The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: Results of the KiTS19 challenge.

There is a large body of literature linking anatomic and geometric characteristics of kidney tumors to perioperative and oncologic outcomes. Semantic segmentation of these tumors and their host kidneys is a promising tool for quantitatively characterizing these lesions, but its adoption is limited due to the manual effort required to produce high-quality 3D segmentations of these structures. Recently, methods based on deep learning have shown excellent results in automatic 3D segmentation, but they require large datasets for training, and there remains little consensus on which methods perform best. The 2019 Kidney and Kidney Tumor Segmentation challenge (KiTS19) was a competition held in conjunction with the 2019 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) which sought to address these issues and stimulate progress on this automatic segmentation problem. A training set of 210 cross sectional CT images with kidney tumors was publicly released with corresponding semantic segmentation masks. 106 teams from five continents used this data to develop automated systems to predict the true segmentation masks on a test set of 90 CT images for which the corresponding ground truth segmentations were kept private. These predictions were scored and ranked according to their average Sørensen-Dice coefficient between the kidney and tumor across all 90 cases. The winning team achieved a Dice of 0.974 for kidney and 0.851 for tumor, approaching the inter-annotator performance on kidney (0.983) but falling short on tumor (0.923). This challenge has now entered an "open leaderboard" phase where it serves as a challenging benchmark in 3D semantic segmentation.

[Multi-scale 3D convolutional neural network-based segmentation of head and neck organs at risk].

OBJECTIVE: To establish an algorithm based on 3D convolution neural network to segment the organs at risk (OARs) in the head and neck on CT images. METHODS: We propose an automatic segmentation algorithm of head and neck OARs based on V-Net. To enhance the feature expression ability of the 3D neural network, we combined the squeeze and exception (SE) module with the residual convolution module in V-Net to increase the weight of the features that has greater contributions to the segmentation task. Using a multi-scale strategy, we completed organ segmentation using two cascade models for location and fine segmentation, and the input image was resampled to different resolutions during preprocessing to allow the two models to focus on the extraction of global location information and local detail features respectively. RESULTS: Our experiments on segmentation of 22 OARs in the head and neck indicated that compared with the existing methods, the proposed method achieved better segmentation accuracy and efficiency, and the average segmentation accuracy was improved by 9%. At the same time, the average test time was reduced from 33.82 s to 2.79 s. CONCLUSIONS: The 3D convolution neural network based on multi-scale strategy can effectively and efficiently improve the accuracy of organ segmentation and can be potentially used in clinical setting for segmentation of other organs to improve the efficiency of clinical treatment.

9,19-Cycloartenol glycoside G3 from Cimicifuga simplex regulates immune responses by modulating Th17/Treg ratio.

Cimicifuga simplex is a medicinal herb which has a wide range of biological activities. We isolated seven 9,19-cycloartenol glycosides from the roots of C. simplex, and among the glycosides, G3 exhibited the strongest inhibitory effect on immune responses, including suppressing the differentiation of CD4+ T cells and directly suppressing the cytokine-induced JAK/STAT signaling pathways. In the IL-23-induced mouse ear model of skin disease, G3 repressed disease development by inhibiting the expression of pro-inflammatory mediators in murine ear skin. Moreover, G3 affected the maturation of DCs in vitro, thereby inducing T cell differentiation, resulting in an increased Treg phenotype and decreased Th17 phenotype. This study provides new evidence that G3 might ameliorate chronic inflammatory skin diseases by suppressing pathogenic CD4+ T cell differentiation and the IL-17+RORγt+/IL-10+FoxP3+ ratio. These findings suggest that G3 might mediate the therapeutic effects observed in psoriasis patients following treatment with C. simplex.