HECKTOR@MICCAI最新文献

MLC at HECKTOR 2022: The Effect and Importance of Training Data when Analyzing Cases of Head and Neck Tumors using Machine Learning MLC在HECKTOR 2022:训练数据在使用机器学习分析头颈部肿瘤病例时的作用和重要性

HECKTOR@MICCAI Pub Date : 2022-11-30 DOI: 10.48550/arXiv.2211.16834

Vajira Lasantha Thambawita, A. Storaas, S. Hicks, P. Halvorsen, M. Riegler

{"title":"MLC at HECKTOR 2022: The Effect and Importance of Training Data when Analyzing Cases of Head and Neck Tumors using Machine Learning","authors":"Vajira Lasantha Thambawita, A. Storaas, S. Hicks, P. Halvorsen, M. Riegler","doi":"10.48550/arXiv.2211.16834","DOIUrl":"https://doi.org/10.48550/arXiv.2211.16834","url":null,"abstract":"Head and neck cancers are the fifth most common cancer worldwide, and recently, analysis of Positron Emission Tomography (PET) and Computed Tomography (CT) images has been proposed to identify patients with a prognosis. Even though the results look promising, more research is needed to further validate and improve the results. This paper presents the work done by team MLC for the 2022 version of the HECKTOR grand challenge held at MICCAI 2022. For Task 1, the automatic segmentation task, our approach was, in contrast to earlier solutions using 3D segmentation, to keep it as simple as possible using a 2D model, analyzing every slice as a standalone image. In addition, we were interested in understanding how different modalities influence the results. We proposed two approaches; one using only the CT scans to make predictions and another using a combination of the CT and PET scans. For Task 2, the prediction of recurrence-free survival, we first proposed two approaches, one where we only use patient data and one where we combined the patient data with segmentations from the image model. For the prediction of the first two approaches, we used Random Forest. In our third approach, we combined patient data and image data using XGBoost. Low kidney function might worsen cancer prognosis. In this approach, we therefore estimated the kidney function of the patients and included it as a feature. Overall, we conclude that our simple methods were not able to compete with the highest-ranking submissions, but we still obtained reasonably good scores. We also got interesting insights into how the combination of different modalities can influence the segmentation and predictions.","PeriodicalId":305210,"journal":{"name":"HECKTOR@MICCAI","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127956818","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Joint nnU-Net and Radiomics Approaches for Segmentation and Prognosis of Head and Neck Cancers with PET/CT images 联合nnU-Net和放射组学方法用于PET/CT图像的头颈部肿瘤分割和预后

HECKTOR@MICCAI Pub Date : 2022-11-18 DOI: 10.48550/arXiv.2211.10138

Hui Xu, Yi-hong Li, Wei Zhao, G. Quellec, Lijun Lu, M. Hatt

{"title":"Joint nnU-Net and Radiomics Approaches for Segmentation and Prognosis of Head and Neck Cancers with PET/CT images","authors":"Hui Xu, Yi-hong Li, Wei Zhao, G. Quellec, Lijun Lu, M. Hatt","doi":"10.48550/arXiv.2211.10138","DOIUrl":"https://doi.org/10.48550/arXiv.2211.10138","url":null,"abstract":"Automatic segmentation of head and neck cancer (HNC) tumors and lymph nodes plays a crucial role in the optimization treatment strategy and prognosis analysis. This study aims to employ nnU-Net for automatic segmentation and radiomics for recurrence-free survival (RFS) prediction using pretreatment PET/CT images in multi-center HNC cohort. A multi-center HNC dataset with 883 patients (524 patients for training, 359 for testing) was provided in HECKTOR 2022. A bounding box of the extended oropharyngeal region was retrieved for each patient with fixed size of 224 x 224 x 224 $mm^{3}$. Then 3D nnU-Net architecture was adopted to automatic segmentation of primary tumor and lymph nodes synchronously.Based on predicted segmentation, ten conventional features and 346 standardized radiomics features were extracted for each patient. Three prognostic models were constructed containing conventional and radiomics features alone, and their combinations by multivariate CoxPH modelling. The statistical harmonization method, ComBat, was explored towards reducing multicenter variations. Dice score and C-index were used as evaluation metrics for segmentation and prognosis task, respectively. For segmentation task, we achieved mean dice score around 0.701 for primary tumor and lymph nodes by 3D nnU-Net. For prognostic task, conventional and radiomics models obtained the C-index of 0.658 and 0.645 in the test set, respectively, while the combined model did not improve the prognostic performance with the C-index of 0.648.","PeriodicalId":305210,"journal":{"name":"HECKTOR@MICCAI","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132987881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Radiomics-enhanced Deep Multi-task Learning for Outcome Prediction in Head and Neck Cancer 放射组学增强的深度多任务学习用于头颈癌预后预测

HECKTOR@MICCAI Pub Date : 2022-11-10 DOI: 10.1007/978-3-031-27420-6_14

Mingyuan Meng, Lei Bi, D. Feng, Jinman Kim

引用次数: 10

Multi-Scale Fusion Methodologies for Head and Neck Tumor Segmentation 头颈部肿瘤分割的多尺度融合方法

HECKTOR@MICCAI Pub Date : 2022-10-29 DOI: 10.48550/arXiv.2210.16704

Abhishek Srivastava, Debesh Jha, B. Aydogan, Mohamed E.Abazeed, Ulas Bagci

引用次数: 1

Recurrence-free Survival Prediction under the Guidance of Automatic Gross Tumor Volume Segmentation for Head and Neck Cancers 自动肿瘤体积分割指导下的头颈部肿瘤无复发生存预测

HECKTOR@MICCAI Pub Date : 2022-09-22 DOI: 10.48550/arXiv.2209.11268

Kai Wang, Yunxiang Li, M. Dohopolski, Tao Peng, W. Lu, You Zhang, Jing Wang

{"title":"Recurrence-free Survival Prediction under the Guidance of Automatic Gross Tumor Volume Segmentation for Head and Neck Cancers","authors":"Kai Wang, Yunxiang Li, M. Dohopolski, Tao Peng, W. Lu, You Zhang, Jing Wang","doi":"10.48550/arXiv.2209.11268","DOIUrl":"https://doi.org/10.48550/arXiv.2209.11268","url":null,"abstract":"For Head and Neck Cancers (HNC) patient management, automatic gross tumor volume (GTV) segmentation and accurate pre-treatment cancer recurrence prediction are of great importance to assist physicians in designing personalized management plans, which have the potential to improve the treatment outcome and quality of life for HNC patients. In this paper, we developed an automated primary tumor (GTVp) and lymph nodes (GTVn) segmentation method based on combined pre-treatment positron emission tomography/computed tomography (PET/CT) scans of HNC patients. We extracted radiomics features from the segmented tumor volume and constructed a multi-modality tumor recurrence-free survival (RFS) prediction model, which fused the prediction results from separate CT radiomics, PET radiomics, and clinical models. We performed 5-fold cross-validation to train and evaluate our methods on the MICCAI 2022 HEad and neCK TumOR segmentation and outcome prediction challenge (HECKTOR) dataset. The ensemble prediction results on the testing cohort achieved Dice scores of 0.77 and 0.73 for GTVp and GTVn segmentation, respectively, and a C-index value of 0.67 for RFS prediction. The code is publicly available (https://github.com/wangkaiwan/HECKTOR-2022-AIRT). Our team's name is AIRT.","PeriodicalId":305210,"journal":{"name":"HECKTOR@MICCAI","volume":"127 16","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113939775","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Automated head and neck tumor segmentation from 3D PET/CT 从3D PET/CT自动分割头颈部肿瘤

HECKTOR@MICCAI Pub Date : 2022-09-22 DOI: 10.48550/arXiv.2209.10809

A. Myronenko, M. R. Siddiquee, Dong Yang, Yufan He, Daguang Xu

引用次数: 2

Segmentation and Risk Score Prediction of Head and Neck Cancers in PET/CT Volumes with 3D U-Net and Cox Proportional Hazard Neural Networks 基于3D U-Net和Cox比例风险神经网络的PET/CT体积头颈部肿瘤分割和风险评分预测

HECKTOR@MICCAI Pub Date : 2022-02-16 DOI: 10.1007/978-3-030-98253-9_22

F. Yousefirizi, I. Janzen, Natalia Dubljevic, Yueh-En Liu, Chloe Hill, Calum MacAulay, A. Rahmim

引用次数: 3

Overview of the HECKTOR Challenge at MICCAI 2021: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT Images MICCAI 2021的HECKTOR挑战概述:PET/CT图像中的自动头颈部肿瘤分割和结果预测

HECKTOR@MICCAI Pub Date : 2022-01-11 DOI: 10.1007/978-3-030-98253-9_1

V. Andrearczyk, Valentin Oreiller, S. Boughdad, C. Rest, H. Elhalawani, Mario Jreige, John O. Prior, M. Vallières, D. Visvikis, M. Hatt, A. Depeursinge

引用次数: 49

Multimodal PET/CT Tumour Segmentation and Prediction of Progression-Free Survival using a Full-Scale UNet with Attention 多模态PET/CT肿瘤分割和全尺寸UNet无进展生存期预测

HECKTOR@MICCAI Pub Date : 2021-11-06 DOI: 10.1007/978-3-030-98253-9_18

Emmanuelle Bourigault, D. McGowan, A. Mehranian, Bartlomiej W. Papie.z

引用次数: 12

Squeeze-and-Excitation Normalization for Automated Delineation of Head and Neck Primary Tumors in Combined PET and CT Images PET和CT联合图像中头颈部原发肿瘤自动圈定的挤压-激发归一化

HECKTOR@MICCAI Pub Date : 2021-02-20 DOI: 10.1007/978-3-030-67194-5_4

Andrei Iantsen, D. Visvikis, M. Hatt

引用次数: 49