Abstract
Inflammatory myopathy (IM) is a kind of heterogeneous disease that relates to disorders of muscle functionalities. The identification of IM subtypes is critical to guide effective patient treatment since each subtype requires distinct therapy. Image analysis of hematoxylin and eosin (H&E)-stained whole-slide specimens of muscle biopsies are considered as a gold standard for effective IM diagnosis. Accurate segmentation of perimysium plays an important role in early diagnosis of many muscle inflammation diseases. However, it remains as a challenging task due to the complex appearance of the perimysium morphology and its ambiguity to the background area. The muscle perimysium also exhibits strong structure spanned in the entire tissue, which makes it difficult for current local patch-based methods to capture this long-range context information. In this book chapter, we propose a novel spatial clockwork recurrent neural network (spatial CW-RNN) to address those issues. Besides perimysium segmentation, we also introduce a fully automatic whole-slide image analysis framework for IM subtype classification using deep convolutional neural networks (DCNNs).
Y. Xie and F. Liu contributed equally to this work.
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Xie, Y., Liu, F., Xing, F., Yang, L. (2019). Deep Learning for Muscle Pathology Image Analysis. In: Lu, L., Wang, X., Carneiro, G., Yang, L. (eds) Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics. Advances in Computer Vision and Pattern Recognition. Springer, Cham. https://doi.org/10.1007/978-3-030-13969-8_2
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