TY - GEN

T1 - Parotid gland segmentation with nnU-Net

T2 - Medical Imaging 2022: Image Processing

AU - Podobnik, Gašper

AU - Strojan, Primož

AU - Peterlin, Primož

AU - Ibragimov, Bulat

AU - Vrtovec, Tomaž

N1 - Publisher Copyright: © 2022 SPIE

PY - 2022

Y1 - 2022

N2 - Head and neck cancer is the sixth most common form of cancer in the world population. A commonly used treatment is radiotherapy, which requires physicians to first segment organs at risk (OARs) and tumors in computed tomography images, which is a laborious and time-consuming process. Therefore, a lot of research is being done to develop automatic methods for OAR segmentation. In this paper, we present the results of parotid gland segmentation with nnU-Net using data from two public datasets (Head-Neck-Radiomics-HN1 and PDDCA) and one private dataset acquired at the local hospital. To simulate a possible model deployment scenario, the first model was trained only on publicly available datasets and evaluated on the private dataset, and then compared to the second model that was trained on the same data with additional 10 images from the private dataset. We enrich the interpretation of the results with the comparison among different datasets and among delineations generated with a deep learning model against the delineations of a junior and senior expert that are available for our private dataset. Significant differences were observed among model performance on different datasets, but not among different observers. The performance of nnU-Net on the PDDCA dataset is on par with the state-of-the-art results reported in the literature. Also, the method performed very well compared to the inter-observer variability calculated on our private dataset.

AB - Head and neck cancer is the sixth most common form of cancer in the world population. A commonly used treatment is radiotherapy, which requires physicians to first segment organs at risk (OARs) and tumors in computed tomography images, which is a laborious and time-consuming process. Therefore, a lot of research is being done to develop automatic methods for OAR segmentation. In this paper, we present the results of parotid gland segmentation with nnU-Net using data from two public datasets (Head-Neck-Radiomics-HN1 and PDDCA) and one private dataset acquired at the local hospital. To simulate a possible model deployment scenario, the first model was trained only on publicly available datasets and evaluated on the private dataset, and then compared to the second model that was trained on the same data with additional 10 images from the private dataset. We enrich the interpretation of the results with the comparison among different datasets and among delineations generated with a deep learning model against the delineations of a junior and senior expert that are available for our private dataset. Significant differences were observed among model performance on different datasets, but not among different observers. The performance of nnU-Net on the PDDCA dataset is on par with the state-of-the-art results reported in the literature. Also, the method performed very well compared to the inter-observer variability calculated on our private dataset.

KW - Deep Learning

KW - Head and Neck Cancer

KW - Inter-observer Variability

KW - nnU-Net

KW - Organs at Risk

KW - Parotid Glands

KW - Segmentation

U2 - 10.1117/12.2609406

DO - 10.1117/12.2609406

M3 - Article in proceedings

AN - SCOPUS:85131917270

T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE

SP - 1

EP - 8

BT - Medical Imaging 2022

A2 - Colliot, Olivier

A2 - Isgum, Ivana

A2 - Landman, Bennett A.

A2 - Loew, Murray H.

PB - SPIE

Y2 - 21 March 2021 through 27 March 2021

ER -