Adaptive Distance Metric Learning for Diffusion Tensor Image Segmentation

Abstract

High quality segmentation of diffusion tensor images (DTI) is of key interest in biomedical research and clinical application. In previous studies, most efforts have been made to construct predefined metrics for different DTI segmentation tasks. These methods require adequate prior knowledge and tuning parameters. To overcome these disadvantages, we proposed to automatically learn an adaptive distance metric by a graph based semi-supervised learning model for DTI segmentation. An original discriminative distance vector was first formulated by combining both geometry and orientation distances derived from diffusion tensors. The kernel metric over the original distance and labels of all voxels were then simultaneously optimized in a graph based semi-supervised learning approach. Finally, the optimization task was efficiently solved with an iterative gradient descent method to achieve the optimal solution. With our approach, an adaptive distance metric could be available for each specific segmentation task. Experiments on synthetic and real brain DTI datasets were performed to demonstrate the effectiveness and robustness of the proposed distance metric learning approach. The performance of our approach was compared with three classical metrics in the graph based semi-supervised learning framework.

Automatic brain tissue segmentation based on graph filter

“…To further assess the performance objectively, quantitative evaluation is performed by calculating the dice similarity coefficient (DSC) and volume difference ratio (VDR) [ 42 – 44 ]. These metrics are commonly utilized benchmark evaluation strategies in the segmentation community.…”

Evaluating the Feasibility of an Agglomerative Hierarchy Clustering Algorithm for the Automatic Detection of the Arterial Input Function Using DSC-MRI

“…Thus, only a few attempts have been made to perform local AIF measurements [4] , [23] , [32] . For the global AIF, many different deconvolution approaches can be used to reduce the impacts of tracer delay, including time insensitive block-circulant singular value decomposition (cSVD) and nonlinear stochastic regularization (NSR) [4] , [21] , [33] – [38] . Second, we only focused on evaluating the performance of the three different AIF detection methods and we did not calculate the absolute values of the hemodynamics, such as CBF, CBV, and MTT.…”

Can incomplete silver standard labels improve performance of DTI-based volumetric segmentation of the corpus callosum?

“…Among the existing semi-automated and automated segmentation methods working directly on DTI, 15 most of them segment the CC on the midsagittal slice such as the ones proposed by Freitas et al, 21 Niogi et al 22 Nazem-Zadeh et al, 23 Luis-Garcıa et al, 24 Cover et al 25 and Herrera et al 26 For volumetric CC segmentation on DTI, there are only five automated methods: Rittner et al 27 extended Freitas et al' 28 approach and achieved a hierarchical segmentation by applying the 3D watershed using automatically selected markers; Kong et al 29 proposed a method to automatically learn an adaptive distance metric, through a semi-supervised learning model based on graphs; Wang et al 30 introduced the gray information of multiple atlases and the prior information of target shapes into the Active Shape Model (ASM) and proposed the Multi-Atlas Active Shape Model (MA-ASM) approach for segmentation of 7 Region of Interest (ROI), including the CC; Rodrigues et al 1 developed a volumetric segmentation method for the CC using Convolutional Neural Networks (CNN) using as input diffusion maps, such as fractional anisotropy (FA), mean diffusivity (MD) and mode of anisotropy (MO), separately and combined; Rodrigues et al 31 developed a subsequent method based on 3D convolutions, presenting better performance in comparison to the 2D model.…”