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insikt - Tumor Segmentation
2023年オートペットチャレンジ：全身3D PET-CT腫瘍セグメンテーションのためのnnUNetベースのモデル開発
本稿では、全身PET-CTスキャンにおける腫瘍の自動セグメンテーションのためにnnUNetを応用し、その有効性と汎用性を高めるためのトレーニングおよび後処理戦略について検討した。
Application of nnUNet for Whole-Body Tumor Segmentation in 3D PET-CT Images from the AutoPET Challenge 2023
This research paper presents the application of a deep learning model, nnUNet, for the automated segmentation of tumors in whole-body PET-CT scans, aiming to improve the accuracy and efficiency of tumor identification in oncological practice.
Improving Mid-Treatment Head and Neck Tumor Segmentation in MRI Using Pre-Treatment Data and Gradient Maps
Integrating pre-treatment tumor information, specifically location and gradient maps, significantly improves the accuracy of deep learning-based segmentation of head and neck tumors in mid-treatment MRI scans.
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