[Target volume segmentation of PET images by an iterative method based on threshold value]
P Castro1, C Huerga2, L A Glaría3
1Servicio de Radiofísica y Protección Radiológica, Hospital Universitario Puerta de Hierro-Majadahonda, Majadahonda, Madrid, España.
Revista Espanola De Medicina Nuclear E Imagen Molecular
|April 8, 2014
Summary
This study introduces an automatic PET image segmentation method for tumor radiotherapy. The novel approach accurately delineates lesions, improving treatment planning with high precision.
Area of Science:
- Medical Imaging
- Radiotherapy
- Image Segmentation
Background:
- Accurate tumor delineation is crucial for effective radiotherapy.
- Current PET image segmentation methods can be limited by lesion size and background noise.
Purpose of the Study:
- To develop and validate an automatic segmentation method for PET images.
- To incorporate lesion size and background influence into the segmentation process.
Main Methods:
- Developed an iterative thresholding method based on PET phantom studies.
- Normalized optimal threshold values to background and adjusted using regression analysis.
- Validated the method on phantom data and retrospectively on oncology patient scans.
Main Results:
- The segmentation method showed linear dependence with signal-to-background ratio (SBR) and inverse proportionality with lesion volume.
- Volume deviations were under 10% compared to real and CT volumes for lesions > 0.6 ml.
Conclusions:
- The proposed automatic segmentation method is simple, reliable, and suitable for clinical radiotherapy treatment planning.
- Achieves precision close to the resolution limits of PET imaging.
