Xiaofan Xiong1, Timothy J Linhardt2, Weiren Liu3, Brian J Smith4, Wenqing Sun5, Christian Bauer2, John J Sunderland6, Michael M Graham6, John M Buatti5, Reinhard R Beichel2. 1. Department of Biomedical Engineering, The University of Iowa, Iowa City, IA, 52242, USA. 2. Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA, 52242, USA. 3. Roy J. and Lucille A. Carver College of Medicine, The University of Iowa, Iowa City, IA, 52242, USA. 4. Department of Biostatistics, The University of Iowa, Iowa City, IA, 52242, USA. 5. Department of Radiation Oncology, University of Iowa Hospitals and Clinics, Iowa City, IA, 52242, USA. 6. Department of Radiology, The University of Iowa, Iowa City, IA, 52242, USA.
Abstract
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.
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.
Authors: Geert Litjens; Thijs Kooi; Babak Ehteshami Bejnordi; Arnaud Arindra Adiyoso Setio; Francesco Ciompi; Mohsen Ghafoorian; Jeroen A W M van der Laak; Bram van Ginneken; Clara I Sánchez Journal: Med Image Anal Date: 2017-07-26 Impact factor: 8.545
Authors: Xiangyuan Ma; Lubomir M Hadjiiski; Jun Wei; Heang-Ping Chan; Kenny H Cha; Richard H Cohan; Elaine M Caoili; Ravi Samala; Chuan Zhou; Yao Lu Journal: Med Phys Date: 2019-02-28 Impact factor: 4.071
Authors: Reinhard R Beichel; Ethan J Ulrich; Brian J Smith; Christian Bauer; Bartley Brown; Thomas Casavant; John J Sunderland; Michael M Graham; John M Buatti Journal: PLoS One Date: 2019-04-19 Impact factor: 3.240
Authors: Daniel Gillett; Russell Senanayake; James MacFarlane; Merel van der Meulen; Olympia Koulouri; Andrew S Powlson; Rosy Crawford; Bethany Gillett; Nick Bird; Sarah Heard; Angelos Kolias; Richard Mannion; Luigi Aloj; Iosif A Mendichovszky; Heok Cheow; Waiel A Bashari; Mark Gurnell Journal: EJNMMI Res Date: 2022-05-07 Impact factor: 3.434