Brain tumor target volume determination for radiation treatment planning through automated MRI segmentation

Mazzara, Gloria Patrika; Velthuizen, R.P.; Pearlman, J.; Greenberg, Harvey; Wagner, Henry

doi:10.1016/j.ijrobp.2004.01.026

Cited by 194 publications

(137 citation statements)

References 24 publications

(27 reference statements)

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“…In agreement with other studies related to automatic tumor segmentation (15,17), our results suggest that the diagnostic efficacy of DSC glioma characterization when using a knowledge-based FCM glioma segmenta- tion method correspond well with the results from manually defined tumor volumes. The diagnostic accuracy values obtained in our study were similar to those reported in the literature (5,11,34,35) and there was no significant difference between the A z values of the automatic and manual method. In addition, using the automatic segmentation procedure, a single patient was analyzed in approximately 4 min whereas the manual glioma identification procedure was performed in approximately 10 min (10), thereby reducing the overall processing time with a factor of more than 2.…”

Section: Discussionsupporting

confidence: 90%

Automatic glioma characterization from dynamic susceptibility contrast imaging: Brain tumor segmentation using knowledge‐based fuzzy clustering

Emblem

Nedregaard

Hald

et al. 2009

Magnetic Resonance Imaging

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Purpose:To assess whether glioma volumes from knowledge-based fuzzy c-means (FCM) clustering of multiple MR image classes can provide similar diagnostic efficacy values as manually defined tumor volumes when characterizing gliomas from dynamic susceptibility contrast (DSC) imaging. Materials and Methods:Fifty patients with newly diagnosed gliomas were imaged using DSC MR imaging at 1.5 Tesla. To compare our results with manual tumor definitions, glioma volumes were also defined independently by four neuroradiologists. Using a histogram analysis method, diagnostic efficacy values for glioma grade and expected patient survival were assessed. Results:The areas under the receiver operator characteristics curves were similar when using manual and automated tumor volumes to grade gliomas (P ϭ 0.576 -0.970). When identifying a high-risk patient group (expected survival Ͻ2 years) and a low-risk patient group (expected survival Ͼ2 years), a higher log-rank value from Kaplan-Meier survival analysis was observed when using automatic tumor volumes (14.403; P Ͻ 0.001) compared with the manual volumes (10.650 -12.761; P ϭ 0.001-0.002). Conclusion:Our results suggest that knowledge-based FCM clustering of multiple MR image classes provides a completely automatic, user-independent approach to selecting the target region for presurgical glioma characterization

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Section: Discussionsupporting

confidence: 90%

Automatic glioma characterization from dynamic susceptibility contrast imaging: Brain tumor segmentation using knowledge‐based fuzzy clustering

Emblem

Nedregaard

Hald

et al. 2009

Magnetic Resonance Imaging

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“…Several studies reported failures of individual expert rater segmentations because structural MRI may obscure the precise visual delineation of glioma boundaries [10][11][12]. In addition, manual segmentation is time-consuming and is a rate-determining step for further treatment planning [13].…”

Section: Introductionmentioning

confidence: 99%

Multi-modal Glioblastoma Segmentation: Man versus Machine

Porz¹,

Bauer²,

Pica³

et al. 2014

J Neurol Surg A Cent Eur Neurosurg

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“…Manual contours have been serving as ground truth as an established practice — for example in segmenting brain tumor, (33) hippocampus region, (34) esophageal and gastroesophageal cancer, (35) among others. Although there is variability in physician's contour of regions of interest, it is a known limitation of studies evaluating efficacy of segmentation algorithms in medical imaging (36) . The appropriateness of using manual contours to serve as ground truth warrants a separate investigation and is beyond the scope of this manuscript.…”

Section: Discussionmentioning

confidence: 99%

A comparative study of automatic image segmentation algorithms for target tracking in MR‐IGRT

Feng

Kawrakow

Olsen

et al. 2016

J Applied Clin Med Phys

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On‐board magnetic resonance (MR) image guidance during radiation therapy offers the potential for more accurate treatment delivery. To utilize the real‐time image information, a crucial prerequisite is the ability to successfully segment and track regions of interest (ROI). The purpose of this work is to evaluate the performance of different segmentation algorithms using motion images (4 frames per second) acquired using a MR image‐guided radiotherapy (MR‐IGRT) system. Manual contours of the kidney, bladder, duodenum, and a liver tumor by an experienced radiation oncologist were used as the ground truth for performance evaluation. Besides the manual segmentation, images were automatically segmented using thresholding, fuzzy k‐means (FKM), k‐harmonic means (KHM), and reaction‐diffusion level set evolution (RD‐LSE) algorithms, as well as the tissue tracking algorithm provided by the ViewRay treatment planning and delivery system (VR‐TPDS). The performance of the five algorithms was evaluated quantitatively by comparing with the manual segmentation using the Dice coefficient and target registration error (TRE) measured as the distance between the centroid of the manual ROI and the centroid of the automatically segmented ROI. All methods were able to successfully segment the bladder and the kidney, but only FKM, KHM, and VR‐TPDS were able to segment the liver tumor and the duodenum. The performance of the thresholding, FKM, KHM, and RD‐LSE algorithms degraded as the local image contrast decreased, whereas the performance of the VP‐TPDS method was nearly independent of local image contrast due to the reference registration algorithm. For segmenting high‐contrast images (i.e., kidney), the thresholding method provided the best speed (<1 ms) with a satisfying accuracy (Dice=0.95). When the image contrast was low, the VR‐TPDS method had the best automatic contour. Results suggest an image quality determination procedure before segmentation and a combination of different methods for optimal segmentation with the on‐board MR‐IGRT system.PACS number(s): 87.57.nm, 87.57.N‐, 87.61.Tg

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Brain tumor target volume determination for radiation treatment planning through automated MRI segmentation

Cited by 194 publications

References 24 publications

Automatic glioma characterization from dynamic susceptibility contrast imaging: Brain tumor segmentation using knowledge‐based fuzzy clustering

Automatic glioma characterization from dynamic susceptibility contrast imaging: Brain tumor segmentation using knowledge‐based fuzzy clustering

Multi-modal Glioblastoma Segmentation: Man versus Machine

A comparative study of automatic image segmentation algorithms for target tracking in MR‐IGRT

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