论文标题
3D PET/CT的自动头和颈部肿瘤分割
Automated head and neck tumor segmentation from 3D PET/CT
论文作者
论文摘要
头颈肿瘤分割挑战（Hecktor）2022为研究人员提供了一个平台，可以将其解决方案与3D CT和PET图像的肿瘤和淋巴结分割。在这项工作中，我们描述了hecktor 2022细分任务的解决方案。我们将所有图像重新样本为共同的分辨率，在头颈部和颈部区域周围的作物，并从Monai训练Segresnet语义分割网络。我们使用5倍的交叉验证来选择最佳模型检查点。最终提交是3次运行中的15个型号的合奏。我们的解决方案（NVAUTO团队名称）以0.78802的汇总骰子得分在Hecktor22挑战排行榜上获得第一名。
Head and neck tumor segmentation challenge (HECKTOR) 2022 offers a platform for researchers to compare their solutions to segmentation of tumors and lymph nodes from 3D CT and PET images. In this work, we describe our solution to HECKTOR 2022 segmentation task. We re-sample all images to a common resolution, crop around head and neck region, and train SegResNet semantic segmentation network from MONAI. We use 5-fold cross validation to select best model checkpoints. The final submission is an ensemble of 15 models from 3 runs. Our solution (team name NVAUTO) achieves the 1st place on the HECKTOR22 challenge leaderboard with an aggregated dice score of 0.78802.
