Semi-automatic level set segmentation of liver tumors combining a spiral-scanning technique with supervised fuzzy pixel classification

Smeets, Dirk; Loeckx, Dirk; Stijnen, Bert; Dobbelaer, Bart De; Vandermeulen, Dirk; Suetens, Paul

doi:10.1016/j.media.2009.09.002

Cited by 116 publications

(71 citation statements)

References 18 publications

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“…Conventional contour similarity measures fall into two categories: volumetric overlap measurements such as Dice similarity coefficient (DSC), (28) Jaccard similarity coefficient (JSC), (30) and relative volume difference (RVD), (31) and distance measurements including average symmetric absolute symmetric surface distance (ASD), (32) root mean square symmetric absolute surface distance (RMSD), (32) and maximum symmetric absolute surface distance (MSD) (32) . When comparing contours using existing similarity measures, conflicting measures may lead to difficult interpretation.…”

Section: Methodsmentioning

confidence: 99%

A comprehensive evaluation of adaptive daily planning for cervical cancer HDR brachytherapy

Meerschaert

Nalichowski

Burmeister

et al. 2016

J Applied Clin Med Phys

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The purpose of this study was to evaluate adaptive daily planning for cervical cancer patients who underwent high‐dose‐rate intracavitary brachytherapy (HDR‐BT) using comprehensive interfractional organ motion measurements. This study included 22 cervical cancer patients who underwent 5 fractions of HDR‐BT. Regions of interest (ROIs) including high‐risk clinical tumor volume (HR‐CTV) and organs at risk (OARs) were manually contoured on daily CT images. All patients were clinically treated with adaptive daily plans (ADP), which involved ROI delineation and dose optimization at each treatment fraction. Single treatment plans (SP) were retrospectively generated by applying the first treatment fraction's dwell times adjusted for decay and dwell positions of the applicator to subsequent treatment fractions. Various existing similarity metrics were calculated for the ROIs to quantify interfractional organ variations. A novel similarity (JRARM) score was established, which combined both volumetric overlap metrics (DSC, JSC, and RVD) and distance metrics (ASD, MSD, and RMSD). Linear regression was performed to determine a relationship between interfractional organ variations of various similarity metrics and D2cc variations from both plans. Wilcoxon signed‐rank tests were used to assess ADP and SP by comparing EQD2thinmathspaceD2cc(α/β=3) for OARs. For interfractional organ variations, the sigmoid demonstrated the greatest variations based on the JRARM, DSC, and RMSD metrics. Comparisons between paired ROIs showed differences in metrics at each treatment fraction. RVD, MSD, and RMSD were found to be significantly correlated to D2cc variations for bladder and sigmoid. The comparison between plans found ADP provided lower EQD2 D2cc of OARs than SP. Specifically, the sigmoid demonstrated statistically significant dose variations (p=0.015). Substantial interfractional organ motion occurs during HDR‐BT based on comprehensive measurements and may significantly affect D2cc of OARs. Adaptive daily planning provides improved dose sparing for OARs compared to single planning with the extent of sparing being different among OARs.PACS number(s): 87.55.D, 87.55.de, 87.55.kh, 87.57.nj

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Section: Methodsmentioning

confidence: 99%

A comprehensive evaluation of adaptive daily planning for cervical cancer HDR brachytherapy

Meerschaert

Nalichowski

Burmeister

et al. 2016

J Applied Clin Med Phys

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“…The centroids value obtained from FCM are used as initial positions of wolf in GWO technique. N Ghatway et al [15] uses fractional differential for liver segmentation of enhanced CT images. This is objective of improving the texture and edge features.…”

Section: Literature Reviewmentioning

confidence: 99%

Liver tumour classification using average correction higher order local autocorrelation coefficient and legendre moments

H.L¹,

Sudhamani²

2018

IJET

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The major reasons for liver carcinoma are cirrhosis and hepatitis. In order to identify carcinoma in the liver abdominal CT images are used. From abdominal CT images, segmentation of liver portion using adaptive region growing, tumor segmentation from extracted liver using Simple Linear Iterative Clustering is already implemented. In this paper, classification of tumors as benign or malignant is accomplished using Rough-set classifier based on texture feature extracted using Average Correction Higher Order Local Autocorrelation Coefficients and Legendre moments. Classification accuracy achieved in proposed scheme is 90%. The results obtained are promising and have been compared with existing methods.

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“…Seeing that the scores fall in [0, 1], this kind of task is just like fuzzy segmentation. Unlike the techniques of [23], [24], which in turn rely only on category probability using Bayes' guideline, our framework is considerably more flexible since it utilizes the probability scores by any classifier. At the same time, we maintain gradient information to end contour evolution when presently there are no fuzzy principles due to distinct limitations.…”

Section: Modified Level Setsmentioning

confidence: 99%

Brain Tumor Segmentation using hybrid of both Netrosopic Modified Nonlocal Fuzzy C-mean and Modified Level Sets

2016

IJSR

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an improved segmentation approach based on Neutrosophic sets (NS) and Modified Non local Fuzzy c-mean clustering (NLFCM) is proposed. The brain tumor MRI image is transformed into NS domain, which is described using three subsets namely; the percentage of truth in a subset T%, the percentage of indeterminacy in a subset I% , and the percentage of falsity in a subset F% . The entropy in NS is defined and employed to evaluate the indeterminacy. NS image is adapted also using Modified Non local Fuzzy Cmean algorithm(MNLFCM). Finally, MRI brain tumor image is segmented and tumor is selected using Modified Level Sets (MLS). The proposed approach denoted as NS-MNLFCM-MLS and compared with another paper using Jaccard Index and Dice Coefficient. The experimental results demonstrate that the proposed approach is less sensitive to noise and performs better on MRI brain image.

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Semi-automatic level set segmentation of liver tumors combining a spiral-scanning technique with supervised fuzzy pixel classification

Cited by 116 publications

References 18 publications

A comprehensive evaluation of adaptive daily planning for cervical cancer HDR brachytherapy

A comprehensive evaluation of adaptive daily planning for cervical cancer HDR brachytherapy

Liver tumour classification using average correction higher order local autocorrelation coefficient and legendre moments

Brain Tumor Segmentation using hybrid of both Netrosopic Modified Nonlocal Fuzzy C-mean and Modified Level Sets

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