The use of positron emission tomography (PET) within radiotherapy treatment planning requires the availability of reliable and accurate segmentation tools. PET automatic segmentation (PET-AS) methods have been recommended for the delineation of tumors, but there is still a lack of thorough validation and cross-comparison of such methods using clinically relevant data. In particular, studies validating PET segmentation tools mainly use phantoms with thick plastic walls inserts of simple spherical geometry and have not specifically investigated the effect of the target object geometry on the delineation accuracy. Our work therefore aimed at generating clinically realistic data using nonspherical thin-wall plastic inserts, for the evaluation and comparison of a set of eight promising PET-AS approaches.
Sixteen nonspherical inserts were manufactured with a plastic wall of 0.18 mm and scanned within a custom plastic phantom. These included ellipsoids and toroids derived with different volumes, as well as tubes, pear- and drop-shaped inserts with different aspect ratios. A set of six spheres of volumes ranging from 0.5 to 102 ml was used for a baseline study. A selection of eight PET-AS methods, written in house, was applied to the images obtained. The methods represented promising segmentation approaches such as adaptive iterative thresholding, region-growing, clustering and gradient-based schemes. The delineation accuracy was measured in terms of overlap with the computed tomography reference contour, using the dice similarity coefficient (DSC), and error in dimensions.
The delineation accuracy was lower for nonspherical inserts than for spheres of the same volume in 88% cases. Slice-by-slice gradient-based methods, showed particularly lower DSC for tori (DSC < 0.5), caused by a failure to recover the object geometry. The region-growing method reached high levels of accuracy for most inserts (DSC > 0.76 except for tori) but showed the largest errors in the recovery of pears and drops dimensions (higher than 10% and 30% of the true length, respectively). Large errors were visible for one of the gradient-based contouring methods when delineating drop-shaped inserts. Low DSC due to systematic underestimation of the volumes was observed for our fuzzy clustering method when using nonspherical inserts. The adaptive iterative thresholding method produced the highest DSC score on our nonspherical dataset (DSC > 0.83, except for tori) and showed robustness to the insert geometry.
This study investigated the accuracy of eight PET-AS methods for the delineation of objects with a range of nonspherical geometries. The authors’ results confirmed the robustness of some segmentation approaches, but also showed the weaknesses of some of the other methods implemented, which were not observed with spherical inserts. This work therefore highlights the importance of using a variety of thin-wall inserts with complex geometries for the validation of PET-AS methods and provided useful information for further development of the methods tested.
This work was carried out as part of the POSITIVE project (Optimization of positron Emission Tomography based Target Volume Delineation in Head and Neck Radiotherapy, REC No. 12/WA/0083), which is funded through Cancer Research Wales Grant No. 7061. The authors wish to thank Dr. V. Jayaprakasam and Mr. A. Edwards for their input in designing and manufacturing the nonspherical inserts. There are no conflicts of interest regarding this work.
II. MATERIALS AND METHODS
II.A. Generation of PET test inserts
II.A.1. Design and manufacturing of test inserts
II.A.2. Quality assessment of test inserts
II.B. Evaluation of PET-AS methods
II.B.1. Development of methods
II.B.2. Image acquisition
II.B.3. Baseline study with spherical inserts
II.B.4. Evaluation of the segmentation on complex test inserts
III.A. Generation of PET test inserts
III.B. Evaluation of PET-AS methods
III.B.1. Baseline study with spherical inserts
III.B.2. Evaluation of the segmentation of nonspherical inserts
- Medical imaging
- Positron emission tomography
- Cluster analysis
- Medical image segmentation
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