  Objective: Knowledge based planning (KBP) typically involves training an
end-to-end deep learning model to predict dose distributions. However, training
end-to-end methods may be associated with practical limitations due to the
limited size of medical datasets that are often used. To address these
limitations, we propose a content based image retrieval (CBIR) method for
retrieving dose distributions of previously planned patients based on
anatomical similarity. Approach: Our proposed CBIR method trains a
representation model that produces latent space embeddings of a patient's
anatomical information. The latent space embeddings of new patients are then
compared against those of previous patients in a database for image retrieval
of dose distributions. All source code for this project is available on github.
Main Results: The retrieval performance of various CBIR methods is evaluated on
a dataset consisting of both publicly available plans and clinical plans from
our institution. This study compares various encoding methods, ranging from
simple autoencoders to more recent Siamese networks like SimSiam, and the best
performance was observed for the multitask Siamese network. Significance:
Applying CBIR to inform subsequent treatment planning potentially addresses
many limitations associated with end-to-end KBP. Our current results
demonstrate that excellent image retrieval performance can be obtained through
slight changes to previously developed Siamese networks. We hope to integrate
CBIR into automated planning workflow in future works, potentially through
methods like the MetaPlanner framework.
