arXiv 2411.147522024-11-22eess.IVcs.CVcs.LG

MRI引导放疗中头颈部肿瘤分割的nnUNet与MedNeXt对比分析

Comparative Analysis of nnUNet and MedNeXt for Head and Neck Tumor Segmentation in MRI-guided Radiotherapy

Sharif University of Technology（谢里夫理工大学）；Institute for AI in Medicine (IKIM), University Medicine Essen（埃森大学医学中心人工智能医学研究所）；Center Algoritmi / LASI, University of Minho（米尼奥大学Algoritmi中心/LASI）；University Hospital RWTH Aachen（亚琛工业大学附属医院）；Cancer Research Center Cologne Essen (CCCE), University Medicine Essen (AöR)（埃森大学医学中心（公法团体）科隆埃森癌症研究中心）；German Cancer Consortium (DKTK), Partner Site Essen（德国癌症联盟埃森合作站点）；TU Dortmund University（多特蒙德工业大学）

Nikoo Moradi, André Ferreira, Behrus Puladi, Jens Kleesiek, Emad Fatemizadeh, Gijs Luijten, Victor Alves, Jan Egger

备注：: 15 pages, 3 figures

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AI总结：

本文针对MRI引导放疗中头颈部肿瘤自动分割问题，采用nnUNet和MedNeXt模型，通过预训练、微调及多通道输入策略，在HNTS-MRG24挑战赛任务1中取得第一名（Dice 0.8254），任务2排名第八（0.7005）。

摘要：

放射治疗（RT）在头颈部癌症（HNC）治疗中至关重要，磁共振成像（MRI）引导的RT具有优越的软组织对比度和功能成像能力。然而，手动肿瘤分割耗时且复杂，因此仍然是一个挑战。在本研究中，我们以TUMOR团队的身份提交了针对HNTS-MRG24 MICCAI挑战赛的解决方案，该挑战赛聚焦于在放疗前（pre-RT）和放疗中（mid-RT）MRI图像中自动分割原发大体肿瘤体积（GTVp）和转移性淋巴结大体肿瘤体积（GTVn）。我们使用了HNTS-MRG2024数据集，该数据集包含150例诊断为HNC的患者的MRI扫描，包括原始和配准的放疗前及放疗中T2加权图像，以及对应的GTVp和GTVn分割掩码。我们采用了两种最先进的深度学习模型：nnUNet和MedNeXt。对于任务1，我们在放疗前配准图像和放疗中图像上预训练模型，随后在原始放疗前图像上进行微调。对于任务2，我们将配准的放疗前图像、配准的放疗前分割掩码和放疗中数据组合为多通道输入进行训练。我们的任务1解决方案在最终测试阶段以0.8254的聚合Dice相似系数获得第一名，任务2解决方案以0.7005的得分排名第八。所提出的解决方案已在GitHub仓库公开提供。

英文摘要：

Radiation therapy (RT) is essential in treating head and neck cancer (HNC), with magnetic resonance imaging(MRI)-guided RT offering superior soft tissue contrast and functional imaging. However, manual tumor segmentation is time-consuming and complex, and therfore remains a challenge. In this study, we present our solution as team TUMOR to the HNTS-MRG24 MICCAI Challenge which is focused on automated segmentation of primary gross tumor volumes (GTVp) and metastatic lymph node gross tumor volume (GTVn) in pre-RT and mid-RT MRI images. We utilized the HNTS-MRG2024 dataset, which consists of 150 MRI scans from patients diagnosed with HNC, including original and registered pre-RT and mid-RT T2-weighted images with corresponding segmentation masks for GTVp and GTVn. We employed two state-of-the-art models in deep learning, nnUNet and MedNeXt. For Task 1, we pretrained models on pre-RT registered and mid-RT images, followed by fine-tuning on original pre-RT images. For Task 2, we combined registered pre-RT images, registered pre-RT segmentation masks, and mid-RT data as a multi-channel input for training. Our solution for Task 1 achieved 1st place in the final test phase with an aggregated Dice Similarity Coefficient of 0.8254, and our solution for Task 2 ranked 8th with a score of 0.7005. The proposed solution is publicly available at Github Repository.

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