Segmentation of malignant gliomas through remote collaboration and statistical fusion

Xu, Zhoubing; Asman, Andrew J.; Singh, Eesha; Chambless, Lola B.; Thompson, Reid C.; Landman, Bennett A.

doi:10.1118/1.4749967

Cited by 2 publications

(1 citation statement)

References 29 publications

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“…Fleiss' kappa assesses the reliability of agreement between a fixed number of raters/users (in this case 3) when assigning categorical ratings to a number of items or classifying items, in this case as foreground (liver) or background. To accomplish the randomness of the decisions on each pixel of a slice of the liver dataset was calculated from the different segmentations produced by the initialization of different individuals [ 50 ].…”

Section: Methodsmentioning

confidence: 99%

A Low-Interaction Automatic 3D Liver Segmentation Method Using Computed Tomography for Selective Internal Radiation Therapy

Goryawala

Güleç

Bhatt

et al. 2014

BioMed Research International

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This study introduces a novel liver segmentation approach for estimating anatomic liver volumes towards selective internal radiation treatment (SIRT). The algorithm requires minimal human interaction since the initialization process to segment the entire liver in 3D relied on a single computed tomography (CT) slice. The algorithm integrates a localized contouring algorithm with a modified k-means method. The modified k-means segments each slice into five distinct regions belonging to different structures. The liver region is further segmented using localized contouring. The novelty of the algorithm is in the design of the initialization masks for region contouring to minimize human intervention. Intensity based region growing together with novel volume of interest (VOI) based corrections is used to accomplish the single slice initialization. The performance of the algorithm is evaluated using 34 liver CT scans. Statistical experiments were performed to determine consistency of segmentation and to assess user dependency on the initialization process. Volume estimations are compared to the manual gold standard. Results show an average accuracy of 97.22% for volumetric calculation with an average Dice coefficient of 0.92. Statistical tests show that the algorithm is highly consistent (P = 0.55) and independent of user initialization (P = 0.20 and Fleiss' Kappa = 0.77 ± 0.06).

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Section: Methodsmentioning

confidence: 99%