TY - GEN
T1 - Stacking Feature Maps of Multi-scaled Medical Images in U-Net for 3D Head and Neck Tumor Segmentation
AU - Shi, Yaying
AU - Zhang, Xiaodong
AU - Yan, Yonghong
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - Machine learning, especially deep learning, has achieved state-of-the-art performance on various computer vision tasks. For computer vision tasks in the medical domain, it remains as challenging tasks since medical data is heterogeneous, multi-level, and multi-scale. Head and Neck Tumor Segmentation Challenge (HECKTOR) provides a platform to apply machine learning techniques to the medical image domain. HECKTOR 2022 provides positron emission tomography/computed tomography (PET/CT) images which includes useful metabolic and anatomical information to sufficiently make an accurate tumor segmentation. In this paper, we proposed a stacked-multi-scaled medical image segmentation framework to automatically segment the Head and Neck tumor using PET/CT images. The main idea of our network was to generate various low-resolution feature maps of PET/CT images to make a better contour of Head and Neck tumors. We used multi-scaled PET/CT images as inputs, and stacked different intermediate feature maps by resolution for a better inference result. In addition, we evaluated our model on the HECKTOR challenge test dataset. Overall, we achieved a 0.69786, 0.66730 mean Dice score on GTVp and GTVn respectively. Our team’s name is HPCAS.
AB - Machine learning, especially deep learning, has achieved state-of-the-art performance on various computer vision tasks. For computer vision tasks in the medical domain, it remains as challenging tasks since medical data is heterogeneous, multi-level, and multi-scale. Head and Neck Tumor Segmentation Challenge (HECKTOR) provides a platform to apply machine learning techniques to the medical image domain. HECKTOR 2022 provides positron emission tomography/computed tomography (PET/CT) images which includes useful metabolic and anatomical information to sufficiently make an accurate tumor segmentation. In this paper, we proposed a stacked-multi-scaled medical image segmentation framework to automatically segment the Head and Neck tumor using PET/CT images. The main idea of our network was to generate various low-resolution feature maps of PET/CT images to make a better contour of Head and Neck tumors. We used multi-scaled PET/CT images as inputs, and stacked different intermediate feature maps by resolution for a better inference result. In addition, we evaluated our model on the HECKTOR challenge test dataset. Overall, we achieved a 0.69786, 0.66730 mean Dice score on GTVp and GTVn respectively. Our team’s name is HPCAS.
KW - Convolutional neural network
KW - Deep learning
KW - HECKTOR challenge
KW - Machine learning
KW - Medical image segmentation
UR - http://www.scopus.com/inward/record.url?scp=85151056898&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85151056898&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-27420-6_8
DO - 10.1007/978-3-031-27420-6_8
M3 - Conference contribution
AN - SCOPUS:85151056898
SN - 9783031274190
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 77
EP - 85
BT - Head and Neck Tumor Segmentation and Outcome Prediction - 3rd Challenge, HECKTOR 2022, Held in Conjunction with MICCAI 2022, Proceedings
A2 - Andrearczyk, Vincent
A2 - Oreiller, Valentin
A2 - Depeursinge, Adrien
A2 - Hatt, Mathieu
PB - Springer Science and Business Media Deutschland GmbH
T2 - 3rd 3D Head and Neck Tumor Segmentation in PET/CT Challenge, HECKTOR 2022, held in Conjunction with the 25th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2022
Y2 - 22 September 2022 through 22 September 2022
ER -