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Proceedings Paper

Automatic FDG-PET-based tumor and metastatic lymph node segmentation in cervical cancer
Author(s): Dídac R. Arbonès; Henrik G. Jensen; Annika Loft; Per Munck af Rosenschöld; Anders Elias Hansen; Christian Igel; Sune Darkner
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Paper Abstract

Treatment of cervical cancer, one of the three most commonly diagnosed cancers worldwide, often relies on delineations of the tumour and metastases based on PET imaging using the contrast agent 18F-Fluorodeoxyglucose (FDG). We present a robust automatic algorithm for segmenting the gross tumour volume (GTV) and metastatic lymph nodes in such images. As the cervix is located next to the bladder and FDG is washed out through the urine, the PET-positive GTV and the bladder cannot be easily separated. Our processing pipeline starts with a histogram-based region of interest detection followed by level set segmentation. After that, morphological image operations combined with clustering, region growing, and nearest neighbour labelling allow to remove the bladder and to identify the tumour and metastatic lymph nodes. The proposed method was applied to 125 patients and no failure could be detected by visual inspection. We compared our segmentations with results from manual delineations of corresponding MR and CT images, showing that the detected GTV lays at least 97.5% within the MR/CT delineations. We conclude that the algorithm has a very high potential for substituting the tedious manual delineation of PET positive areas.

Paper Details

Date Published: 21 March 2014
PDF: 8 pages
Proc. SPIE 9034, Medical Imaging 2014: Image Processing, 903441 (21 March 2014); doi: 10.1117/12.2042909
Show Author Affiliations
Dídac R. Arbonès, Univ. of Copenhagen (Denmark)
Henrik G. Jensen, Univ. of Copenhagen (Denmark)
Annika Loft, Rigshospitalet (Denmark)
Per Munck af Rosenschöld, Rigshospitalet (Denmark)
Anders Elias Hansen, Univ. of Copenhagen (Denmark)
Christian Igel, Univ. of Copenhagen (Denmark)
Sune Darkner, Univ. of Copenhagen (Denmark)


Published in SPIE Proceedings Vol. 9034:
Medical Imaging 2014: Image Processing
Sebastien Ourselin; Martin A. Styner, Editor(s)

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