Semi-Automatic Segmentation Software for Quantitative Clinical Brain Glioblastoma Evaluation

Abstract

Rationale and Objectives Quantitative measurement provides essential information about disease progression and treatment response in patients with Glioblastoma multiforme (GBM). The goal of this paper is to present and validate a software pipeline for semi-automatic GBM segmentation, called AFINITI (Assisted Follow-up in NeuroImaging of Therapeutic Intervention), using clinical data from GBM patients. Materials and Methods Our software adopts the current state-of-the-art tumor segmentation algorithms and combines them into one clinically usable pipeline. Both the advantages of the traditional voxel-based and the deformable shape-based segmentation are embedded into the software pipeline. The former provides an automatic tumor segmentation scheme based on T1- and T2-weighted MR brain data, and the latter refines the segmentation results with minimal manual input. Results Twenty six clinical MR brain images of GBM patients were processed and compared with manual results. The results can be visualized using the embedded graphic user interface (GUI). Conclusion Validation results using clinical GBM data showed high correlation between the AFINITI results and manual annotation. Compared to the voxel-wise segmentation, AFINITI yielded more accurate results in segmenting the enhanced GBM from multimodality MRI data. The proposed pipeline could be used as additional information to interpret MR brain images in neuroradiology.

Semiautomated Volumetric Measurement on Postcontrast MR Imaging for Analysis of Recurrent and Residual Disease in Glioblastoma Multiforme

“…14 Application of computer-assisted volumetry (CAV) has been explored for the evaluation of gliomas; however, these studies have dealt with native nontreated disease and have not been validated against other measurement techniques. [15][16][17] In this study, we describe a novel CAV technique for assessment of tumor burden in the patient with GBM. Specifically, we describe the reliability and feasibility of this technique compared with traditional linear-based measurements in the patient with postresection GBM.…”

Iterative Probabilistic Voxel Labeling: Automated Segmentation for Analysis of The Cancer Imaging Archive Glioblastoma Images

“…To address these deficiencies, effort has been devoted to developing automated algorithms for segmenting tumor volumes. [8][9][10][11][12] These algorithms include clustering, 13,14 discriminative strategies, 15 and generative approaches. 11,16,17 The success of these methods has been limited by widely differing MR imaging protocols for image acquisition and quality 18 and the significant overlap between the radiographic appearance of glioblastoma tumors and normal cerebrum on MR imaging.…”

Classification of tumor area using combined DCE and DSC MRI in patients with glioblastoma

“…However, having multiple parameters complicates the extraction of diagnostic information across the images on a voxel basis, and necessitates the use of automatic or semi-automatic segmentation methods [12][13][14][15][16]. Automatic segmentation can be performed based on various imaging parameters extracted from the raw data or in the temporal domain; they can be categorized into supervised or unsupervised algorithms, and can either be performed at the group-or at the subject-level.…”