Abstract
Purpose: In this work we address image segmentation within dosimetry using deep learning and make three main contributions: a) to extend and op- timize the architecture of an existing Convolutional Neural Network (CNN) in order to obtain a fast, robust and accurate Computed Tomography (CT) based organ segmentation method for kidneys and livers; b) to train the CNN with an inhomogeneous set of CT scans and validate the CNN for daily dosimetry; c) to evaluate dosimetry results obtained using automated organ segmentation in comparison to manual segmentation done by two independent experts. Methods: We adapted a performant deep learning approach using CT-images to calculate organ boundaries with sufficiently high and adequate accuracy and processing time. The segmented organs were consequently used as binary masks for further convolution with a point spread function to retrieve the ac- tivity values from quantitatively reconstructed SPECT images for ”volumet- ric”/3D dosimetry. The retrieved activities were used to perform dosimetry calculations considering the kidneys as source organ. Results: The computational expenses of the algorithm was adequate enough to be used in clinical daily routine, required minimum pre-processing and per- formed within an acceptable accuracy of 93 . 4% for liver segmentation and of 94 . 1% for kidney segmentation. Additionally, kidney self-absorbed doses calcu- lated using automated segmentation differed 6 . 3% from dosimetries performed by two medical physicists in 8 patients. Conclusion: The proposed approach may accelerate volumetric dosimetry of kidneys in molecular radiotherapy with 177Lu-labelled radio-pharmaceuticals such as 177Lu-DOTATOC. However, even though a fully automated segmen- tation methodology based on CT images accelerates the organ segmentation and performs with high accuracy, it does not remove the need for supervision and corrections by experts, mostly due to misalignments in the co-registration between SPECT and CT images.Trial registration: EudraCT, 2016-001897-13. Registered 26.04.2016, www.clinicaltrialsregister.eu/ctr-search/search?query=2016-001897-13
Boosting radiotherapy dose calculation accuracy with deep learning
