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Nonlocal spatial clustering in automated brain hematoma and edema segmentation
Author(s) -
Tu Wei,
Kong Linglong,
Karunamuni Rohana,
Butcher Ken,
Zheng Lili,
McCourt Rebecca
Publication year - 2019
Publication title -
applied stochastic models in business and industry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.413
H-Index - 40
eISSN - 1526-4025
pISSN - 1524-1904
DOI - 10.1002/asmb.2431
Subject(s) - segmentation , artificial intelligence , edema , computer science , hematoma , cluster analysis , sørensen–dice coefficient , medicine , pattern recognition (psychology) , image segmentation , radiology , surgery
Hematoma and edema volume are potential predictors of 30‐day mortality rate and functional outcome (degree of disability or dependence in daily activities after a stroke) for patients with intracerebral hemorrhage. The manual segmentation of hematoma and edema from computed tomography scans is common practice but a time‐consuming and labor‐intensive task. Automated segmentation is an appealing alternative, but it is challenging because of the poorly defined boundary between edema and the surrounding healthy brain tissue. There is limited literature on this problem, and we aim to help fill the gap between the theoretical development of segmentation methods and the practical need. Our framework is fully automated and requires no supervision. The method uses nonlocal regularized spatial fuzzy C‐means clustering in the initialization stage and the active contour without edges method in the refinement stage. To evaluate it, we used 30 subjects with different sizes, shapes, and locations of hematoma and edema. Compared with the manual segmentation results from two independent raters, our method performs hematoma segmentation well, with an average dice score coefficient of 0.92. Although there is a lack of ground truth in edema segmentation due to the high inter and intrarater variation, our results are comparable with manual segmentation results.