WE‐C‐330A‐03: Seed Segmentation in C‐Arm Fluoroscopy for Brachytherapy Implant Reconstruction

S. Vikal, A. Jain, A. Deguet, Danny Y Song, G. Fichtinger

Research output: Contribution to journalArticle

Abstract

Purpose: Intra‐operative dosimetry in prostate brachytherapy critically depends on discerning the 3‐D locations of implanted seeds. The accuracy of 3‐D seed reconstruction step is, in turn, limited by the accuracy with which the position and orientation of individual implanted seed in the fluoroscopic images can be found. A method for robustly segmenting the seeds in fluoroscopic images is proposed here. Methods and Materials: The process of determining the locations and orientations of implanted seeds is sub‐divided into three main steps. In the first step, the image is segmented by shape‐size based morphological approach to eliminate background noise and do away with non‐uniform brightness of the image, to get seed‐like regions. These regions are either single seeds or overlapping multiple seed clusters. In the second step, the regions are analyzed and classified definitively, in a two‐phase statistical process coupled with information extraction from original intensity image, into two classes: single seed and overlapping multiple seed cluster. In the third step, the region belonging to overlapping multiple seed cluster is resolved into its constituent individual seeds through a simple and novel technique. Results: The proposed algorithm was tested on a set of ten clinical fluoroscopic images. The algorithm correctly determines the seeds with overall average of 99.57%. The clusters are not correctly resolved only in two images (2 clusters each, 1.7% and 1.6% of total seeds in respective implants). One false positive (noise labeled as seed) each is reported in two images, both the cases being where the tip of catheter appears to be of the size and shape of seed. Conclusions: The algorithm builds on an existing framework of morphological processing and provides further improvements in classification and cluster resolution. The algorithm appears to be robust and accurate despite the poor resolution of clinical images.

Original languageEnglish (US)
Pages (from-to)2229
Number of pages1
JournalMedical Physics
Volume33
Issue number6
DOIs
StatePublished - 2006

Fingerprint

Fluoroscopy
Brachytherapy
Seeds
Noise
Information Storage and Retrieval
Prostate

ASJC Scopus subject areas

  • Biophysics
  • Radiology Nuclear Medicine and imaging

Cite this

WE‐C‐330A‐03 : Seed Segmentation in C‐Arm Fluoroscopy for Brachytherapy Implant Reconstruction. / Vikal, S.; Jain, A.; Deguet, A.; Song, Danny Y; Fichtinger, G.

In: Medical Physics, Vol. 33, No. 6, 2006, p. 2229.

Research output: Contribution to journalArticle

Vikal, S. ; Jain, A. ; Deguet, A. ; Song, Danny Y ; Fichtinger, G. / WE‐C‐330A‐03 : Seed Segmentation in C‐Arm Fluoroscopy for Brachytherapy Implant Reconstruction. In: Medical Physics. 2006 ; Vol. 33, No. 6. pp. 2229.
@article{344d5d030cff478ba0aaf0dd7730d4bd,
title = "WE‐C‐330A‐03: Seed Segmentation in C‐Arm Fluoroscopy for Brachytherapy Implant Reconstruction",
abstract = "Purpose: Intra‐operative dosimetry in prostate brachytherapy critically depends on discerning the 3‐D locations of implanted seeds. The accuracy of 3‐D seed reconstruction step is, in turn, limited by the accuracy with which the position and orientation of individual implanted seed in the fluoroscopic images can be found. A method for robustly segmenting the seeds in fluoroscopic images is proposed here. Methods and Materials: The process of determining the locations and orientations of implanted seeds is sub‐divided into three main steps. In the first step, the image is segmented by shape‐size based morphological approach to eliminate background noise and do away with non‐uniform brightness of the image, to get seed‐like regions. These regions are either single seeds or overlapping multiple seed clusters. In the second step, the regions are analyzed and classified definitively, in a two‐phase statistical process coupled with information extraction from original intensity image, into two classes: single seed and overlapping multiple seed cluster. In the third step, the region belonging to overlapping multiple seed cluster is resolved into its constituent individual seeds through a simple and novel technique. Results: The proposed algorithm was tested on a set of ten clinical fluoroscopic images. The algorithm correctly determines the seeds with overall average of 99.57{\%}. The clusters are not correctly resolved only in two images (2 clusters each, 1.7{\%} and 1.6{\%} of total seeds in respective implants). One false positive (noise labeled as seed) each is reported in two images, both the cases being where the tip of catheter appears to be of the size and shape of seed. Conclusions: The algorithm builds on an existing framework of morphological processing and provides further improvements in classification and cluster resolution. The algorithm appears to be robust and accurate despite the poor resolution of clinical images.",
author = "S. Vikal and A. Jain and A. Deguet and Song, {Danny Y} and G. Fichtinger",
year = "2006",
doi = "10.1118/1.2241679",
language = "English (US)",
volume = "33",
pages = "2229",
journal = "Medical Physics",
issn = "0094-2405",
publisher = "AAPM - American Association of Physicists in Medicine",
number = "6",

}

TY - JOUR

T1 - WE‐C‐330A‐03

T2 - Seed Segmentation in C‐Arm Fluoroscopy for Brachytherapy Implant Reconstruction

AU - Vikal, S.

AU - Jain, A.

AU - Deguet, A.

AU - Song, Danny Y

AU - Fichtinger, G.

PY - 2006

Y1 - 2006

N2 - Purpose: Intra‐operative dosimetry in prostate brachytherapy critically depends on discerning the 3‐D locations of implanted seeds. The accuracy of 3‐D seed reconstruction step is, in turn, limited by the accuracy with which the position and orientation of individual implanted seed in the fluoroscopic images can be found. A method for robustly segmenting the seeds in fluoroscopic images is proposed here. Methods and Materials: The process of determining the locations and orientations of implanted seeds is sub‐divided into three main steps. In the first step, the image is segmented by shape‐size based morphological approach to eliminate background noise and do away with non‐uniform brightness of the image, to get seed‐like regions. These regions are either single seeds or overlapping multiple seed clusters. In the second step, the regions are analyzed and classified definitively, in a two‐phase statistical process coupled with information extraction from original intensity image, into two classes: single seed and overlapping multiple seed cluster. In the third step, the region belonging to overlapping multiple seed cluster is resolved into its constituent individual seeds through a simple and novel technique. Results: The proposed algorithm was tested on a set of ten clinical fluoroscopic images. The algorithm correctly determines the seeds with overall average of 99.57%. The clusters are not correctly resolved only in two images (2 clusters each, 1.7% and 1.6% of total seeds in respective implants). One false positive (noise labeled as seed) each is reported in two images, both the cases being where the tip of catheter appears to be of the size and shape of seed. Conclusions: The algorithm builds on an existing framework of morphological processing and provides further improvements in classification and cluster resolution. The algorithm appears to be robust and accurate despite the poor resolution of clinical images.

AB - Purpose: Intra‐operative dosimetry in prostate brachytherapy critically depends on discerning the 3‐D locations of implanted seeds. The accuracy of 3‐D seed reconstruction step is, in turn, limited by the accuracy with which the position and orientation of individual implanted seed in the fluoroscopic images can be found. A method for robustly segmenting the seeds in fluoroscopic images is proposed here. Methods and Materials: The process of determining the locations and orientations of implanted seeds is sub‐divided into three main steps. In the first step, the image is segmented by shape‐size based morphological approach to eliminate background noise and do away with non‐uniform brightness of the image, to get seed‐like regions. These regions are either single seeds or overlapping multiple seed clusters. In the second step, the regions are analyzed and classified definitively, in a two‐phase statistical process coupled with information extraction from original intensity image, into two classes: single seed and overlapping multiple seed cluster. In the third step, the region belonging to overlapping multiple seed cluster is resolved into its constituent individual seeds through a simple and novel technique. Results: The proposed algorithm was tested on a set of ten clinical fluoroscopic images. The algorithm correctly determines the seeds with overall average of 99.57%. The clusters are not correctly resolved only in two images (2 clusters each, 1.7% and 1.6% of total seeds in respective implants). One false positive (noise labeled as seed) each is reported in two images, both the cases being where the tip of catheter appears to be of the size and shape of seed. Conclusions: The algorithm builds on an existing framework of morphological processing and provides further improvements in classification and cluster resolution. The algorithm appears to be robust and accurate despite the poor resolution of clinical images.

UR - http://www.scopus.com/inward/record.url?scp=84855777540&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84855777540&partnerID=8YFLogxK

U2 - 10.1118/1.2241679

DO - 10.1118/1.2241679

M3 - Article

AN - SCOPUS:84855777540

VL - 33

SP - 2229

JO - Medical Physics

JF - Medical Physics

SN - 0094-2405

IS - 6

ER -