The ROI was drawn around each metastasis encompassing the entire enhancing portion of the brain metastasis as well as central areas of necrosis and excluding edema, generating a binary mask (for a 1-class segmentation task). All ground truth segmentations were reviewed and edited by H.S., a neuroradiation oncologist with 13 years of postfellowship experience. Ground truth segmentations of BM were manually acquired on the 3D T1 MPRAGE images (ITK-SNAP 11 by C.H., a neuroradiologist with 8 years of postfellowship experience. Brain-extracted T2 FLAIR and 3D T1 MPRAGE data were bias-field corrected using the N4ITK algorithm (3D Slicer) 10 and saved in a NIfTI format. Brain extraction was performed using a custom-written script in Matlab and the Image Processing Toolbox, Release 2022a and 2023a (MathWorks) which uses a brain mask derived from a binary threshold of the coregistered and interpolated trace DWI ( b=1000 s/mm 2) data set. T2 FLAIR and trace DWI ( b=1000 s/mm 2) images were registered to 3D T1 MPRAGE using BRAINSFit (3D Slicer ) 10 with a 6- df rigid registration and linear interpolation to 1-mm isotropic resolution. In this study, we evaluate BLAST methodology for segmentation of BM and hypothesize that it is accurate and reproducible. Within the parameter space, voxels related to the background layer are then removed from the entire volume and additional operator-defined thresholds are applied to preferentially detect and segment BM. In the present implementation of the methodology, K-means clustering is used to define the statistics of the background layer on a section of normal brain. The methodology first establishes a parameter space with origin and axes defined by the signal intensity statistics of background brain (Background Layer Statistics ). In this article, we describe an alternative semiautomated method for segmentation of BM using multiparametric MR imaging. 9 Furthermore, the signal heterogeneity and the small size of BM relative to background brain are additional challenges for clustering algorithms, to accurately identify and classify these tumors. Brain tumor segmentation with these techniques can be challenging because segmentation performance is highly dependent on initial conditions and the algorithm used. 8 K-means clustering groups signal intensity data into k classes by iteratively computing a mean intensity for each class and clustering voxels into the closest class centroid. Unsupervised techniques using clustering methods (eg, K-means, fuzzy c-means, and the expectation-maximization method) are iterative algorithms that segment by grouping voxels with similar signal properties (intensities), then estimating and optimizing cluster properties. Unsupervised techniques do not require a priori training and can be used to facilitate the creation of ground truth data, which can be used to train DL models. Additionally, optimal performance of deep learning (DL) algorithms across multiple institutions commonly requires retraining with additional site-specific data (distributions). 6 These supervised methods, however, require large numbers of (manual) labels for training, which is a time-consuming and costly process and can be prone to bias introduced by the training set. Supervised methods, such as those based on deep convolutional neural networks, have recently garnered attention, showing excellent performance in brain tumor segmentation tasks, 3 ⇓- 5 with 1 algorithm now FDA-cleared. Machine learning methods can be grouped into supervised and unsupervised algorithms. To aid in this requirement, accurate segmentation of the tumor is needed to provide precise lesion targeting and to monitor changes in the size of the metastases between baseline and follow-up scans.ĭuring the past few years, advances in machine learning methods have led to improvements in automated and semiautomated brain tumor segmentation. Additionally, treatment of BM requires measurements of tumor burden at baseline and follow-up to assess treatment response. 2 A requirement for SRS is accurate detection and contouring of BM. 1 However, recent advances in the treatment of BM, with, for example, stereotactic radiosurgery (SRS) have led to improvements in patient outcomes with less impact on neurocognition and quality of life. ABBREVIATIONS: BL background layer BLAST Background Layer Statistics BM brain metastases DL deep learning DSC Dice-Sørensen coefficient HD Hausdorff distance ICC intraclass correlation coefficient IQR interquartile range SRS stereotactic radiosurgery TH thresholdīrain metastases (BM) are diagnosed in up to 40% of patients with metastatic cancer and usually imply a short survival.
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