Head and neck tumor segmentation in PET/CT: The HECKTOR challenge

Oreiller, Valentin; Andrearczyk, Vincent; Jreige, Mario; Boughdad, Sarah; Elhalawani, Hesham; Castelli, J.; Vallières, Martin; Zhu, Simeng; Xie, Juanying; Peng, Ying; Iantsen, Andrei; Hatt, Mathieu; Yuan, Yading; Ma, Jun; Yang, Xiaoping; Rao, C. Hemasundara; Pai, Suraj; Ghimire, Kanchan; Feng, Xue; Naser, Mohamed A.; Fuller, Clifton D.; Yousefirizi, Fereshteh; Rahmim, Arman; Chen, Huai; Wang, Lisheng; Prior, John O.; Depeursinge, Adrien

doi:10.1016/j.media.2021.102336

Cited by 147 publications

(86 citation statements)

References 43 publications

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“…In [50], we reported an inter-observer (four observers) agreement of 0.6110 on a subset of the HECKTOR 2020 data containing 21 randomly drawn cases. Similar agreements were reported in the literature [22] with an average DSC agreement of three observers of 0.57 using only the CT images for annotation and 0.69 using both PET and CT. A source of error therefore originates from the degree of subjectivity in the annotation and correction of the expert.…”

Section: Sources Of Errorsmentioning

confidence: 99%

“…In [5], we developed a baseline Convolutional Neural Network (CNN) approach based on a leave-one-center-out cross-validation on the training data of the HECKTOR challenge. Promising results were obtained with limitations that motivated additional data curation, data cleaning and the creation of the first HECKTOR challenge in 2020 [4,50]. This first edition compared segmentation architectures as well as the complementarity of the two modalities for the segmentation of GTVt in H&N.…”

Section: Introduction: Research Contextmentioning

confidence: 99%

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Overview of the HECKTOR Challenge at MICCAI 2020: Automatic Head and Neck Tumor Segmentation in PET/CT

Andrearczyk

Oreiller

Jreige

et al. 2021

Lecture Notes in Computer Science

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This paper presents an overview of the second edition of the HEad and neCK TumOR (HECKTOR) challenge, organized as a satellite event of the 24th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2021. The challenge is composed of three tasks related to the automatic analysis of PET/CT images for patients with Head and Neck cancer (H&N), focusing on the oropharynx region. Task 1 is the automatic segmentation of H&N primary Gross Tumor Volume (GTVt) in FDG-PET/CT images. Task 2 is the automatic prediction of Progression Free Survival (PFS) from the same FDG-PET/CT. Finally, Task 3 is the same as Task 2 with ground truth GTVt annotations provided to the participants. The data were collected from six centers for a total of 325 images, split into 224 training and 101 testing cases. The interest in the challenge was highlighted by the important participation with 103 registered teams and 448 result submissions. The best methods obtained a Dice Similarity Coefficient (DSC) of 0.7591 in the first task, and a Concordance index (C-index) of 0.7196 and 0.6978 in Tasks 2 and 3, respectively. In all tasks, simplicity of the approach was found to be key to ensure generalization performance. The comparison of the PFS prediction performance in Tasks 2 and 3 suggests that providing the GTVt contour was not crucial to achieve best results, which indicates that fully automatic methods can be used. This potentially obviates the need for GTVt contouring, opening avenues for reproducible and large scale radiomics studies including thousands potential subjects. equal contribution equal contribution

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Section: Sources Of Errorsmentioning

confidence: 99%

Section: Introduction: Research Contextmentioning

confidence: 99%

Overview of the HECKTOR Challenge at MICCAI 2020: Automatic Head and Neck Tumor Segmentation in PET/CT

Andrearczyk

Oreiller

Jreige

et al. 2021

Lecture Notes in Computer Science

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“…Data for Segmentation. PET and CT images used in this challenge were provided by the organisers of the HECKTOR challenge at the 24th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) [1]. The total number of training cases is 224 from 5 centers: CHGJ, CHMR, CHUS, CHUP, and CHUM.…”

Section: Datamentioning

confidence: 99%

“…Head and Neck (H&N) tumour is the fifth most prevalent cancer worldwide. Improving the accuracy and efficiency of disease diagnosis and treatment is the rationale behind the developments of computer-aided systems in medical imaging [1,2]. However, obtaining manual segmentations, which can be used for diagnosing and treatment purposes, is time consuming and suffers from intra-and inter-observer biases.…”

Section: Introductionmentioning

confidence: 99%

Multimodal PET/CT Tumour Segmentation and Prediction of Progression-Free Survival using a Full-Scale UNet with Attention

Bourigault¹,

McGowan²,

Mehranian³

et al. 2021

Preprint

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Segmentation of head and neck (H&N) tumours and prediction of patient outcome are crucial for patient's disease diagnosis and treatment monitoring. Current developments of robust deep learning models are hindered by the lack of large multi-centre, multi-modal data with quality annotations. The MICCAI 2021 HEad and neCK TumOR (HECKTOR) segmentation and outcome prediction challenge creates a platform for comparing segmentation methods of the primary gross target volume on fluorodeoxyglucose (FDG)-PET and Computed Tomography images and prediction of progression-free survival in H&N oropharyngeal cancer. For the segmentation task, we proposed a new network based on an encoder-decoder architecture with full inter-and intra-skip connections to take advantage of low-level and high-level semantics at full scales. Additionally, we used Conditional Random Fields as a post-processing step to refine the predicted segmentation maps. We trained multiple neural networks for tumor volume segmentation, and these segmentations were ensembled achieving an average Dice Similarity Coefficient of 0.75 in cross-validation, and 0.76 on the challenge testing data set. For prediction of patient progression free survival task, we propose a Cox proportional hazard regression combining clinical, radiomic, and deep learning features. Our survival prediction model achieved a concordance index of 0.82 in cross-validation, and 0.62 on the challenge testing data set.

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“…In the following sections, we first introduce the data provided by the MICCAI 2020 HEad and neCK TumOR segmentation and outcome prediction (HECKTOR) challenge [6,7]. Our proposed methods and training scheme are additionally explained.…”

Section: Introductionmentioning

confidence: 99%

Segmentation and Risk Score Prediction of Head and Neck Cancers in PET/CT Volumes with 3D U-Net and Cox Proportional Hazard Neural Networks

Yousefirizi¹,

Janzen²,

Dubljevic

et al. 2022

Lecture Notes in Computer Science

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We utilized a 3D nnU-Net model with residual layers supplemented by squeeze and excitation (SE) normalization for tumor segmentation from PET/CT images provided by the Head and Neck Tumor segmentation challenge (HECKTOR). Our proposed loss function incorporates the Unified Focal and Mumford-Shah losses to take the advantage of distribution, region, and boundary-based loss functions. The results of leave-one-out-center-cross-validation performed on different centers showed a segmentation performance of 0.82 average Dice score (DSC) and 3.16 median Hausdorff Distance (HD), and our results on the test set achieved 0.77 DSC and 3.01 HD. Following lesion segmentation, we proposed training a case-control proportional hazard Cox model with an MLP neural net backbone to predict the hazard risk score for each discrete lesion. This hazard risk prediction model (CoxCC) was to be trained on a number of PET/CT radiomic features extracted from the segmented lesions, patient and lesion demographics, and encoder features provided from the penultimate layer of a multi-input 2D PET/CT convolutional neural network tasked with predicting time-to-event for each lesion. A 10-fold cross-validated CoxCC model resulted in a c-index validation score of 0.89, and a c-index score of 0.61 on the HECKTOR challenge test dataset.

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Head and neck tumor segmentation in PET/CT: The HECKTOR challenge

Cited by 147 publications

References 43 publications

Overview of the HECKTOR Challenge at MICCAI 2020: Automatic Head and Neck Tumor Segmentation in PET/CT

Overview of the HECKTOR Challenge at MICCAI 2020: Automatic Head and Neck Tumor Segmentation in PET/CT

Multimodal PET/CT Tumour Segmentation and Prediction of Progression-Free Survival using a Full-Scale UNet with Attention

Segmentation and Risk Score Prediction of Head and Neck Cancers in PET/CT Volumes with 3D U-Net and Cox Proportional Hazard Neural Networks

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