Interactive 3D segmentation for primary gross tumor volume in oropharyngeal cancer.

IF 3.9 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES

Scientific Reports Pub Date : 2025-08-05 DOI:10.1038/s41598-025-13601-3

Mikko Saukkoriipi, Jaakko Sahlsten, Joel Jaskari, Lotta Orsmaa, Jari Kangas, Nastaran Rasouli, Roope Raisamo, Jussi Hirvonen, Helena Mehtonen, Jorma Järnstedt, Antti Mäkitie, Mohamed Naser, Clifton Fuller, Benjamin Kann, Kimmo Kaski

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Abstract

Radiotherapy is the main treatment modality of oropharyngeal cancer (OPC), in which an accurate segmentation of primary gross tumor volume (GTVt) is essential but also challenging due to significant interobserver variability and the time consumed in manual tumor delineation. For such a challenge an interactive deep learning (DL) based approach offers the advantage of automatic high-performance segmentation with the flexibility for user correction when necessary. In this study, we investigate an interactive DL for GTVt segmentation in OPC by introducing a novel two-stage Interactive Click Refinement (2S-ICR) framework and implementing state-of-the-art algorithms. Using the 2021 HEad and neCK TumOR dataset for development and an external dataset from The University of Texas MD Anderson Cancer Center for evaluation, the 2S-ICR framework achieves a Dice similarity coefficient of 0.722 ± 0.142 without user interaction and 0.858 ± 0.050 after ten interactions, thus outperforming existing methods in both cases.

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口咽癌原发大体肿瘤体积的交互式三维分割。

放疗是口咽癌（OPC）的主要治疗方式，其中原发肿瘤总体积（GTVt）的准确分割是必不可少的，但由于观察者之间的显著差异和人工肿瘤划定所消耗的时间，这也具有挑战性。对于这样的挑战，基于交互式深度学习（DL）的方法提供了自动高性能分割的优势，并在必要时灵活地进行用户纠正。在本研究中，我们通过引入一种新的两阶段交互式点击细化（2S-ICR）框架和实现最先进的算法，研究了OPC中用于GTVt分割的交互式深度学习。使用2021年头颈部肿瘤数据集进行开发，并使用来自德克萨斯大学MD安德森癌症中心的外部数据集进行评估，s2 - icr框架在没有用户交互的情况下获得了0.722±0.142的Dice相似系数，在10次交互后获得了0.858±0.050的Dice相似系数，从而在两种情况下都优于现有方法。

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来源期刊

Scientific Reports Natural Science Disciplines-

CiteScore

7.50

自引率

4.30%

发文量

19567

审稿时长

3.9 months

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