Automatic segmentation of stem and leaf components and individual maize plants in field terrestrial LiDAR data using convolutional neural networks

High-throughput maize phenotyping at both organ and plant levels plays a key role in molecular breeding for increasing crop yields. Although the rapid development of light detection and ranging(Li DAR) provides a new way to characterize three-dimensional(3 D) plant structure, there is a need to develop robust algorithms for extracting 3 D phenotypic traits from Li DAR data to assist in gene identification and selection. Accurate 3 D phenotyping in field environments remains challenging, owing to difficulties in segmentation of organs and individual plants in field terrestrial Li DAR data. We describe a two-stage method that combines both convolutional neural networks(CNNs) and morphological characteristics to segment stems and leaves of individual maize plants in field environments. It initially extracts stem points using the Point CNN model and obtains stem instances by fitting 3 D cylinders to the points. It then segments the field Li DAR point cloud into individual plants using local point densities and 3 D morphological structures of maize plants. The method was tested using 40 samples from field observations and showed high accuracy in the segmentation of both organs(F-score =0.8207) and plants(Fscore =0.9909). The effectiveness of terrestrial Li DAR for phenotyping at organ(including leaf area and stem position) and individual plant(including individual height and crown width) levels in field environments was evaluated. The accuracies of derived stem position(position error =0.0141 m), plant height(R^(2)>0.99), crown width(R^(2)>0.90), and leaf area(R^(2)>0.85) allow investigating plant structural and functional phenotypes in a high-throughput way. This CNN-based solution overcomes the major challenges in organ-level phenotypic trait extraction associated with the organ segmentation, and potentially contributes to studies of plant phenomics and precision agriculture.

Investigation of the immune effects of Scutellaria baicalensis on blood leukocytes and selected organs of the chicken＇s lymphatic system

Background： The health of chickens and the welfare of poultry industry are central to the efforts of addressing global food security. Therefore, it is essential to study chicken immunology to maintain and improve its health and to find novel and sustainable solutions. This paper presents a study on investigation of the effect of Scutellaria baicalensis root（SBR） on the immune response of broiler chicken, especially on lymphocytes and heterophils reactivity, regarding their contribution to the development of immunity of the chickens.Methods： The 121-day-old Hubbard Hi-Y male broiler hybrids were randomly assigned to four treatment groups,three SBR supplemented groups（0.5, 1.0, and 1.5% of SBR） and one control group. Each treatment was replicated five times with six birds per replicate pen in a battery brooder. Blood was collected after 3-（rd） and 6-（th）wk of the experiment, and hemoglobin and hematocrit values were determined, as well as total leukocyte count and differential count were performed. Nitroblue tetrazolium test and phagocytosis assay as nonspecific immune parameters and humoral immune responses to the antigenic challenge by sheep red blood cells were performed.Moreover, the ability of peripheral blood lymphocytes to form radial segmentation（RS） of their nuclei was analyzed.Body weight and relative weight of spleen, liver, and bursa of Fabricius were recorded.Results： Results showed that mean heterophile/lymphocyte ratio increased in the SBR groups compared to the control group and the blood of the chickens showed lymphocytic depletion. The results also demonstrated that the relative weight of bursa of Fabricius and spleen in groups fed with SBR significantly decreased compared to the control group. This study also showed that the addition of SBR significantly inhibited the formation of RS of nuclei compared to some cytotoxic substances.Conclusion： We found that SBR supplementation should be carefully evaluated when given to poultry. The excess intake of SBR supplementation may cause immunologic inhibition and may negatively affect the development of immune organs. SBR has inhibited the formation of radial segmentation nuclei showing antimetastatic properties and also the phagocytosis of chicken heterophils.

MultiTrans:Multi-scale feature fusion transformer with transfer learning strategy for multiple organs segmentation of head and neck CT images

Radiotherapy with precise segmentation of head and neck organs at risk(OARs)is one of the important treatment methods for head and neck cancer.In routine clinical practice,OARs are manually segmented by doctors to avoid irreversible adverse reactions caused by radiotherapy,which is time-consuming and laborious.To assist doctors in OARs segmentation,a MultiTrans framework with a multi-scale feature fusion module was proposed in this paper.In the multi-scale feature fusion module,the original image and the feature map of CNN were fused together to form a compound feature map for more complete high-resolution global information.In addition,the global information was also fully utilized in MultiTrans by using the feature map restored from the compound feature map in the skip connection.The multi-scale interactive high-resolution information can make full use of medical image information and obtain features more comprehensively,thus improve the segmentation accuracy.Experiments showed that MultiTrans had an average Dice score coefficient(DSC)of 74.01%in all organs,effectively improved segmentation accuracy.In addition,we proposed a transfer learning strategy for small organs by transferring the weight parameters of organs with a large amount of data to organs with a small amount of data to speed up the convergence of MultiTrans and reduce the demand for data volume in the MultiTrans.With this strategy,the average DSC of small organs was obviously increased,making the segmentation of small organs more accurate.The proposed framework and transfer learning strategy have the potential of assisting doctors in OARs delineation.