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

• URL: http://arxiv.org/abs/2409.06605v1
• Date: Tue, 10 Sep 2024 15:58:21 GMT
• Title: Interactive 3D Segmentation for Primary Gross Tumor Volume in Oropharyngeal Cancer
• Authors: 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,
• Abstract summary: We implement state-of-the-art algorithms and propose a novel two-stage Interactive Click Refinement framework. 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.
• Score: 1.9997842016096374
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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