Evaluation of manual and automated approaches for segmentation and extraction of quantitative indices from [18F]FDG PET-CT images
- Author(s)
- Krokos, G; Kotwal, T; Malaih, A; Barrington, S; Jackson, P; Hicks, RJ; Marsden, PK; Fischer, BM;
- Details
- Publication Year 2024-01-05,Volume 10,Issue #2,Page 025007
- Journal Title
- Biomedical Physics & Engineering Express
- Publication Type
- Research article
- Abstract
- Utilisation of whole organ volumes to extract anatomical and functional information from computed tomography (CT) and positron emission tomography (PET) images may provide key information for the treatment and follow-up of cancer patients. However, manual organ segmentation, is laborious and time-consuming. In this study, a CT-based deep learning method and a multi-atlas method were evaluated for segmenting the liver and spleen on CT images to extract quantitative tracer information from Fluorine-18 fluorodeoxyglucose ([(18)F]FDG) PET images of 50 patients with advanced Hodgkin lymphoma (HL). Manual segmentation was used as the reference method. The two automatic methods were also compared with a manually defined volume of interest (VOI) within the organ, a technique commonly performed in clinical settings. Both automatic methods provided accurate CT segmentations, with the deep learning method outperforming the multi-atlas with a DICE coefficient of 0.93 ± 0.03 (mean ± standard deviation) in liver and 0.87 ± 0.17 in spleen compared to 0.87 ± 0.05 (liver) and 0.78 ± 0.11 (spleen) for the multi-atlas. Similarly, a mean relative error of -3.2% for the liver and -3.4% for the spleen across patients was found for the mean standardized uptake value (SUV(mean)) using the deep learning regions while the corresponding errors for the multi-atlas method were -4.7% and -9.2%, respectively. For the maximum SUV (SUV(max)), both methods resulted in higher than 20% overestimation due to the extension of organ boundaries to include neighbouring, high-uptake regions. The conservative VOI method which did not extend into neighbouring tissues, provided a more accurate SUV(max)estimate. In conclusion, the automatic, and particularly the deep learning method could be used to rapidly extract information of the SUV(mean)within the liver and spleen. However, activity from neighbouring organs and lesions can lead to high biases in SUV(max)and current practices of manually defining a volume of interest in the organ should be considered instead.
- Publisher
- IOP Publishing
- Keywords
- Humans; *Positron Emission Tomography Computed Tomography; *Fluorodeoxyglucose F18; Positron-Emission Tomography; Tomography, X-Ray Computed; Liver/diagnostic imaging; Ct; Pet; atlas; deep learning; organ segmentation
- Department(s)
- Physical Sciences
- Publisher's Version
- https://doi.org/10.1088/2057-1976/ad160e
- Open Access at Publisher's Site
https://doi.org/10.1088/2057-1976/ad160e- Terms of Use/Rights Notice
- Refer to copyright notice on published article.
Creation Date: 2024-02-20 12:40:58
Last Modified: 2024-02-20 12:45:33