A fully automatic method for biological target volume segmentation of brain metastases

Salvatore Vitabile, Alessandro Stefano, Orazio Gambino, Edoardo Ardizzone, Roberto Pirrone, Maria Carla Gilardi, Alessandro Stefano, Giorgio Russo, Massimo Ippolito, Davide D'Urso, Franco Marletta, Corrado D'Arrigo

Risultato della ricerca: Article

11 Citazioni (Scopus)

Abstract

Leksell Gamma Knife is a mini-invasive technique to obtain a complete destruction of cerebral lesions delivering a single high dose radiation beam. Positron Emission Tomography (PET) imaging is increasingly utilized for radiation treatment planning. Nevertheless, lesion volume delineation in PET datasets is challenging because of the low spatial resolution and high noise level of PET images. Nowadays, the biological target volume (BTV) is manually contoured on PET studies. This procedure is time expensive and operator-dependent. In this article, a fully automatic algorithm for the BTV delineation based on random walks (RW) on graphs is proposed. The results are compared with the outcomes of the original RW method, 40% thresholding method, region growing method, and fuzzy c-means clustering method. To validate the effectiveness of the proposed approach in a clinical environment, BTV segmentation on 18 patients with cerebral metastases is performed. Experimental results show that the segmentation algorithm is accurate and has real-time performance satisfying the physician requirements in a radiotherapy environment.
Lingua originaleEnglish
pagine (da-a)29-37
Numero di pagine9
RivistaInternational Journal of Imaging Systems and Technology
Volume26
Stato di pubblicazionePublished - 2016

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Positron emission tomography
Brain
Radiotherapy
Dosimetry
Imaging techniques
Radiation
Planning

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Software
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

Cita questo

A fully automatic method for biological target volume segmentation of brain metastases. / Vitabile, Salvatore; Stefano, Alessandro; Gambino, Orazio; Ardizzone, Edoardo; Pirrone, Roberto; Gilardi, Maria Carla; Stefano, Alessandro; Russo, Giorgio; Ippolito, Massimo; D'Urso, Davide; Marletta, Franco; D'Arrigo, Corrado.

In: International Journal of Imaging Systems and Technology, Vol. 26, 2016, pag. 29-37.

Risultato della ricerca: Article

Vitabile, Salvatore ; Stefano, Alessandro ; Gambino, Orazio ; Ardizzone, Edoardo ; Pirrone, Roberto ; Gilardi, Maria Carla ; Stefano, Alessandro ; Russo, Giorgio ; Ippolito, Massimo ; D'Urso, Davide ; Marletta, Franco ; D'Arrigo, Corrado. / A fully automatic method for biological target volume segmentation of brain metastases. In: International Journal of Imaging Systems and Technology. 2016 ; Vol. 26. pagg. 29-37.
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abstract = "Leksell Gamma Knife is a mini-invasive technique to obtain a complete destruction of cerebral lesions delivering a single high dose radiation beam. Positron Emission Tomography (PET) imaging is increasingly utilized for radiation treatment planning. Nevertheless, lesion volume delineation in PET datasets is challenging because of the low spatial resolution and high noise level of PET images. Nowadays, the biological target volume (BTV) is manually contoured on PET studies. This procedure is time expensive and operator-dependent. In this article, a fully automatic algorithm for the BTV delineation based on random walks (RW) on graphs is proposed. The results are compared with the outcomes of the original RW method, 40{\%} thresholding method, region growing method, and fuzzy c-means clustering method. To validate the effectiveness of the proposed approach in a clinical environment, BTV segmentation on 18 patients with cerebral metastases is performed. Experimental results show that the segmentation algorithm is accurate and has real-time performance satisfying the physician requirements in a radiotherapy environment.",
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T1 - A fully automatic method for biological target volume segmentation of brain metastases

AU - Vitabile, Salvatore

AU - Stefano, Alessandro

AU - Gambino, Orazio

AU - Ardizzone, Edoardo

AU - Pirrone, Roberto

AU - Gilardi, Maria Carla

AU - Stefano, Alessandro

AU - Russo, Giorgio

AU - Ippolito, Massimo

AU - D'Urso, Davide

AU - Marletta, Franco

AU - D'Arrigo, Corrado

PY - 2016

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N2 - Leksell Gamma Knife is a mini-invasive technique to obtain a complete destruction of cerebral lesions delivering a single high dose radiation beam. Positron Emission Tomography (PET) imaging is increasingly utilized for radiation treatment planning. Nevertheless, lesion volume delineation in PET datasets is challenging because of the low spatial resolution and high noise level of PET images. Nowadays, the biological target volume (BTV) is manually contoured on PET studies. This procedure is time expensive and operator-dependent. In this article, a fully automatic algorithm for the BTV delineation based on random walks (RW) on graphs is proposed. The results are compared with the outcomes of the original RW method, 40% thresholding method, region growing method, and fuzzy c-means clustering method. To validate the effectiveness of the proposed approach in a clinical environment, BTV segmentation on 18 patients with cerebral metastases is performed. Experimental results show that the segmentation algorithm is accurate and has real-time performance satisfying the physician requirements in a radiotherapy environment.

AB - Leksell Gamma Knife is a mini-invasive technique to obtain a complete destruction of cerebral lesions delivering a single high dose radiation beam. Positron Emission Tomography (PET) imaging is increasingly utilized for radiation treatment planning. Nevertheless, lesion volume delineation in PET datasets is challenging because of the low spatial resolution and high noise level of PET images. Nowadays, the biological target volume (BTV) is manually contoured on PET studies. This procedure is time expensive and operator-dependent. In this article, a fully automatic algorithm for the BTV delineation based on random walks (RW) on graphs is proposed. The results are compared with the outcomes of the original RW method, 40% thresholding method, region growing method, and fuzzy c-means clustering method. To validate the effectiveness of the proposed approach in a clinical environment, BTV segmentation on 18 patients with cerebral metastases is performed. Experimental results show that the segmentation algorithm is accurate and has real-time performance satisfying the physician requirements in a radiotherapy environment.

KW - Optical and Magnetic Materials; 1707; Software

KW - biological target volume; cerebral tumors segmentation; gamma knife; PET imaging; random walk; Electrical and Electronic Engineering; Electronic

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