GLISTR: Glioma Image Segmentation and Registration

Abstract

We present a generative approach for simultaneously registering a probabilistic atlas of a healthy population to brain magnetic resonance (MR) scans showing glioma and segmenting the scans into tumor as well as healthy tissue labels. The proposed method is based on the expectation maximization (EM) algorithm that incorporates a glioma growth model for atlas seeding, a process which modifies the original atlas into one with tumor and edema adapted to best match a given set of patient’s images. The modified atlas is registered into the patient space and utilized for estimating the posterior probabilities of various tissue labels. EM iteratively refines the estimates of the posterior probabilities of tissue labels, the deformation field and the tumor growth model parameters. Hence, in addition to segmentation, the proposed method results in atlas registration and a low-dimensional description of the patient scans through estimation of tumor model parameters. We validate the method by automatically segmenting 10 MR scans and comparing the results to those produced by clinical experts and two state-of-the-art methods. The resulting segmentations of tumor and edema outperform the results of the reference methods, and achieve a similar accuracy from a second human rater. We additionally apply the method to 122 patients scans and report the estimated tumor model parameters and their relations with segmentation and registration results. Based on the results from this patient population, we construct a statistical atlas of the glioma by inverting the estimated deformation fields to warp the tumor segmentations of patients scans into a common space.

Imaging genomics in cancer research: limitations and promises

“…86 Segmentation methods that explicitly incorporate biophysical models of tumour growth, in a way to facilitate imaging-based segmentation, have also been proposed. 87,88 Although validation of these methods is a very challenging and effort-demanding task, some international efforts for creating validation platforms have started to emerge. A prime example is the Brain Tumor Segmentation challenge organized annually, which uses TCIA and other public data sets, along with ground truth, to evaluate a variety of algorithms.…”

GLISTRboost: Combining Multimodal MRI Segmentation, Registration, and Biophysical Tumor Growth Modeling with Gradient Boosting Machines for Glioma Segmentation

“…Specifically, these data are a combination of the training set (10 LGGs and 20 HGGs) used in the BRATS 2013 challenge [17], as well as 44 LGG and 112 HGG scans provided from the National Institutes of Health (NIH) Cancer Imaging Archive (TCIA). The data of each patient consists of native and contrast-enhanced (CE) T1-weighted, as well as T2-weighted and T2 Fluid-attenuated inversion recovery (FLAIR) MRI volumes.…”

“…A modified version of the GLioma Image SegmenTation and Registration (GLISTR) software [10] was subsequently used to delineate the boundaries of healthy tissues (i.e., white and gray matter, cerebrospinal fluid, vessels and cerebellum), as well as tumor tissues (i.e., edema, necrosis, non-enhancing and enhancing parts of the tumor). Although GLISTR was inspired by a sequential approach of segmentation of the input brain scans followed by the registration of the outcome to a given healthy atlas [8], it was originally proposed in [9,10] as a tool that jointly performs segmentation and registration, but handles only scans with solitary HGGs.…”

“…Although GLISTR was inspired by a sequential approach of segmentation of the input brain scans followed by the registration of the outcome to a given healthy atlas [8], it was originally proposed in [9,10] as a tool that jointly performs segmentation and registration, but handles only scans with solitary HGGs. It was then conceptually improved in [14] to target broader brain tumor appearances, including multifocal masses and complex shapes with heterogeneous textures (e.g., LGGs), enabling it to also participate in the BRATS 2014 challenge [13].…”

Multi-Feature Guided Brain Tumor Segmentation Based on Magnetic Resonance Images

“…Then the hierarchical RW algorithm is performed to identify the tumor and edema. An atlas-based method is commonly used in tumor-growth modeling [9]. An energy cost function-based segmentation method is adopted in this paper, which can incorporate meaningful visual information into the cost function and guide the segmentation specifically.…”