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
{"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","doi":"arxiv-2409.06605","DOIUrl":null,"url":null,"abstract":"The main treatment modality for oropharyngeal cancer (OPC) is radiotherapy,\nwhere accurate segmentation of the primary gross tumor volume (GTVp) is\nessential. However, accurate GTVp segmentation is challenging due to\nsignificant interobserver variability and the time-consuming nature of manual\nannotation, while fully automated methods can occasionally fail. An interactive\ndeep learning (DL) model offers the advantage of automatic high-performance\nsegmentation with the flexibility for user correction when necessary. In this\nstudy, we examine interactive DL for GTVp segmentation in OPC. We implement\nstate-of-the-art algorithms and propose a novel two-stage Interactive Click\nRefinement (2S-ICR) framework. Using the 2021 HEad and neCK TumOR (HECKTOR)\ndataset for development and an external dataset from The University of Texas MD\nAnderson Cancer Center for evaluation, the 2S-ICR framework achieves a Dice\nsimilarity coefficient of 0.713 $\\pm$ 0.152 without user interaction and 0.824\n$\\pm$ 0.099 after five interactions, outperforming existing methods in both\ncases.","PeriodicalId":501289,"journal":{"name":"arXiv - EE - Image and Video Processing","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - EE - Image and Video Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.06605","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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.