Magnetic Resonance Imaging-Based Target Volume Delineation in Radiation Therapy Treatment Planning for Brain Tumors Using Localized Region-Based Active Contour

Aslian, Hossein; Sadeghi, Mahdi; Mahdavi, Seied Rabi; Mofrad, Farshid Babapour; Astarakee, Mahdi; Khaledi, Navid; Fadavi, Pedram

doi:10.1016/j.ijrobp.2013.04.049

Cited by 25 publications

(21 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Because there are heterogeneous intensity distributions in many application fields using MR images, such as cancer imaging, Aslian et al (2013) argue that it is better to use local image statistics instead of a global one. A localized region-based Active Contour semiautomatic segmentation method (by means of Level Set functions) was applied on brain MR images of patients to obtain clinical target volumes.…”

Section: Related Workmentioning

confidence: 99%

Gamma Knife treatment planning: MR brain tumor segmentation and volume measurement based on unsupervised Fuzzy C-Means clustering

Militello

Rundo

Vitabile

et al. 2015

Int. J. Imaging Syst. Technol.

View full text Add to dashboard Cite

Nowadays, radiation treatment is beginning to intensively use MRI thanks to its greater ability to discriminate healthy and diseased soft-tissues. Leksell Gamma Knife® is a radio-surgical device, used to treat different brain lesions, which are often inaccessible for conventional surgery, such as benign or malignant tumors. Currently, the target to be treated with radiation therapy is contoured with slice-by-slice manual segmentation on MR datasets. This approach makes the segmentation procedure time consuming and operator-dependent. The repeatability of the tumor boundary delineation may be ensured only by using automatic or semiautomatic methods, supporting clinicians in the treatment planning phase. This article proposes a semiautomatic segmentation method, based on the unsupervised Fuzzy C-Means clustering algorithm. Our approach helps segment the target and automatically calculates the lesion volume. To evaluate the performance of the proposed approach, segmentation tests on 15 MR datasets were performed, using both area-based and distance-based metrics, obtaining the following average values: Similarity Index=95.59%, Jaccard Index=91.86%, Sensitivity=97.39%, Specificity=94.30%, Mean Absolute Distance=0.246[pixels], Maximum Distance=1.050[pixels], and Hausdorff Distance=1.365[pixels]

show abstract

Section: Related Workmentioning

confidence: 99%

Gamma Knife treatment planning: MR brain tumor segmentation and volume measurement based on unsupervised Fuzzy C-Means clustering

Militello

Rundo

Vitabile

et al. 2015

Int. J. Imaging Syst. Technol.

View full text Add to dashboard Cite

show abstract

“…In clinical studies, current Response Assessment in Neurooncology (RANO) criterion simply uses a bidirectional measurement to determine tumour volume for assessing treatment response [4]. Although a full 3D volume measurement may provide a more accurate volume assessment, there is a need for accurate and fully automated methods since manual segmentation (region of interest drawing) around tumour margins on a slice-by-slice basis is time-consuming and can take 12 min or more per tumour, with semiautomatic methods taking 3–5 min [5, 6]. T2w/FLAIR images can also be useful to help define the target volumes for radiotherapy planning of high-grade gliomas [2, 5]; hence, an automated segmentation that is not subject to operator subjectivity may be beneficial [5].…”

Section: Introductionmentioning

confidence: 99%

“…Although a full 3D volume measurement may provide a more accurate volume assessment, there is a need for accurate and fully automated methods since manual segmentation (region of interest drawing) around tumour margins on a slice-by-slice basis is time-consuming and can take 12 min or more per tumour, with semiautomatic methods taking 3–5 min [5, 6]. T2w/FLAIR images can also be useful to help define the target volumes for radiotherapy planning of high-grade gliomas [2, 5]; hence, an automated segmentation that is not subject to operator subjectivity may be beneficial [5]. In this study, we have concentrated on developing and validating an automated method for a single MRI modality, FLAIR, that could be readily translated for clinical use.…”

Section: Introductionmentioning

confidence: 99%

Automated brain tumour detection and segmentation using superpixel-based extremely randomized trees in FLAIR MRI

et al. 2016

View full text Add to dashboard Cite

PurposeWe propose a fully automated method for detection and segmentation of the abnormal tissue associated with brain tumour (tumour core and oedema) from Fluid- Attenuated Inversion Recovery (FLAIR) Magnetic Resonance Imaging (MRI).MethodsThe method is based on superpixel technique and classification of each superpixel. A number of novel image features including intensity-based, Gabor textons, fractal analysis and curvatures are calculated from each superpixel within the entire brain area in FLAIR MRI to ensure a robust classification. Extremely randomized trees (ERT) classifier is compared with support vector machine (SVM) to classify each superpixel into tumour and non-tumour.ResultsThe proposed method is evaluated on two datasets: (1) Our own clinical dataset: 19 MRI FLAIR images of patients with gliomas of grade II to IV, and (2) BRATS 2012 dataset: 30 FLAIR images with 10 low-grade and 20 high-grade gliomas. The experimental results demonstrate the high detection and segmentation performance of the proposed method using ERT classifier. For our own cohort, the average detection sensitivity, balanced error rate and the Dice overlap measure for the segmented tumour against the ground truth are 89.48 %, 6 % and 0.91, respectively, while, for the BRATS dataset, the corresponding evaluation results are 88.09 %, 6 % and 0.88, respectively.ConclusionsThis provides a close match to expert delineation across all grades of glioma, leading to a faster and more reproducible method of brain tumour detection and delineation to aid patient management.

show abstract

“…Research on delineation of volume targets has been carried out using the active contour, and is only limited to the CTV delineation process. The results of the study showed a reasonable accuracy and showed that this algorithm has a great ability to determine CTV [3].…”

Section: Introductionmentioning

confidence: 76%

Delineation of Target Volume for Brain Tumors in Radiotherapy Using Active Contour Segmentation and Simpson Integration

Fitriyah

Setiawati

Adi

2020

Int. J. Eng. Tech. Mgmt. Res.

View full text Add to dashboard Cite

This research conducts the delineation process for a brain tumor on the radio therapy. Delineation means the imaging process of target volume that is conducted in the beginning of Treatment Planning System (TPS). Target volume consists of gross tumor volume (GTV), clinical target volume (CTV), planning target volume (PTV), and Organ at Risk (OAR). This research aims to delineate target volume and measure the target volume axial brain tumor. The images that are used are Magnetic Resonance Imaging (MRI) including axial, coronal, and sagittal slice. Evaluation is only done with one patient affected by a brain tumor. Delineation process is performed by the active contour segmentation method and volume calculations using Simpson integration method. The delineation of target volume is conducted by segmenting each slice of images moreover they are reconstructed until the three dimension of visualized target volume is obtained. Having done this process, target area and target volume calculations are conducted. This research results range error rate for target area is 0.0006 to 0.0059 and target volume is 0.0001 to 0.0013

show abstract

Magnetic Resonance Imaging-Based Target Volume Delineation in Radiation Therapy Treatment Planning for Brain Tumors Using Localized Region-Based Active Contour

Cited by 25 publications

References 20 publications

Gamma Knife treatment planning: MR brain tumor segmentation and volume measurement based on unsupervised Fuzzy C-Means clustering

Gamma Knife treatment planning: MR brain tumor segmentation and volume measurement based on unsupervised Fuzzy C-Means clustering

Automated brain tumour detection and segmentation using superpixel-based extremely randomized trees in FLAIR MRI

Delineation of Target Volume for Brain Tumors in Radiotherapy Using Active Contour Segmentation and Simpson Integration

Contact Info

Product

Resources

About