Segmenting lung tumors on longitudinal imaging studies via a patient-specific adaptive convolutional neural network.

PURPOSE: To design a deep learning algorithm that automatically delineates lung tumors seen on weekly magnetic resonance imaging (MRI) scans acquired during radiotherapy and facilitates the analysis of geometric tumor changes.
METHODS: This longitudinal imaging study comprised 9 lung cancer patients who had 6-7 weekly T2-weighted MRI scans during radiotherapy. Tumors on all scans were manually contoured as the ground truth. Meanwhile, a patient-specific adaptive convolutional neural network (A-net) was developed to simulate the workflow of adaptive radiotherapy and to utilize past weekly MRI and tumor contours to segment tumors on the current weekly MRI. To augment the training data, each voxel inside the volume of interest was expanded to a 3 × 3 cm patch as the input, whereas the classification of the corresponding patch, background or tumor, was the output. Training was updated weekly to incorporate the latest MRI scan. For comparison, a population-based neural network was implemented, trained, and validated on the leave-one-out scheme. Both algorithms were evaluated by their precision, DICE coefficient, and root mean square surface distance between the manual and computerized segmentations.
RESULTS: Training of A-net converged well within 2 h of computations on a computer cluster. A-net segmented the weekly MR with a precision, DICE, and root mean square surface distance of 0.81 ± 0.10, 0.82 ± 0.10, and 2.4 ± 1.4 mm, and outperformed the population-based algorithm with 0.63 ± 0.21, 0.64 ± 0.19, and 4.1 ± 3.0 mm, respectively.
CONCLUSION: A-net can be feasibly integrated into the clinical workflow of a longitudinal imaging study and become a valuable tool to facilitate decision- making in adaptive radiotherapy.