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Learning Image Representations for Content Based Image Retrieval of Radiotherapy Treatment Plans by Huang, Charles; Vasudevan, Varun; Pastor-Serrano, Oscar; Islam, Md Tauhidul; Nomura, Yusuke; Dubrowski, Piotr; Wang, Jen-Yeu; Schulz, Joseph B.; Yang, Yong; Xing, Lei is a scholarly article available to read on EtoBox.

What is Learning Image Representations for Content Based Image Retrieval of Radiotherapy Treatment Plans about?

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. Signific

Author

Huang, Charles; Vasudevan, Varun; Pastor-Serrano, Oscar; Islam, Md Tauhidul; Nomura, Yusuke; Dubrowski, Piotr; Wang, Jen-Yeu; Schulz, Joseph B.; Yang, Yong; Xing, Lei

Published

2022

Language

EN