Abstract
BackgroundPET-based tumor delineation is an error prone and labor intensive part of image analysis. Especially for patients with advanced disease showing bulky tumor FDG load, segmentations are challenging. Reducing the amount of user-interaction in the segmentation might help to facilitate segmentation tasks especially when labeling bulky and complex tumors. Therefore, this study reports on segmentation workflows/strategies that may reduce the inter-observer variability for large tumors with complex shapes with different levels of user-interaction.MethodsTwenty PET images of bulky tumors were delineated independently by six observers using four strategies: (I) manual, (II) interactive threshold-based, (III) interactive threshold-based segmentation with the additional presentation of the PET-gradient image and (IV) the selection of the most reasonable result out of four established semi-automatic segmentation algorithms (Select-the-best approach). The segmentations were compared using Jaccard coefficients (JC) and percentage volume differences. To obtain a reference standard, a majority vote (MV) segmentation was calculated including all segmentations of experienced observers. Performed and MV segmentations were compared regarding positive predictive value (PPV), sensitivity (SE), and percentage volume differences.ResultsThe results show that with decreasing user-interaction the inter-observer variability decreases. JC values and percentage volume differences of Select-the-best and a workflow including gradient information were significantly better than the measurements of the other segmentation strategies (p-value<0.01). Interactive threshold-based and manual segmentations also result in significant lower and more variable PPV/SE values when compared with the MV segmentation.ConclusionsFDG PET segmentations of bulky tumors using strategies with lower user-interaction showed less inter-observer variability. None of the methods led to good results in all cases, but use of either the gradient or the Select-the-best workflow did outperform the other strategies tested and may be a good candidate for fast and reliable labeling of bulky and heterogeneous tumors.
Highlights
In oncology, Positron Emission Tomography combined with Computed Tomography (PET/ CT) using the tracer fluorodeoxyglucose (FDG) is important for cancer diagnosis [1,2,3]
To obtain a reference standard, a majority vote (MV) segmentation was calculated including all segmentations of experienced observers
Jaccard coefficients (JC) values and percentage volume differences of Select-the-best and a workflow including gradient information were significantly better than the measurements of the other
Summary
Positron Emission Tomography combined with Computed Tomography (PET/ CT) using the tracer fluorodeoxyglucose (FDG) is important for cancer diagnosis [1,2,3]. For patients with advanced stage cancer with bulky tumors, analysis and evaluation of these feature values can add valuable information and help to direct treatment. Since these features are highly sensitive to tumor delineation [5,7], a reliable and reproducible segmentation is essential. For this purpose, a segmentation strategy with low interobserver variability is important. For large tumors (metabolic active tumor volume (MATV) > 300mL) with irregular and complex shapes, a manual segmentation is very time consuming and prone to segmentation errors. This study reports on segmentation workflows/strategies that may reduce the inter-observer variability for large tumors with complex shapes with different levels of userinteraction
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