Rapid and Accurate MRI Segmentation of Peritumoral Brain Edema in Meningiomas

Brain tumor segmentation consists of separating the different tumor tissues (solid or active tumor, edema, and necrosis) from normal brain tissues: gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF). In brain tumor studies, the existence of abnormal tissues may be easily detectable most of the time. However, accurate and reproducible segmentation and characterization of abnormalities are not straightforward. In the past, many researchers in the field of medical imaging and soft computing have made significant survey in the field of brain tumor segmentation. Both semiautomatic and fully automatic methods have been proposed. Clinical acceptance of segmentation techniques has depended on the simplicity of the segmentation, and the degree of user supervision. Interactive or semiautomatic methods are likely to remain dominant in practice for some time, especially in these applications where erroneous interpretations are unacceptable. This article presents an overview of the most relevant brain tumor segmentation methods, conducted after the acquisition of the image. Given the advantages of magnetic resonance imaging over other diagnostic imaging, this survey is focused on MRI brain tumor segmentation. Semiautomatic and fully automatic techniques are emphasized. Copyright © 2013 Elsevier Inc. All rights reserved.

This paper describes a framework for automatic brain tumor segmentation from MR images. The detection of edema is done simultaneously with tumor segmentation, as the knowledge of the extent of edema is important for diagnosis, planning, and treatment. Whereas many other tumor segmentation methods rely on the intensity enhancement produced by the gadolinium contrast agent in the T1-weighted image, the method proposed here does not require contrast enhanced image channels. The only required input for the segmentation procedure is the T2 MR image channel, but it can make use of any additional non-enhanced image channels for improved tissue segmentation. The segmentation framework is composed of three stages. First, we detect abnormal regions using a registered brain atlas as a model for healthy brains. We then make use of the robust estimates of the location and dispersion of the normal brain tissue intensity clusters to determine the intensity properties of the different tissue types. In the second stage, we determine from the T2 image intensities whether edema appears together with tumor in the abnormal regions. Finally, we apply geometric and spatial constraints to the detected tumor and edema regions. The segmentation procedure has been applied to three real datasets, representing different tumor shapes, locations, sizes, image intensities, and enhancement.

Insular gliomas remain surgically challenging cases due to complex anatomy, including surrounding vasculature and the relationship to functional structures. To define the morbidity profile associated with aggressive insular glioma removal as well as its impact on long-term outcome, the authors retrospectively evaluated the extent of resection (EOR) in the context of this complex anatomy and function and assessed its role in determining disease progression, malignant transformation, and, ultimately, patient survival. The study population included adults who had undergone initial or repeat resection of insular gliomas of all grades. Tumor location was identified according to a proposed quadrant-style classification (Zones I-IV) of the insula. Low- and high-grade gliomas were volumetrically analyzed using FLAIR and contrast-enhanced T1-weighted MR imaging, respectively. One hundred fifteen procedures involving 104 patients with insular gliomas were identified. Patients presented with low-grade gliomas (LGGs) in 70 cases (60%) and high-grade gliomas (HGGs) in 45 (40%). Zone I (anterior-superior) was the most common site within the insula (40 patients [39%]), followed by Zone I+IV (anterior-superior + anterior-inferior; 26 patients [25%]). The median EOR was 82% (range 31-100%) for low-grade lesions and 81% (range 47-100%) for high-grade lesions. Zone I was associated with the highest median EOR (86%), and among all lesion grades, the insular quadrant anatomy was predictive of the EOR (p = 0.0313). Overall, there were 16 deaths (15%) during a median follow-up of 4.2 years. There were no surgery-related deaths, and new, permanent postoperative deficits were noted in 6 patients (6%). Among LGGs, tumor progression and malignant transformation were identified in 20 (29%) and 14 cases (20%), respectively. Among HGGs, progression was identified in 16 cases (36%). Patients with LGGs resected >or= 90% had a 5-year overall survival (OS) rate of 100%, whereas those with lesions resected or= 90% had a 2-year OS rate of 91%; when the EOR was < 90%, the 2-year OS rate was 75%. The EOR was predictive of OS both in cases of LGGs (hazard ratio [HR] 0.955, 95% CI 0.921-0.992, p = 0.017) and HGGs (HR 0.955, 95% CI 0.918-0.994, p = 0.024). Progression-free survival (PFS) was also predicted by the EOR in both LGGs (HR 0.973, 95% CI 0.948-0.998, p = 0.0414) and HGGs (HR 0.958, 95% CI 0.919-0.999, p = 0.0475). Interestingly, among patients with LGGs, malignant progression was also significantly associated with a lower EOR (HR 0.968, 95% CI 0.393-0.998, p = 0.0369). Aggressive resection of insular gliomas of all grades can be accomplished with an acceptable morbidity profile and is predictive of improved OS and PFS. Among insular LGGs, a greater EOR is also associated with longer malignant PFS. Data in this study also suggest that insular gliomas generally follow a more indolent course than similar lesions in other brain regions.