Automatic FDG-PET-based tumor and metastatic lymph node segmentation in cervical cancer

TY - GEN

T1 - Automatic FDG-PET-based tumor and metastatic lymph node segmentation in cervical cancer

AU - Rodríguez Arbonès, Dídac

AU - Jensen, Henrik Grønholt

AU - Loft, Annika

AU - af Rosenschöld, Per Munck

AU - Hansen, Anders Elias

AU - Igel, Christian

AU - Darkner, Sune

PY - 2014

Y1 - 2014

N2 - 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.

AB - 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.

U2 - 10.1117/12.2042909

DO - 10.1117/12.2042909

M3 - Article in proceedings

T3 - Progress in Biomedical Optics and Imaging

BT - Medical Imaging 2014

A2 - Ourselin, Sebastian

A2 - Styner, Martin A.

PB - SPIE - International Society for Optical Engineering

T2 - Medical Imaging 2014

Y2 - 15 February 2014

ER -