Current plant organ analysis algorithms are mostly based on two-dimensional images. There is an issue of overlapping in plant organs in the 2D space, and only specific species of plants can be well handled in 2D. Therefore, we propose a method for propagating labels of plant organs by using a modified Graph Neural Network on three-dimensional plant point clouds. We first convert the three-dimensional point cloud files of plants into a dataset with a graph data structure, and then input it into an improved GCNII for training. Only a few sampled points of the plant are needed for label propagation, then it can lift the efficiency of plant organ segmentation and classification. The accuracy of label propagation results for maize, sorghum, tomato, and tobacco can reach over 95%. The research is of great significance to reducing the amount of manual labeling work load for 3D plant phenotyping.
Prostatic adenocarcinoma is one of the most commonly occurring cancers among men in the world, and it also the most curable cancer when it is detected early. Multiparametric MRI (mpMRI) combines anatomic and functional prostate imaging techniques, which have been shown to produce high sensitivity and specificity in cancer localization, which is important in planning biopsies and focal therapies. However, in previous investigations, lesion localization was achieved mainly by manual segmentation, which is time-consuming and prone to observer variability. Here, we developed an algorithm based on locality alignment discriminant analysis (LADA) technique, which can be considered as a version of linear discriminant analysis (LDA) localized to patches in the feature space. Sensitivity, specificity and accuracy generated by the proposed algorithm in five prostates by LADA were 52.2%, 89.1% and 85.1% respectively, compared to 31.3%, 85.3% and 80.9% generated by LDA. The delineation accuracy attainable by this tool has a potential in increasing the cancer detection rate in biopsies and in minimizing collateral damage of surrounding tissues in focal therapies.
Image matting is a method that separates foreground and background objects in an image, and has been widely used in medical image segmentation. Previous work has shown that matting can be formulated as a graph Laplacian matrix. In this paper, we derived matting from a local regression and global alignment view, as an attempt to provide a more intuitive solution to the segmentation problem. In addition, we improved the matting algorithm by adding a weight extension and refer to the proposed approach as Adaptive Weight Matting (AWM), where an adaptive weight was added to each local regression term to reduce the bias caused by outliers. We compared the segmentation results generated by the proposed method and several state-of-the-art segmentation methods, including conventional matting, graph-cuts and random walker, on medical images of different organs acquired using different imaging modalities. Experimental results demonstrated the advantages of AWM on medical image segmentation.
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