首页|The Squeeze & Excitation Normalization Based nnU-Net for Segmenting Head & Neck Tumors

The Squeeze & Excitation Normalization Based nnU-Net for Segmenting Head & Neck Tumors

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Head and neck cancer is one of the most common malignancies in the world.We propose SE-nnU-Net by adapting SE(squeeze and excitation)normalization into nnU-Net,so as to segment head and neck tumors in PET/CT images by combining advantages of SE capturing features of interest regions and nnU-Net configuring itself for a specific task.The basic module referred to convolution-ReLU-SE is designed for SE-nnU-Net.In the encoder it is combined with residual structure while in the decoder without residual structure.The loss function combines Dice loss and Focal loss.The specific data preprocessing and augmentation techniques are developed,and specific network architecture is designed.Moreover,the deep supervised mechanism is introduced to calculate the loss function using the last four layers of the decoder of SE-nnU-Net.This SE-nnU-net is applied to HECKTOR 2020 and HECKTOR 2021 challenges,respectively,using different experimental design.The experimental results show that SE-nnU-Net for HECKTOR 2020 obtained 0.745,0.821,and 0.725 in terms of Dice,Precision,and Recall,respectively,while the SE-nnU-Net for HECKTOR 2021 obtains 0.778 and 3.088 in terms of Dice and median HD95,respectively.This SE-nnU-Net for segmenting head and neck tumors can provide auxiliary opinions for doctors'diagnoses.

Head and neck tumorsPET/CT imagesImage segmentationSE-nnU-NetSqueeze and excita-tion normalization

Juanying XIE、Ying PENG、Mingzhao WANG

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School of Computer Science,Shaanxi Normal University,Xi'an 710119,China

National Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaFundamental Research Funds for the Central UniversitiesFundamental Research Funds for the Central Universities

620761591203101061673251GK202105003GK202207017

2024

10.23919/cje.2022.00.306

电子学报(英文)

电子学报(英文)

CSTPCDEI
ISSN：1022-4653
年,卷(期)：2024.33(3)