H Yao*, J Chang, J Baker, Northwell Health and Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Lake Success, New York

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PO-GePV-M-167 (Sunday, 7/25/2021)   [Eastern Time (GMT-4)]

Purpose: Create a 3D deep learning model capable of generating a Planning Target Volume (PTV) on the prostate and nodes volume with CT input images for radiotherapy treatment planning.

Methods: We anonymized and exported 265 CT images and PTV_4500 volumes of prostate cancer patients for this project. PTVs with 45 Gy were contoured by 7 MDs and reviewed to be clinically acceptable. Two groups were formed from 212 cases for training and 53 for testing. We developed a Pixel-Deconvolutional Network (PDN) model by tweaking the weighting parameters to minimize the dice loss function using the training cases. The performance of the auto-segmentation was analyzed through the similarity DICE and Average Hausdorff Distance (AHD) between manual (gold-standard) and auto segmented PTV_4500 (predicted). Given the restrictions of computing resources, all images required resizing to lower resolution initially as input for the network.

Results: The mean DICE/AHD scores with standard deviation (SD) for the testing set were (0.716±0.076) and (2.061±1.749 cm) for the CT image modality. If excluding one testing case with AHD > 10 cm, the mean and SD were (0.723±0.062) and (1.885±1.209 cm). The computation time for training was about 222 minutes.

Conclusion: A deep-learning network was developed to contour the PTV for radiotherapy of prostate with nodes to 45 Gy with simulation CT images. The predicted contours have a reasonable dice score and AHD value compared with the manual contouring. We believe increased contouring accuracy is possible through more curating of the training data.

ePosters

Keywords

Prostate Therapy, Segmentation, Image Analysis

Taxonomy

IM/TH- Image Segmentation Techniques: Modality: CT

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