not only generate hierarchical labels for images, but also improve the traditional leaf-level classification performance due to incorporating the hierarchical information. Moreover, this framework can be built on top of any CNN architecture which is primarily designed for leaf-level classification. Accordingly, we build a high performance network based on the CNN-RNN paradigm which outperforms the original CNN (wider-ResNet) and also the current state-of-the-art. In addition, we investigate how to utilize the CNN-RNN framework to improve the fine category classification when a fraction of the training data is only annotated with coarse labels. Experimental results demonstrate that CNN-RNN can use the coarse-labeled training data to improve the classification of fine categories, and in some cases it even surpasses the performance achieved by fully annotated training data. This reveals that, CNN-RNN can alleviate the challenge of specialized and expensive annotation of fine labels.

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Although quantification of the transverse ultrasonographic image by use of first-order gray level statistics may be helpful, the method is not sufficiently sensitive to accurately and unequivocally determine the type of tendon tissue. Quantitative analysis should incorporate transverse and longitudinal information.

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