Detection of Volume-Changing Metastatic Brain Tumors on Longitudinal MRI Using a Semiautomated Algorithm Based on the Jacobian Operator Field

Shearkhani, Omid; Khademi, April; Eilaghi, Armin; Hojjat, Seyed-Parsa; Symons, Sean; Heyn, Chinthaka; Machnowska, Matylda; Chan, Alex Siu Wing; Sahgal, Arjun; Maralani, Pejman

doi:10.3174/ajnr.a5352

Cited by 7 publications

(5 citation statements)

References 47 publications

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“…The methodology was subsequently adapted to detect and segment BM on postgadolinium T1-weighted sequences, but the performance of the detection and segmentation task was limited by false-positives, mainly from vascular structures (arteries and veins), the dura, and the choroid plexus. 15 These methods require training on a set of normal brains to model the intensity distribution of tissue types. One of the advantages of BLAST is that no training is required to perform segmentations because the normal brain cluster is defined on the basis of the statistics from the subject.…”

Section: Discussionmentioning

confidence: 99%

Segmentation of Brain Metastases Using Background Layer Statistics (BLAST)

Heyn,

Moody,

Tseng

et al. 2023

AJNR Am J Neuroradiol

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BACKGROUND AND PURPOSE: Accurate segmentation of brain metastases is important for treatment planning and evaluating response. The aim of this study was to assess the performance of a semiautomated algorithm for brain metastases segmentation using Background Layer Statistics (BLAST). MATERIALS AND METHODS:Nineteen patients with 48 parenchymal and dural brain metastases were included. Segmentation was performed by 4 neuroradiologists and 1 radiation oncologist. K-means clustering was used to identify normal gray and white matter (background layer) in a 2D parameter space of signal intensities from postcontrast T2 FLAIR and T1 MPRAGE sequences. The background layer was subtracted and operator-defined thresholds were applied in parameter space to segment brain metastases. The remaining voxels were back-projected to visualize segmentations in image space and evaluated by the operators. Segmentation performance was measured by calculating the Dice-Sørensen coefficient and Hausdorff distance using ground truth segmentations made by the investigators. Contours derived from the segmentations were evaluated for clinical acceptance using a 5-point Likert scale. RESULTS:The median Dice-Sørensen coefficient was 0.82 for all brain metastases and 0.9 for brain metastases of $10 mm. The median Hausdorff distance was 1.4 mm. Excellent interreader agreement for brain metastases volumes was found with an intraclass correlation coefficient ¼ 0.9978. The median segmentation time was 2.8 minutes/metastasis. Forty-five contours (94%) had a Likert score of 4 or 5, indicating that the contours were acceptable for treatment, requiring no changes or minor edits. CONCLUSIONS:We show accurate and reproducible segmentation of brain metastases using BLAST and demonstrate its potential as a tool for radiation planning and evaluating treatment response.ABBREVIATIONS: BL ¼ background layer; BLAST ¼ Background Layer Statistics; BM ¼ brain metastases; DL ¼ deep learning; DSC ¼ Dice-Sørensen coefficient; HD ¼ Hausdorff distance; ICC ¼ intraclass correlation coefficient; IQR ¼ interquartile range; SRS ¼ stereotactic radiosurgery; TH ¼ threshold B rain metastases (BM) are diagnosed in up to 40% of patients with metastatic cancer and usually imply a short survival. 1 However, recent advances in the treatment of BM, with, for

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Section: Discussionmentioning

confidence: 99%

Segmentation of Brain Metastases Using Background Layer Statistics (BLAST)

Heyn,

Moody,

Tseng

et al. 2023

AJNR Am J Neuroradiol

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“…In equation (8), κ meant the low-dimensional feature space data. In addition, the improved FCM objective function could be expressed as follows.…”

Section: Adaptive Incremental Image Automatic Segmentationmentioning

confidence: 99%

“…Nuclear medicine imaging systems often have the characteristics of complex system structure and dense local connections. In order to ensure the safety of nuclear medicine imaging systems, it is generally necessary to use special recorders for nuclear medicine imaging system equipment for real-time monitoring [ 8 , 9 ]. However, the current research and development of nuclear medicine imaging systems are mainly focused on system equipment, and the research on their recorders is rarely involved.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy C-Means Algorithm-Based ARM-Linux-Embedded System Combined with Magnetic Resonance Imaging for Progression Prediction of Brain Tumors

Wang

Song

Wang

et al. 2022

Computational and Mathematical Methods in Medicine

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The aim of this research was to analyze the application of fuzzy C-means (FCM) algorithm-based ARM-Linux-embedded system in magnetic resonance imaging (MRI) images for prediction of brain tumors. The optimized FCM (OFCM) algorithm was proposed based on kernel function, and the ARM-Linux-embedded imaging system was designed under ARM9 chip and Linux recorder, which were applied in MRI images of brain tumor patients. It was found that the sensitivity, specificity, and accuracy of the OFCM algorithm (90.46%, 88.97%, and 97.46%) were greater obviously than those of the deterministic C-means clustering algorithm (80.38%, 77.98%, and 85.24%) and the traditional FCM algorithm (83.26%, 79.56%, and 86.45%), and the difference was statistically substantial ( P < 0.05 ). The ME and running time of the OFCM algorithm decreased sharply in contrast to those of the deterministic C-means clustering algorithm and the traditional FCM algorithm ( P < 0.05 ). There were great differences in fraction anisotropy (FA) and mean diffusion (MD) of tumor parenchymal area, surrounding edema area, and normal white matter area ( P < 0.05 ). FA of stage III+IV was smaller than those of stage I and II ( P < 0.05 ), while the apparent diffusion coefficient (ADC) of stage III+IV was greater than that of stage I and II ( P < 0.05 ). In conclusion, the poor update data processing and low data clustering efficiency of FCM were solved by OFCM. Moreover, computational efficiency of ARM-Linux-embedded imaging system was improved, so as to better realize the prediction of brain tumor patients through ARM-Linux-embedded system based on adaptive FCM incremental clustering algorithm.

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“…Limited prior work exists on automatic BM tracking. Shearkhani et al applied a Jacobian operator field to detect size changes in brain metastases on longitudinal MRs, with significant challenges from new and resolved BMs and false positive detections from blood vessels [23] . Chitphakdithai et al performed deformable intrapatient registration to estimate BM size changes, but relied on manual segmentation of the initial tumor [24] .…”

Section: Introductionmentioning

confidence: 99%