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Closure of an epithelium opening is a critical morphogenetic event for development. An excellent example for this process is the transient closure of embryonic eyelid. Eyelid closure requires shape change and migration of epithelial cells at the tip of the developing eyelids, and is dictated by numerous signaling pathways. Here we evaluated gene expression in epithelial cells isolated from the tip (leading edge, LE) and inner surface epithelium (IE) of the eyelid from E15.5 mouse fetuses by laser capture microdissection (LCM). We showed that the LE and IE cells are different at E15.5, such that IE had higher expression of muscle specific genes, while LE acquired epithelium identities. Despite their distinct destinies, these cells were overall similar in expression of signaling components for the "eyelid closure pathways". However, while the LE cells had more abundant expression of Fgfr2, Erbb2, Shh, Ptch1 and 2, Smo and Gli2, and Jag1 and Notch1, the IE cells had more abundant expression of Bmp5 and Bmpr1a. In addition, the LE cells had more abundant expression of adenomatosis polyposis coli down-regulated 1 (Apcdd1), but the IE cells had high expression of Dkk2. Our results suggest that the functionally distinct LE and IE cells have also differential expression of signaling molecules that may contribute to the cell-specific responses to morphogenetic signals. The expression pattern suggests that the EGF, Shh and NOTCH pathways are preferentially active in LE cells, the BMP pathways are effective in IE cells, and the Wnt pathway may be repressed in LE and IE cells via different mechanisms.

Recently, convolutional neural networks (CNNs) have shown significant advantages in the tasks of image classification; however, these usually require a large number of labeled samples for training. In practice, it is difficult and costly to obtain sufficient labeled samples of polarimetric synthetic aperture radar (PolSAR) images. To address this problem, we propose a novel semi-supervised classification method for PolSAR images in this paper, using the co-training of CNN and a support vector machine (SVM). In our co-training method, an eight-layer CNN with residual network (ResNet) architecture is designed as the primary classifier, and an SVM is used as the auxiliary classifier. In particular, the SVM is used to enhance the performance of our algorithm in the case of limited labeled samples. In our method, more and more pseudo-labeled samples are iteratively yielded for training through a two-stage co-training of CNN and SVM, which gradually improves the performance of the two classifiers. The trained CNN is employed as the final classifier due to its strong classification capability with enough samples. We carried out experiments on two C-band airborne PolSAR images acquired by the AIRSAR systems and an L-band spaceborne PolSAR image acquired by the GaoFen-3 system. The experimental results demonstrate that the proposed method can effectively integrate the complementary advantages of SVM and CNN, providing overall classification accuracy of more than 97%, 96% and 93% with limited labeled samples (10 samples per class) for the above three images, respectively, which is superior to the state-of-the-art semi-supervised methods for PolSAR image classification.

Distance measure plays a critical role in various applications of polarimetric synthetic aperture radar (PolSAR) image data. In recent decades, plenty of distance measures have been developed for PolSAR image data from different perspectives, which, however, have not been well analyzed and summarized. In order to make better use of these distance measures in algorithm design, this paper provides a systematic survey of them and analyzes their relations in detail. We divide these distance measures into five main categories (i.e., the norm distances, geodesic distances, maximum likelihood (ML) distances, generalized likelihood ratio test (GLRT) distances, stochastics distances) and two other categories (i.e., the inter-patch distances and those based on metric learning). Furthermore, we analyze the relations between different distance measures and visualize them with graphs to make them clearer. Moreover, some properties of the main distance measures are discussed, and some advice for choosing distances in algorithm design is also provided. This survey can serve as a reference for researchers in PolSAR image processing, analysis, and related fields.