Gravel Image Auto-Segmentation Based on an Improved Normalized Cuts Algorithm

The study of the grain-size distribution of gravels is always an important and challenging issue in stratigraphy and morphology, especially in the field of automated measurement. It largely reduces many manual processes and time consumption. Precise segmentation method plays a very important role in it. In this study, a digital image method using an improved normalized cuts algorithm is proposed for auto-segmentation of gravel image. It added grain-size estimation, and used the feature vector based on color. It has made great improvements in many respects, especially in accuracy of edge segmentation and automation. Compared with manual measurement methods and other image processing methods, the method studied in this paper is an efficient method for precisely segmenting gravel images.

Auto-Segmentation on Liver with U-Net and Pixel De-Convolutional Network

Purpose: To improve the liver auto-segmentation performance of three-dimensional (3D) U-net by replacing the conventional up-sampling convolution layers with the Pixel De-convolutional Network (PDN) that considers spatial features. Methods: The U-net was originally developed to segment neuronal structure with outstanding performance but suffered serious artifacts from indirectly unrelated adjacent pixels in its up-sampling layers. The hypothesis of this study was that the segmentation quality of the liver could be improved with PDN in which the up-sampling layer was replaced by a pixel de-convolution layer (PDL). Seventy-eight plans of abdominal cancer patients were anonymized and exported. Sixty-two were chosen for training two networks: 1) 3D U-Net, and 2) 3D PDN, by minimizing the Dice loss function. The other sixteen plans were used to test the performance. The similarity Dice and Average Hausdorff Distance (AHD) were calculated and compared between these two networks. Results: The computation time for 62 training cases and 200 training epochs was about 30 minutes for both networks. The segmentation performance was evaluated using the remaining 16 cases. For the Dice score, the mean ± standard deviation were 0.857 ± 0.011 and 0.858 ± 0.015 for the PDN and U-Net, respectively. For the AHD, the mean ± standard deviation were 1.575 ± 0.373 and 1.675 ± 0.769, respectively, corresponding to an improvement of 6.0% and 51.5% of mean and standard deviation for the PDN. Conclusion: The PDN has outperformed the U-Net on liver auto-segmentation. The predicted contours of PDN are more conformal and smoother when compared with the U-Net.

A Hybrid Algorithm to Address Ambiguities in Deformable Image Registration for Radiation Therapy

We proposed the use of a hybrid deformable image registration approach that combines compact-support radial basis functions (CSRBF) spline registration with intensity-based image registration. The proposed method first uses the pre-viously developed image intensity-based method to achieve voxel-by-voxel correspondences over the entire image re-gion. Next, for those areas of inaccurate registration, a sparse set of landmark correspondences was defined for local deformable image registration using a multi-step CSRBF approach. This hybrid registration takes advantage of both intensity-based method for automatic processing of entire images and the CSRBF spline method for fine adjustment over specific regions. The goal of using this hybrid registration is to locally control the quality of registration results in specific regions of interest with minimal human intervention. The major applications of this approach in radiation ther-apy are for the corrections of registration failures caused by various imaging artifacts resulting in, low image contrast, and non-correspondence situations where an object may not be imaged in both target and source images. Both synthetic and real patient data have been used to evaluate this hybrid method. We used contours mapping to validate the accuracy of this method on real patient image. Our studies demonstrated that this hybrid method could improve overall registra-tion accuracy with moderate overhead. In addition, we have also shown that the multi-step CSRBF registration proved to be more effective in handling large deformations while maintaining the smoothness of the transformation than origi-nal CSRBF.

Atlas-based deep gray matter and white matter analysis in Alzheimer's disease: diffusion abnormality and correlation with cognitive function

Objective To identify the diffusion alterations of deep gray matter(GM)and white matter(WM)among Alzheimer’s disease(AD),mild cognitive impairment(MCI)and healthy people by atlas-based analysis(ABA),and to investigate the respective relationship with cognitive function.Methods Twenty-one AD patients(AD group),8 MCI patients(MCI group)and