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Deep Learning Segmentation of Cardiac Substructures in Breast Cancer Radiotherapy Patients

X Jin*, J Hilliard, J Dise, J Kavanaugh, I Zoberi, M Thomas, C Robinson, G Hugo, Washington University School of Medicine, St. Louis, MO

Presentations

(Sunday, 7/12/2020)   [Eastern Time (GMT-4)]

Room: AAPM ePoster Library

Purpose: To develop and evaluate deep learning (DL) based auto-segmentation of cardiac substructures from non-contrast planning CT images in patients undergoing breast cancer radiotherapy.


Methods: Nine substructures including Aortic Valve(AV), Left Anterior Descending(LAD), Tricuspid Valve(TV), Mitral Valve(MV), Pulmonic Valve(PV), Right Atrium(RA), Right Ventricle(RV), Left Atrium(LA) and Left Ventricle(LV) were manually delineated by physician on non-contrast CT images of 50 patients with breast cancer. The image/label pairs of 30 subjects were used to train a 3D deep neural network while the remaining 20 were used for validation and testing. Our custom-designed network was based on a ResNet design. Random non-rigid and rigid transformations were used to augment the dataset. We investigated multiple loss functions including Dice Similarity Coefficient (DCS), Cross-entropy (CE), Euclidean loss as well as combinations of them. Without post-processing, the predicted label maps were compared to ground truth label via DSC and mean and 90th percentile symmetric surface distance (90th-SSD).


Results: When using combination of CE and DSC (CE-D) as loss function, DL achieved a mean DSC=0.80±0.03 for chambers and a mean DSC=0.53±0.06 for smaller structures (AV, LAD, PV, MV, TV). The mean and 90th-SSD for chambers is 1.91±0.26 mm and 4.60±0.69 mm and is 2.15±0.75 mm and 5.10±1.44 mm for smaller structures. Modified Dice (MD) loss function performs best in segmenting chambers when evaluating with 90th-SSD (4.45±80 mm), but worse in segmenting LAD (90th-SSD=8.66±2.88 mm), while CE-D as loss function provides best segmentation on smaller structures. The execution time for segmenting each patient is around 2.1s.


Conclusion: DL provides a fast and accurate segmentation of large cardiac substructures in non-contrast CT images, although performance for smaller structures was dependent on choice of loss function. Evaluation of clinical acceptability and integration into clinical workflow are pending.

Download ePoster [PDF]

Funding Support, Disclosures, and Conflict of Interest: GH reports honoraria / travel costs, Varian; research grants: Varian Medical Systems, Viewray, Siemens. MAT reports honoraria, ViewRay. CGR reports stock / leadership, Radialogica; consulting: Varian, AstraZeneca, EMD Serono; research grants: Varian, Elekta, Merck; travel costs: Siemens. JK reports: research grants: Varian; travel costs and honoraria: Varian

Keywords

Segmentation, Image Processing, Heart

Taxonomy

IM/TH- Image Segmentation Techniques: Machine Learning

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