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Head and Neck Tumour Segmentation in PET Images: Performance Evaluation of 3D U-Net with Maximum Voting-Based Surrogate Ground Truth

2025-03-26

Abstract excerpt

<title>Abstract</title> <p>Accurate segmentation of head and neck tumours in PET images is critical for effective treatment planning, disease progression monitoring, and radiotherapy. However, achieving reliable ground truth data remains challenging due to inter- and intra-observer variability. The U-Net, a Deep Convolutional Neural Network (DCNN), has demonstrated strong potential for automated segmentation, yet...

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Literature Corpus work
502eb891-c96d-52f9-af37-84bd3b98575f
DOI
10.21203/rs.3.rs-6198985/v1
Open publication

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Head and Neck Tumour Segmentation in PET Images: Performance Evaluation of 3D U-Net with Maximum Voting-Based Surrogate Ground TruthDOI 10.21203/rs.3.rs-6198985/v1
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