Three-dimensional prostate CT segmentation through fine-tuning of a pre-trained neural network using no reference labeling

Kayla Caughlin; Maysam Shahedi; Jonathan E. Shoag; Christopher Barbieri; Daniel Margolis; Baowei Fei

doi:10.1117/12.2581963

15 February 2021 Three-dimensional prostate CT segmentation through fine-tuning of a pre-trained neural network using no reference labeling

Kayla Caughlin, Maysam Shahedi, Jonathan E. Shoag, Christopher Barbieri, Daniel Margolis, Baowei Fei

Author Affiliations +

Proceedings Volume 11598, Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling; 115980L (2021) https://doi.org/10.1117/12.2581963
Event: SPIE Medical Imaging, 2021, Online Only

Abstract

Accurate segmentation of the prostate on computed tomography (CT) has many diagnostic and therapeutic applications. However, manual segmentation is time-consuming and suffers from high inter- and intra-observer variability. Computerassisted approaches are useful to speed up the process and increase the reproducibility of the segmentation. Deep learningbased segmentation methods have shown potential for quick and accurate segmentation of the prostate on CT images. However, difficulties in obtaining manual, expert segmentations on a large quantity of images limit further progress. Thus, we proposed an approach to train a base model on a small, manually-labeled dataset and fine-tuned the model using unannotated images from a large dataset without any manual segmentation. The datasets used for pre-training and finetuning the base model have been acquired in different centers with different CT scanners and imaging parameters. Our fine-tuning method increased the validation and testing Dice scores. A paired, two-tailed t-test shows a significant change in test score (p = 0.017) demonstrating that unannotated images can be used to increase the performance of automated segmentation models.

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Kayla Caughlin, Maysam Shahedi, Jonathan E. Shoag, Christopher Barbieri, Daniel Margolis, and Baowei Fei "Three-dimensional prostate CT segmentation through fine-tuning of a pre-trained neural network using no reference labeling", Proc. SPIE 11598, Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling, 115980L (15 February 2021); https://doi.org/10.1117/12.2581963

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