Enhancing organ at risk segmentation with improved deep neural networks

Ilkin Isler; Curtis Lisle; Justin Rineer; Patrick Kelly; Damla Turgut; Jacob Ricci; Ulas Bagci

doi:10.1117/12.2611498

4 April 2022 Enhancing organ at risk segmentation with improved deep neural networks

Ilkin Isler, Curtis Lisle, Justin Rineer, Patrick Kelly, Damla Turgut, Jacob Ricci, Ulas Bagci

Proceedings Volume 12032, Medical Imaging 2022: Image Processing; 1203233 (2022) https://doi.org/10.1117/12.2611498
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Conference Poster

Abstract

Organ at risk (OAR) segmentation is a crucial step for treatment planning and outcome determination in radiotherapy treatments of cancer patients. Several deep learning based segmentation algorithms have been developed in recent years, however, U-Net remains the de facto algorithm designed specifically for biomedical image segmentation and has spawned many variants with known weaknesses. In this study, our goal is to present simple architectural changes in U-Net to improve its accuracy and generalization properties. Unlike many other available studies evaluating their algorithms on single center data, we thoroughly evaluate several variations of U-Net as well as our proposed enhanced architecture on multiple data sets for an extensive and reliable study of the OAR segmentation problem. Our enhanced segmentation model includes (a)architectural changes in the loss function, (b)optimization framework, and (c)convolution type. Testing on three publicly available multi-object segmentation data sets, we achieved an average of 80% dice score compared to the baseline U-Net performance of 63%.

Citation Download Citation

Ilkin Isler, Curtis Lisle, Justin Rineer, Patrick Kelly, Damla Turgut, Jacob Ricci, and Ulas Bagci "Enhancing organ at risk segmentation with improved deep neural networks", Proc. SPIE 12032, Medical Imaging 2022: Image Processing, 1203233 (4 April 2022); https://doi.org/10.1117/12.2611498

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