Brain tumor classification

Gliomas are the most common malignant brain tumors, with powerful invasiveness and an undesirable prognosis. Actin-related protein 2/3 complex subunit 5 (ARPC5) encodes a component of the Arp2/3 protein complex, which plays a significant role in regulating the actin cytoskeleton.

14p visharma 20-10-2023 2 2   Download

Glioblastoma (GBM) is a type of highly malignant brain tumor that is known for its significant intratumoral heterogeneity, meaning that there can be a high degree of variability within the tumor tissue. Despite the identification of several subtypes of GBM in recent years, there remains to explore a classification based on genes related to proliferation and growth.

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Understanding cellular and molecular heterogeneity in glioblastoma (GBM), the most common and aggressive primary brain malignancy, is a crucial step towards the development of effective therapies. Besides the inter-patient variability, the presence of multiple cell populations within tumors calls for the need to develop modeling strategies able to extract the molecular signatures driving tumor evolution and treatment failure.

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Compare and contrast the common types of brain tumors that affect the cerebrum, the cerebellum, the meninges, and the cranial nerves in adults and children, and outline their molecular basis and clinicopathologic features.

12p caothientrangnguyen 10-05-2020 14 1   Download

In Chapter 1 we present in detail a framework for fully automated brain tissue classification. The framework consists of a sequence of fully automated state of the art image registration (both rigid and nonrigid) and image segmentation algorithms. Models of the spatial distribution of brain tissues are combined with models of expected tissue intensities, including correction of MR bias fields and estimation of partial voluming. We also demonstrate how this framework can be applied in the presence of lesions....

831p echbuon 02-11-2012 62 9   Download

Consequently, it is necessary to integrate the information of all the spectral images to classify tissues. Multi-spectral image processing techniques [1-3] are hence employed to collect spectral information for classification and of clinically critical values. In this paper, a new classification approach was proposed, it is called unsupervised Vector Seeded Region Growing (UVSRG). The UVSRG mainly select seed pixel vectors by means of standard deviation and relative Euclidean distance.

288p wawawawawa 27-07-2012 61 7   Download