Encoding histopathology whole slide images with location-aware graphs for diagnostically relevant regions retrieval.

Content-based histopathological image retrieval (CBHIR) has become popular in recent years in histopathological image analysis. CBHIR systems provide auxiliary diagnosis information for pathologists by searching for and returning regions that are contently similar to the region of interest (ROI) from a pre-established database. It is challenging and yet significant in clinical applications to retrieve diagnostically relevant regions from a database consisting of histopathological whole slide images (WSIs). In this paper, we propose a novel framework for regions retrieval from WSI database based on location-aware graphs and deep hash techniques. Compared to the present CBHIR framework, both structural information and global location information of ROIs in the WSI are preserved by graph convolution and self-attention operations, which makes the retrieval framework more sensitive to regions that are similar in tissue distribution. Moreover, benefited from the graph structure, the proposed framework has good scalability for both the size and shape variation of ROIs. It allows the pathologist to define query regions using free curves according to the appearance of tissue. Thirdly, the retrieval is achieved based on the hash technique, which ensures the framework is efficient and adequate for practical large-scale WSI database. The proposed method was evaluated on an in-house endometrium dataset with 2650 WSIs and the public ACDC-LungHP dataset. The experimental results have demonstrated that the proposed method achieved a mean average precision above 0.667 on the endometrium dataset and above 0.869 on the ACDC-LungHP dataset in the task of irregular region retrieval, which are superior to the state-of-the-art methods. The average retrieval time from a database containing 1855 WSIs is 0.752 ms. The source code is available at https://github.com/zhengyushan/lagenet.

Stain Standardization Capsule for Application-Driven Histopathological Image Normalization.

Color consistency is crucial to developing robust deep learning methods for histopathological image analysis. With the increasing application of digital histopathological slides, the deep learning methods are probably developed based on the data from multiple medical centers. This requirement makes it a challenging task to normalize the color variance of histopathological images from different medical centers. In this paper, we propose a novel color standardization module named stain standardization capsule based on the capsule network and the corresponding dynamic routing algorithm. The proposed module can learn and generate uniform stain separation outputs for histopathological images in various color appearance without the reference to manually selected template images. The proposed module is light and can be jointly trained with the application-driven CNN model. The proposed method was validated on three histopathology datasets and a cytology dataset, and was compared with state-of-the-art methods. The experimental results have demonstrated that the SSC module is effective in improving the performance of histopathological image analysis and has achieved the best performance in the compared methods.

Deep Learning Methods for Lung Cancer Segmentation in Whole-Slide Histopathology Images-The ACDC@LungHP Challenge 2019.

Accurate segmentation of lung cancer in pathology slides is a critical step in improving patient care. We proposed the ACDC@LungHP (Automatic Cancer Detection and Classification in Whole-slide Lung Histopathology) challenge for evaluating different computer-aided diagnosis (CADs) methods on the automatic diagnosis of lung cancer. The ACDC@LungHP 2019 focused on segmentation (pixel-wise detection) of cancer tissue in whole slide imaging (WSI), using an annotated dataset of 150 training images and 50 test images from 200 patients. This paper reviews this challenge and summarizes the top 10 submitted methods for lung cancer segmentation. All methods were evaluated using metrics using the precision, accuracy, sensitivity, specificity, and DICE coefficient (DC). The DC ranged from 0.7354 ±0.1149 to 0.8372 ±0.0858. The DC of the best method was close to the inter-observer agreement (0.8398 ±0.0890). All methods were based on deep learning and categorized into two groups: multi-model method and single model method. In general, multi-model methods were significantly better (p 0.01) than single model methods, with mean DC of 0.7966 and 0.7544, respectively. Deep learning based methods could potentially help pathologists find suspicious regions for further analysis of lung cancer in WSI.

Genome-Wide Identification of Long Noncoding RNAs and Their Responses to Salt Stress in Two Closely Related Poplars.

Long noncoding RNAs (lncRNAs) are involved in various biological regulatory processes, but their roles in plants resistance to salt stress remain largely unknown. To systematically explore the characteristics of lncRNAs and their roles in plant salt responses, we conducted strand-specific RNA-sequencing of four tissue types with salt treatments in two closely related poplars (Populus euphratica and Populus alba var. pyramidalis), and a total of 10,646 and 10,531 lncRNAs were identified, respectively. These lncRNAs showed significantly lower values in terms of length, expression, and expression correction than with mRNA. We further found that about 40% and 60% of these identified lncRNAs responded to salt stress with tissue-specific expression patterns across the two poplars. Furthermore, lncRNAs showed weak evolutionary conservation in sequences and exhibited diverse regulatory styles; in particular, tissue- and species-specific responses to salt stress varied greatly in two poplars, for example, 322 lncRNAs were found highly expressed in P. euphratica but not in P. alba var. pyramidalis and 3,425 lncRNAs were identified to be species-specific in P. euphratica in response to salt stress. Moreover, tissue-specific expression of lncRNAs in two poplars were identified with predicted target genes included Aux/IAA, NAC, MYB, involved in regulating plant growth and the plant stress response. Taken together, the systematic analysis of lncRNAs between sister species enhances our understanding of the characteristics of lncRNAs and their roles in plant growth and salt response.

Coexpression of PalbHLH1 and PalMYB90 Genes From Populus alba Enhances Pathogen Resistance in Poplar by Increasing the Flavonoid Content.

Secondary metabolites of the flavonoid pathway participate in plant defense, and bHLH and MYB transcription factors regulate the synthesis of these metabolites. Here, we define the regulatory mechanisms in response to pathogens. Two transcription factors from Populus alba var. pyramidalis, PalbHLH1 and PalMYB90, were overexpressed together in poplar, and transcriptome analysis revealed differences in response to pathogen infection. The transgenic plants showed elevated levels of several key flavonoid pathway components: total phenols, proanthocyanidins (PAs), and anthocyanins and intermediates quercetin and kaempferol. Furthermore, PalbHLH1 and PalMYB90 overexpression in poplar enhanced antioxidase activities and H2O2 release and also increased resistance to Botrytis cinerea and Dothiorella gregaria infection. Gene expression profile analysis showed most genes involved in the flavonoid biosynthesis pathway or antioxidant response to be upregulated in MYB90/bHLH1-OE poplar, but significant differential expression occurred in response to pathogen infection. Specifically, expression of PalF3H (flavanone 3-hydroxylase), PalDFR (dihydroflavonol 4-seductase), PalANS (anthocyanin synthase), and PalANR (anthocyanin reductase), which function in initial, middle, and final steps of anthocyanin and PA biosynthesis, respectively, was significantly upregulated in D. gregaria-infected MYB90/bHLH1-OE poplar. Our results highlight that PalbHLH1 and PalMYB90 function as transcriptional activators of flavonoid pathway secondary-metabolite synthesis genes, with differential mechanisms in response to bacterial or fungal infection.