鼻咽癌放疗计划中大体肿瘤靶区与危及器官的全自动分割
Fully Automatic Segmentation of Gross Target Volume and Organs-at-Risk for Radiotherapy Planning of Nasopharyngeal Carcinoma
AI总结：
针对鼻咽癌放疗计划中的自动勾画需求，提出基于强度协调、自动裁剪和独立3D U-Net的全自动框架，实现45个OAR与两个GTV分割，并在SegRap 2023验证阶段两项任务均获第二名。
摘要：
由于相邻软组织之间对比度较低，头颈部（Head&Neck, H&N）区域CT图像中的靶区分割具有挑战性。SegRap 2023挑战赛专注于对鼻咽癌（Nasopharyngeal Carcinoma, NPC）分割算法进行基准测试，这些算法将用作放射治疗计划的自动勾画工具。我们提出了一个全自动框架，并开发了两个模型，分别用于：a）分割45个危及器官（Organs at Risk, OARs），以及b）分割两个大体肿瘤靶区（Gross Tumor Volumes, GTVs）。为此，我们通过协调强度分布对图像体数据进行预处理，然后围绕靶区区域自动裁剪体数据。预处理后的体数据被分别用于为每项任务训练一个标准的3D U-Net模型。在挑战赛的验证阶段，我们的方法在两项任务中均获得第二名。所提出的框架可在 https://github.com/Astarakee/segrap2023 获取。
英文摘要：
Target segmentation in CT images of Head&Neck (H&N) region is challenging due to low contrast between adjacent soft tissue. The SegRap 2023 challenge has been focused on benchmarking the segmentation algorithms of Nasopharyngeal Carcinoma (NPC) which would be employed as auto-contouring tools for radiation treatment planning purposes. We propose a fully-automatic framework and develop two models for a) segmentation of 45 Organs at Risk (OARs) and b) two Gross Tumor Volumes (GTVs). To this end, we preprocess the image volumes by harmonizing the intensity distributions and then automatically cropping the volumes around the target regions. The preprocessed volumes were employed to train a standard 3D U-Net model for each task, separately. Our method took second place for each of the tasks in the validation phase of the challenge. The proposed framework is available at https://github.com/Astarakee/segrap2023
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
