Abstract
In this study we have attempted to optimize a PET based adaptive threshold segmentation method for delineating small tumors, particularly in a background of high tracer activity. The metabolic nature of pituitary adenomas and the constraints of MRI imaging in the postoperative setting to delineate these tumors during radiosurgical procedures motivated us to develop this method. Phantom experiments were done to establish a relationship between the threshold required for segmenting the PET images and the target size and the activity concentration within the target in relation to its background. The threshold was developed from multiple linear regression of the experimental data optimized for tumor sizes less than 4 cm3. We validated our method against the phantom target volumes with measured target to background ratios ranging from 1.6 to 14.58. The method was tested on ten retrospective patients with residual growth hormone‐secreting pituitary adenomas that underwent radiosurgery and compared against the volumes delineated by manual method. The predicted volumes against the true volume of the phantom inserts gave a correlation coefficient of 99% (p<0.01). In the ten retrospective patients, the automatically segmented tumor volumes against volumes manually delineated by the clinicians had a correlation of 94% (p<0.01). This adaptive threshold segmentation showed promising results in delineating tumor volumes in pituitary adenomas planned for stereotactic radiosurgery, particularly in the postoperative setting where MR and CT images may be associated with artifacts, provided optimization experiment is carried out.PACS number: 87.57.nm, 87.57.uk
Highlights
Thomas et al.: Adaptive threshold segmentation from PET target delineating has been reported and variability was largely affected by the experience of the clinician
While magnetic resonance imaging (MRI) is the standard imaging modality used for these tumors, delineating tumors in a postsurgical setting is difficult due to artifacts from old blood, fat used for packing the sella, and haemostatic material.[6]. The metabolic nature of pituitary tumors makes them good candidates for functional imaging using FDG PET[7,8] and may be useful during radiosurgical procedures for pituitary adenomas given the constraints of MRI in the postoperative setting
Since we were delineating small tumors, fixed threshold was not an ideal option, given its own limitations.[26]. Since this data-driven adaptive threshold method is based on the simple principle that the required threshold for segmentation is inversely proportional to T/B, it is understood by an average clinical user than achieving an intuitive understanding of the mathematical models for clinical implementation
Summary
Thomas et al.: Adaptive threshold segmentation from PET target delineating has been reported and variability was largely affected by the experience of the clinician. This process is quite slow and has poor reproducibility.[1] PET images are susceptible to variations in window level settings,(2–4) and changing the intensity of the images or the color scale dramatically changes the perception of the volume of the target.[5] In spite of these problems with manual segmentation, it still remains the most common method of delineation, probably because it is very simple and does not need any optimization. There is a sharp dose falloff beyond the target, the need for precise definition of the target cannot be overemphasized
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