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A 3D deep convolutional neural network approach for the automated measurement of cerebellum tracer uptake in FDG PET‐CT scans
Author(s) -
Xiong Xiaofan,
Linhardt Timothy J.,
Liu Weiren,
Smith Brian J.,
Sun Wenqing,
Bauer Christian,
Sunderland John J.,
Graham Michael M.,
Buatti John M.,
Beichel Reinhard R.
Publication year - 2020
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1002/mp.13970
Subject(s) - convolutional neural network , sørensen–dice coefficient , standardized uptake value , segmentation , positron emission tomography , artificial intelligence , computer science , nuclear medicine , pattern recognition (psychology) , pet ct , pearson product moment correlation coefficient , artificial neural network , coefficient of variation , image segmentation , mathematics , statistics , medicine
Purpose The purpose of this work was to assess the potential of deep convolutional neural networks in automated measurement of cerebellum tracer uptake in F‐18 fluorodeoxyglucose (FDG) positron emission tomography (PET) scans. Methods Three different three‐dimensional (3D) convolutional neural network architectures (U‐Net, V‐Net, and modified U‐Net) were implemented and compared regarding their performance in 3D cerebellum segmentation in FDG PET scans. For network training and testing, 134 PET scans with corresponding manual volumetric segmentations were utilized. For segmentation performance assessment, a fivefold cross‐validation was used, and the Dice coefficient as well as signed and unsigned distance errors were calculated. In addition, standardized uptake value (SUV) uptake measurement performance was assessed by means of a statistical comparison to an independent reference standard. Furthermore, a comparison to a previously reported active‐shape‐model‐based approach was performed. Results Out of the three convolutional neural networks investigated, the modified U‐Net showed significantly better segmentation performance. It achieved a Dice coefficient of 0.911 ± 0.026, a signed distance error of 0.220 ± 0.103 mm, and an unsigned distance error of 1.048 ± 0.340 mm. When compared to the independent reference standard, SUV uptake measurements produced with the modified U‐Net showed no significant error in slope and intercept. The estimated reduction in total SUV measurement error was 95.1%. Conclusions The presented work demonstrates the potential of deep convolutional neural networks in automated SUV measurement of reference regions. While it focuses on the cerebellum, utilized methods can be generalized to other reference regions like the liver or aortic arch. Future work will focus on combining lesion and reference region analysis into one approach.