Interactive 3D Segmentation for Primary Gross Tumor Volume in Oropharyngeal Cancer

Mikko Saukkoriipi, Jaakko Sahlsten, Joel Jaskari, Lotta Orasmaa, 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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引用次数: 0

Abstract

The main treatment modality for oropharyngeal cancer (OPC) is radiotherapy, where accurate segmentation of the primary gross tumor volume (GTVp) is essential. However, accurate GTVp segmentation is challenging due to significant interobserver variability and the time-consuming nature of manual annotation, while fully automated methods can occasionally fail. An interactive deep learning (DL) model offers the advantage of automatic high-performance segmentation with the flexibility for user correction when necessary. In this study, we examine interactive DL for GTVp segmentation in OPC. We implement state-of-the-art algorithms and propose a novel two-stage Interactive Click Refinement (2S-ICR) framework. Using the 2021 HEad and neCK TumOR (HECKTOR) 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 0.713 $\pm$ 0.152 without user interaction and 0.824 $\pm$ 0.099 after five interactions, outperforming existing methods in both cases.
口咽癌原发总肿瘤体积的交互式三维分割技术
口咽癌(OPC)的主要治疗方式是放射治疗,其中准确分割原发肿瘤总体积(GTVp)至关重要。然而,由于观察者之间存在显著差异,而且人工标注耗时,准确的 GTVp 分割极具挑战性,而全自动方法偶尔也会失败。交互式深度学习(DL)模型具有自动进行高性能分割的优势,而且在必要时用户可以灵活地进行修正。在本研究中,我们研究了用于 OPC 中 GTVp 分割的交互式深度学习。我们采用了最先进的算法,并提出了一种新颖的两阶段交互式点击再细化(2S-ICR)框架。使用2021 HEad and neCK Tumor(HECKTOR)数据集进行开发,并使用德克萨斯大学MDAnderson癌症中心的外部数据集进行评估,2S-ICR框架在没有用户交互的情况下实现了0.713美元/pm$ 0.152的相似系数,在5次交互后实现了0.824美元/pm$ 0.099的相似系数,在这两种情况下均优于现有方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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