Overview of the HECKTOR Challenge at MICCAI 2021: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT Images

Andrearczyk, Vincent; Oreiller, Valentin; Boughdad, Sarah; Rest, Catherine Cheze Le; Elhalawani, Hesham; Jreige, Mario; Prior, John O.; Vallières, Martin; Visvikis, Dimitris; Hatt, Mathieu; Depeursinge, Adrien

doi:10.1007/978-3-030-98253-9_1

Cited by 75 publications

(78 citation statements)

References 61 publications

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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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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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“…HECKTOR (HEad and neCK TumOR) 2021 challenge is the successor of the HECKTOR 2020, both aimed to improve the automatic segmentation methods for head and neck cancer based on PET and CT images [7,8]. In addition, this year the challenge has been extended to include outcome prediction in patients using both image and clinical data.…”

Section: Introductionmentioning

confidence: 99%

Skip-SCSE Multi-scale Attention and Co-learning Method for Oropharyngeal Tumor Segmentation on Multi-modal PET-CT Images

Biase

Tang

Sourlos

et al. 2022

Lecture Notes in Computer Science

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One of the primary treatment options for head and neck cancer is (chemo)radiation. Accurate delineation of the contour of the tumors is of great importance in the successful treatment of the tumor and in the prediction of patient outcomes. With this paper we take part in the HECKTOR 2021 challenge and we propose our methods for automatic tumor segmentation on PET and CT images of oropharyngeal cancer patients. To achieve this goal, we investigated different deep learning methods with the purpose of highlighting relevant image and modality related features, to refine the contour of the primary tumor. More specifically, we tested a Co-learning method [1] and a 3D Skip Spatial and Channel Squeeze and Excitation Multi-Scale Attention method (Skip-scSE-M), on the challenge dataset. The best results achieved on the test set were 0.762 mean Dice Similarity Score and 3.143 median of the Hausdorf Distance at 95%.

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“…In this paper, we are proposing a multimodal machine learning algorithm that, without prior information on the exact location of the tumor, utilizes both tabular and imaging data for the prognosis of Progression Free Survival (PFS) for patients who have H&N oropharyngeal cancer. This work is carried out to address the prognosis task of the MICCAI 2021 Head and Neck Tumor segmentation and outcome prediction challenge (HECKTOR) [2][1]. Visualization of the EHR data was performed to observe the distribution of patients in terms of gender and age, as shown in Fig.…”

Section: Introductionmentioning

confidence: 99%

An Ensemble Approach for Patient Prognosis of Head and Neck Tumor Using Multimodal Data

Saeed¹,

Majzoub²,

Sobirov³

et al. 2022

Preprint

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Accurate prognosis of a tumor can help doctors provide a proper course of treatment and, therefore, save the lives of many. Traditional machine learning algorithms have been eminently useful in crafting prognostic models in the last few decades. Recently, deep learning algorithms have shown significant improvement when developing diagnosis and prognosis solutions to different healthcare problems. However, most of these solutions rely solely on either imaging or clinical data. Utilizing patient tabular data such as demographics and patient medical history alongside imaging data in a multimodal approach to solve a prognosis task has started to gain more interest recently and has the potential to create more accurate solutions. The main issue when using clinical and imaging data to train a deep learning model is to decide on how to combine the information from these sources. We propose a multimodal network that ensembles deep multi-task logistic regression (MTLR), Cox proportional hazard (CoxPH) and CNN models to predict prognostic outcomes for patients with head and neck tumors using patients' clinical and imaging (CT and PET) data. Features from CT and PET scans are fused and then combined with patients' electronic health records for the prediction. The proposed model is trained and tested on 224 and 101 patient records respectively. Experimental results show that our proposed ensemble solution achieves a C-index of 0.72 on The HECKTOR test set that saved us the first place in prognosis task of the HECKTOR challenge. The full implementation based on PyTorch is available on https://github.com/numanai/BioMedIA-Hecktor2021.

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Overview of the HECKTOR Challenge at MICCAI 2021: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT Images

Cited by 75 publications

References 61 publications

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

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

Skip-SCSE Multi-scale Attention and Co-learning Method for Oropharyngeal Tumor Segmentation on Multi-modal PET-CT Images

An Ensemble Approach for Patient Prognosis of Head and Neck Tumor Using Multimodal Data

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